Smoke sensor labyrinth optimization design and dust pollution resistance method

Abstract

The invention belongs to the technical field of fire detection, and particularly discloses a smoke sensor labyrinth optimization design and dust pollution resistance method, which comprises the following steps: detecting corresponding smoke and dust fusion particles and water vapor particles in an air detection area through a signal transmitting device; water vapor particles are screened to weaken smoke alarm triggered by mistake caused by the water vapor particles, a differential signal processing technology is adopted to distinguish and identify fusion particles, meanwhile, the fusion particles are introduced into a labyrinth measuring cavity, the concentration of smoke and dust is measured in the labyrinth measuring cavity, and the detection stability is enhanced; the width of a labyrinth channel in a labyrinth measuring cavity is automatically adjusted according to the smoke concentration to carry out space division of smoke particles and dust particles, so that the adaptability of the detector to a complex environment is improved; the smoke concentration in the labyrinth measuring cavity is subjected to fire early warning judgment, the cleaning device additionally arranged in the labyrinth measuring cavity is started according to the smoke concentration and the dust concentration for cleaning treatment, and the detection continuity is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of fire detection and relates to a smoke sensor maze optimization design and a dust pollution resistance method. Background Art

[0002] As the complexity of modern buildings and industrial facilities increases, fire safety has become a critical issue. As a core component of the fire warning system, the performance and reliability of smoke sensors are directly related to the safety of life and property. However, traditional smoke sensors face many challenges in practical applications, especially in complex environments, where the presence of interfering substances such as dust and water vapor can significantly affect the detection accuracy and stability of the sensor.

[0003] Existing smoke sensors usually use a maze structure to enhance the sensitivity of optical detection, but their design often fails to effectively distinguish smoke particles from dust particles, causing the measurement results to be interfered with by dust pollution. Therefore, how to optimize the maze structure design and effectively resist dust pollution without significantly increasing the complexity of the system has become a key issue that needs to be solved in the field of smoke sensors.

[0004] In the prior art, there are also some related solutions related to fire detection. For example, the Chinese patent publication number CN118522112A discloses a method and system for improving the anti-dust accumulation performance of smoke fire detectors. The method adjusts the detector initialization state to a high-sensitivity state, regularly detects the smoke concentration and updates the background value, and then adaptively adjusts the working state of the detector according to the detection result and the background value of the detector, and finally alarms the fire according to the smoke concentration; reduces the influence of dust accumulation in the optical darkroom of the detector on the working state of the photoelectric smoke fire detector, thereby extending the cycle of cleaning the detector, reducing manpower and material consumption, and solving the technical problem that dust accumulation in the optical darkroom of the detector leads to a decrease in the reliability of the detector alarm and the inability to timely warn of the fire risk.

[0005] Another Chinese patent with the publication number CN107016816B discloses a smoke detector maze structure and a smoke detection method thereof, which includes two transmitting devices and a receiving device, one of which is for large-angle transmission and reception, and the other is for small-angle transmission and reception; large-angle transmission and reception means that the angle between the transmitting device and the receiving device is an obtuse angle, and small-angle transmission and reception means that the angle between the transmitting device and the receiving device is an acute angle. The attenuation effect of smoke blocking light and the reflection effect of smoke on light work together, which is more conducive to the effective detection of different smokes, reduces false alarms and missed alarms, and adds right-angled triangle stripes above and below the maze to effectively reduce the influence of dust accumulation on smoke collection signals.

[0006] Although the above solutions propose some methods for fire detection, there are still the following limitations: 1. During fire alarm detection, there is a lack of a dynamic detection and compensation mechanism for environmental humidity, and the smoke alarm conditions cannot be dynamically adjusted according to humidity changes. In a high-humidity environment, water vapor in the air will increase the light scattering effect, which may cause the optical smoke detector to misidentify water vapor particles as smoke particles, resulting in false alarms, and thus reducing the detection accuracy of the detector in a high-humidity environment.

[0007] 2. During smoke detection, there is no effective distinction between smoke particles and dust particles. Especially in a complex environment with a lot of dust, smoke particles and dust particles are prone to interfere with each other, resulting in inaccurate detection results, and thus the smoke detector has poor adaptability to complex environments. Summary of the Invention

[0008] In view of this, to solve the problems raised in the above background technology, a maze optimization design and anti-dust pollution method for a smoke sensor are proposed.

[0009] The object of the present invention can be achieved by the following technical solutions: The present invention provides a maze optimization design and anti-dust pollution method for a smoke sensor, including the following steps: Transmitting and receiving first and second band signals to an air detection area through a signal transmitting device to detect the fusion particles of smoke and dust and water vapor particles.

[0010] Adjust the detection accuracy of the smoke sensor based on the second band signal.

[0011] Use differential signal processing technology to process the first band signal, distinguish and identify the fusion particles of smoke and dust, and introduce the fusion particles into the maze measurement chamber.

[0012] Measure the smoke concentration and dust concentration in the maze measurement chamber through an optical detection device.

[0013] Automatically adjust the width of the maze channels in the maze measurement chamber according to the smoke concentration for spatial segmentation of smoke particles and dust particles.

[0014] Make a fire warning judgment on the smoke concentration in the maze measurement chamber, and start the cleaning device added in the maze measurement chamber for cleaning according to the smoke concentration and dust concentration.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention adjusts the smoke alarm accuracy limit data by detecting the environmental humidity, and weakens the smoke alarm triggered by water vapor particles by mistake.

[0016] (2) The present invention screens out water vapor particles, automatically adjusts the width of the maze passage according to the smoke concentration, and separately introduces smoke and dust into their respective passages in the maze measurement chamber, achieving spatial separation of smoke particles and dust particles, enabling the detector to work stably in a complex environment with high humidity and a lot of dust, and increasing the adaptability of the detector. At the same time, the optical detection device is used to measure the smoke concentration and dust concentration, enhancing the stability and accuracy of the detection.

[0017] (3) The present invention starts the cleaning device added in the maze measurement chamber for cleaning according to the smoke concentration and dust concentration, automatically cleans the maze measurement chamber, and ensures the continuity and accuracy of the detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0019] Figure 1 It is a schematic flow chart of the implementation steps of the method of the present invention.

[0020] Figure 2 It is a schematic structural diagram of the maze measurement chamber of the present invention.

[0021] Figure 3 It is a schematic flow chart of the fire warning judgment of the present invention.

[0022] Reference numerals: 1. Smoke passage, 2. Dust passage, 3. Moving baffle, 4. Entrance. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0024] Please refer to Figure 1 As shown, the present invention provides an optimized design of a smoke sensor maze and an anti-dust pollution method, including the following steps: step1. Transmit and receive the first and second band signals to the air detection area through the signal transmission device to detect the fusion particles of smoke and dust and water vapor particles.

[0025] The signal transmitting device specifically is: LEDs 1 and 2 with different wavelengths are used to respectively emit optical signals in the first band and the second band, and a dual-channel signal processing circuit is configured to respectively receive the reflected signals from LEDs 1 and 2.

[0026] In a preferred embodiment, the emitting and receiving of the first and second band signals to the air detection area specifically is: detecting the fused particles of smoke and dust through the optical signal in the first band.

[0027] Detecting the water vapor particles through the optical signal in the second band.

[0028] The first band usually selects shorter wavelengths (such as blue light or ultraviolet light) because the light of these wavelengths has a stronger scattering effect on smoke and dust particles. The sizes of smoke and dust particles are usually small (micrometer level) and are more sensitive to the scattering of short-wavelength light.

[0029] The second band usually selects longer wavelengths (such as red light or infrared light) because the absorption and scattering characteristics of the light of these wavelengths on water vapor particles are more obvious. The water vapor particles have a strong absorption of long-wavelength light, and their size distribution and optical characteristics are different from those of smoke and dust.

[0030] step2: Adjust the detection accuracy of the smoke sensor based on the second band signal.

[0031] In a preferred embodiment, the adjusting the detection accuracy of the smoke sensor based on the second band signal includes: comparing the second band signal with a preset band signal threshold, and when the second band signal is higher than the preset band signal threshold, uploading the meteorological environment humidity through the cloud network.

[0032] Matching the meteorological environment humidity in a preset humidity-alarm parameter mapping table to obtain the alarm accuracy limit data of the corresponding smoke sensor in the air detection area. The alarm accuracy limit data includes the alarm timing count threshold and the alarm delay duration. The alarm accuracy limit data is used to map the accuracy state of the smoke detector. Reasonably setting the alarm accuracy limit data according to the specific application scenario helps to balance the sensitivity and anti-interference ability of the detector, so as to achieve the best detector performance.

[0033] The humidity-alarm parameter mapping table includes the meteorological environment humidity and the alarm accuracy limit data. The humidity-alarm parameter mapping table is used to represent the mapping relationship between different meteorological environment humidities and alarm accuracy limit data. The data example of the humidity-alarm parameter mapping table is as follows:

[0034] Table 1: Humidity-alarm parameter mapping table

[0035]

[0036] Specifically, the second-band signal is used to detect water vapor particles, and its intensity is related to the environmental humidity. By comparing with a preset intensity signal, it can be determined whether the current environmental humidity is abnormal. Uploading the meteorological environmental humidity to the cloud can achieve centralized management and analysis of data, facilitating unified regulation of the smoke sensor. Adjusting the alarm accuracy limit data according to the meteorological environmental humidity can improve the adaptability and reliability of the smoke sensor in different humidity environments.

[0037] Since the generation of water vapor particles due to air humidity will interfere with the detection accuracy of the smoke sensor, when the optical signal generated by water vapor is recognized, it is necessary to increase the alarm delay time and raise the alarm threshold value to achieve the ability to resist water mist and false alarms caused by water vapor.

[0038] The present invention adjusts the smoke alarm accuracy limit data by detecting the environmental humidity, weakening the smoke alarm caused by false triggering of water vapor particles.

[0039] step3. Process the first-band signal using differential signal processing technology to distinguish and identify the fusion particles of smoke and dust, and introduce the fusion particles into the maze measurement chamber.

[0040] Please refer to Figure 2 As shown, in a preferred embodiment, the maze measurement chamber includes a water vapor adsorption device, an air flow control device, a smoke channel 1, and a dust channel 2.

[0041] The water vapor adsorption device is used to screen out water vapor particles.

[0042] Specifically, the water vapor adsorption device can adopt a water vapor diversion plate made of physical adsorption materials (such as silica gel, molecular sieve, etc.) or chemical adsorption materials (such as calcium chloride, etc.), which is set at the entrance 4 or inside the channel of the maze measurement chamber. These materials can efficiently adsorb water vapor and have no interference with the detection of smoke and dust, ensuring that water vapor is effectively adsorbed before entering the detection area.

[0043] Water vapor particles may interfere with the detection accuracy of smoke and dust, especially in high-humidity environments. Through the water vapor adsorption device, the interference of water vapor on the detection results can be effectively reduced, improving the accuracy of the sensor.

[0044] The air flow control device is used to introduce or extract smoke particles from one side entrance 4 into or out of the smoke channel 1, and introduce or extract dust particles from the other side entrance 4 into or out of the dust channel 2.

[0045] The smoke channel 1 is used to gather smoke particles.

[0046] The dust channel 2 is used to gather dust particles.

[0047] In a further preferred embodiment, the differential signal processing technology is used to process the first-band signal to distinguish and identify the fused particles of smoke and dust, and introduce the fused particles into the maze measurement chamber. Specifically: B1. The differential signal processing technology is used to perform differential processing on the first-band signal to determine the signal reflection intensities of the smoke particles and the dust particles, reflecting the concentrations of the smoke particles and the dust particles in the air detection area.

[0048] The differential signal processing technology is a method for extracting useful information based on the differences between signals. In its simplest form, it involves subtracting two related signals, which are usually the measurement results of the same physical quantity under different conditions, or the measurements from different but related physical phenomena. For smoke particles and dust particles, there are significant differences in aspects such as signal frequency, amplitude, and phase, so they can be distinguished by the differential signal processing technology.

[0049] B2. Determine the low-speed airflow and high-speed airflow in the maze measurement chamber respectively according to the signal reflection intensities of the smoke particles and the dust particles.

[0050] Among them, smoke particles are usually smaller and lighter, with a lower signal reflection intensity (such as 1 - 100 relative units). To effectively capture and measure these particles, a slower airflow speed (for example, 0.1 - 1 m / s) is usually required to prevent the particles from bypassing the sensor due to the too-fast airflow; dust particles are larger and heavier than smoke particles, with a higher signal reflection intensity (such as 100 - 1000 relative units). Due to their larger weight, a faster airflow speed (such as 1 - 10 m / s) may be required to ensure that they can be transported to the detection area without settling.

[0051] In actual measurement, calibration and optimization need to be carried out according to factors such as the light source wavelength, particle concentration, detector sensitivity, and environmental conditions to ensure the accuracy of the measurement results.

[0052] B3. Generate an airflow control instruction for the airflow control device according to the low-speed airflow and the high-speed airflow, and introduce the particles through the airflow control device.

[0053] Specifically, a flow deflector can be set at the entrance 4 of the detector to divide the airflow into two parts: low-speed and high-speed. According to the physical property differences between the smoke particles and the dust particles, the smoke particles can be carried into the smoke channel 1 by the low-speed airflow, and the dust particles can be carried into the dust channel 2 by the high-speed airflow. Smoke particles usually have a smaller particle size (0.0 - 1 micron), are light in weight, are easy to move with the airflow, and have a small inertia, so they can follow the low-speed airflow into the smoke channel 1; dust particles usually have a larger particle size (1 - 100 microns), are heavier in weight, have a larger inertia, and are not easy to change direction with the airflow, so they can follow the high-speed airflow area and finally enter the dust channel 2.

[0054] Step 4. Measure the smoke concentration and dust concentration in the maze measurement chamber through an optical detection device.

[0055] In a preferred embodiment, in the maze measurement chamber, measuring the smoke concentration and dust concentration through an optical detection device includes: measuring the optical signal intensities of the smoke particles and dust particles in the smoke channel 1 and the dust channel 2 by the optical detection device in the maze measurement chamber, and comparing them with the signal reflection intensities of the smoke particles and dust particles to determine the guiding deviation coefficients of the smoke channel 1 and the dust channel 2.

[0056] The guiding deviation coefficient of the smoke channel 1 is used to reflect the interference caused by the presence of dust particles in the smoke channel 1 to the detection of smoke particles, and the guiding deviation coefficient of the dust channel 2 is used to reflect the interference caused by the presence of smoke particles in the dust channel 2 to the detection of dust particles.

[0057] On the one hand, in a complex airflow environment, due to their light mass, smoke particles are more easily affected by airflow disturbances and their movement trajectories are difficult to stabilize. It is difficult for them to enter the smoke channel 1 stably along the expected path of gravity. Although dust particles are relatively less affected by airflow, they may also deviate from the sedimentation path guided by gravity and enter the wrong channel under the impact of strong airflow. On the other hand, dust particles are prone to agglomeration. After agglomeration, the volume and mass of the particles increase, and their sedimentation characteristics change. This may cause some dust that should originally enter the smoke channel 1 according to the characteristics of small particles to enter the dust channel 2 due to agglomeration, resulting in guiding deviation. At the same time, smoke particles may also agglomerate under specific conditions, which also affects their accurate entry into the corresponding channels according to the gravity difference.

[0058] Under the guiding deviation coefficient of the smoke channel 1 correct the concentration of the entering dust particles to generate the smoke concentration of the smoke channel 1 , that is , is the optical signal intensity of the smoke particles in the smoke channel 1, is the smoke concentration under the corresponding unit optical signal intensity of the preset smoke particles, which is used to convert the optical signal intensity into the smoke particle concentration value and is determined through experiments. For example is 0.1, is the preset smoke concentration offset, which is used to correct the measurement data of the smoke particles to make it closer to the true value. For example is 0.05. The smoke concentration generation data table of the smoke channel 1 is as follows:

[0059] Table 2: Example table of smoke concentration generation data for the smoke channel

[0060]

[0061] The guiding deviation coefficient of the dust passage 2 corrects the concentration of the incoming smoke particles downward to generate the dust concentration of the dust passage 2 , that is , is the optical signal intensity of the dust particles in the dust passage 2 is the dust concentration corresponding to the preset unit optical signal intensity of the dust particles, which is used to convert the optical signal intensity into the dust particle concentration value and is determined through experiments is the preset dust concentration offset, which is used to correct the measurement data of the dust particles to make it closer to the true value

[0062] Analyzing the dust concentration helps to evaluate the air quality of the smoke detection environment. Dust and smoke particles have similarities in physical properties and behaviors. High-concentration dust may interfere with the operation of the smoke detector, resulting in false alarms or missed alarms. By monitoring the dust concentration in the smoke detection environment, the sensitivity of the detector can be adjusted or measures can be taken to reduce the impact of dust on the detector, thereby improving the accuracy and reliability of smoke detection

[0063] In a further preferred embodiment, the guiding deviation coefficient of the smoke passage 1 is obtained by dividing the optical signal intensity of the smoke particles in the dust passage 2 by the optical signal intensity of the smoke particles in the smoke passage 1

[0064] Ideally, the guiding deviation coefficient is 0, indicating that all smoke particles enter the smoke passage 1. In actual situations, the guiding deviation coefficient is greater than 0, indicating that some smoke particles enter the dust passage 2

[0065] The guiding deviation coefficient of the dust passage 2 is obtained by dividing the optical signal intensity of the dust particles in the smoke passage 1 by the optical signal intensity of the dust particles in the dust passage 2

[0066] step5. Automatically adjust the width of the maze channels in the maze measurement cavity according to the smoke concentration to perform spatial segmentation of the smoke particles and dust particles

[0067] In a preferred embodiment, the automatic adjustment of the width of the maze channels in the maze measurement cavity according to the smoke concentration for spatial segmentation of the smoke particles and dust particles includes: comparing the smoke concentration with the dust concentration to obtain the concentration ratio of the smoke particles to the dust particles, and mapping it to the space ratio of the smoke passage 1

[0068] Control the width of the maze channels in the maze measurement cavity according to the space ratio of the smoke passage 1 to achieve spatial segmentation of the smoke particles and dust particles

[0069] Specifically, in order to enable the width of the maze channels in the maze measurement chamber to be freely changed to create a partitioned space, a movable baffle 3 can be installed in the maze measurement chamber, which helps to dynamically introduce an appropriate amount of smoke and prevent dust particles from interfering with the smoke detection and recognition, thus making the smoke detection result more accurate.

[0070] The width of the maze channels in the maze measurement chamber refers to the opening and closing position width of the movable baffle 3. The total length of the width of the maze channels is a fixed value, such as set to 10 mm. The data examples of the width of the maze channels in the maze measurement chamber are as follows:

[0071] Table 3: Example Table of the Width of the Maze Channels in the Maze Measurement Chamber

[0072]

[0073] Among them, the calculation method of the concentration ratio of smoke particles to dust particles is: [smoke concentration / (smoke concentration + dust concentration)] × 100%, which represents the ratio of smoke particles to the total particulate matter (smoke + dust).

[0074] The present invention screens out water vapor particles, automatically adjusts the width of the maze channels according to the smoke concentration, separately introduces smoke and dust into their respective channels in the maze measurement chamber, realizes the spatial separation of smoke particles and dust particles, enables the detector to work stably in a complex environment with high humidity and a lot of dust, and increases the adaptability of the detector. At the same time, the optical detection device measures the smoke concentration and dust concentration, enhancing the stability and accuracy of the detection.

[0075] step6. Perform a fire warning judgment on the smoke concentration in the maze measurement chamber, and start the cleaning device added in the maze measurement chamber for cleaning according to the smoke concentration and dust concentration.

[0076] Please refer to Figure 3 As shown, in a preferred embodiment, the content of performing a fire warning judgment on the smoke concentration in the maze measurement chamber includes: obtaining the smoke concentration in the smoke channel 1, and determining whether the smoke concentration in the smoke channel 1 in the maze measurement chamber reaches the fire warning standard according to the preset smoke concentration determination model.

[0077] When the smoke concentration in the smoke channel 1 in the maze measurement chamber reaches the fire warning standard, a fire warning is issued.

[0078] Specifically, the preset smoke concentration determination model is as , where represents the smoke concentration determination result instruction in the smoke channel 1 in the maze measurement chamber, represents the smoke concentration in the smoke channel 1, represents the corresponding preset threshold of the smoke concentration in the smoke channel 1, which is determined by experience. It means that the smoke concentration in smoke channel 1 in the maze measurement chamber does not reach the fire warning standard. It indicates that the smoke concentration in smoke channel 1 in the maze measurement chamber has reached the fire warning standard.

[0079] In a further preferred embodiment, the cleaning device added in the maze measurement chamber is activated according to the smoke concentration and the dust concentration to perform cleaning processing. Specifically, the cleaning device is installed in linkage with the airflow control device, and after the fire warning judgment is completed, the airflow is drawn out of the particles in the smoke channel 1 and the dust channel 2 through the airflow control device, and the particle retention amount in each channel is detected at the same time.

[0080] The particle retention refers to the concentration of remaining smoke particles or the concentration of remaining dust particles after the airflow in the channel is led out.

[0081] When at least one of the accumulation amounts of particles in the smoke passage 1 and the dust passage 2 exceeds a preset accumulation amount, the cleaning device is started to perform cleaning processing.

[0082] Specifically, the cleaning device is connected to the smoke channel 1 and the dust channel 2 respectively, so that it can independently clean different channels respectively. For example, when the amount of particle accumulation in the smoke channel 1 exceeds a preset amount, the cleaning device is started to clean the particles in the smoke channel 1.

[0083] The present invention starts a cleaning device added in the maze measuring cavity to perform cleaning processing according to the smoke concentration and the dust concentration, automatically cleans the maze measuring cavity, and ensures the continuity and accuracy of detection.

[0084] In a further preferred embodiment, the smoke sensor maze optimization design and dust pollution resistance method further includes: based on time-division multiplexing technology, alternately detecting LED1 and LED2 signals in the smoke sensor standby state to achieve energy-saving operation.

[0085] The basic idea of ​​time-division multiplexing technology is to divide the time of a channel into multiple time slices, each time slice is used to transmit data from different signal sources, and in a specific time period, only one signal source occupies the channel for data transmission. In the case of using two LEDs in a smoke sensor, different time periods can be allocated to each LED to emit light, so that only one LED is in working state at a time, while the other is in off or low power state.

[0086] By controlling the working state of the LEDs in a time-sharing manner, only one LED is working at any given time, thus reducing the total energy consumption required to turn on all LEDs simultaneously. This not only saves electrical energy but also reduces the thermal effects caused by continuous high-power operation, helping to extend the device life. At the same time, since only one LED is activated for measurement each time, the interference between them is reduced, which helps to improve the quality of signal detection.

[0087] Using time-division multiplexing technology, the working cycle of each LED can also be dynamically adjusted according to actual needs. For example, in the case of low smoke concentration, the detection frequency can be reduced to further save energy; while in high-sensitivity areas where there may be a fire risk, the detection frequency can be increased to ensure timely response.

[0088] The above content is only an example and explanation of the concept of the present invention. Those skilled in the art of the present technology can make various modifications or supplements to the described specific embodiments or use similar methods for substitution, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they should fall within the protection scope of the present invention.

Claims

1. A smoke sensor maze optimization design and dust pollution resistance method, characterized in that: The steps include: The signal transmitting device transmits and receives the first and second band signals to the air detection area to detect the fused particles of smoke and dust and water vapor particles; Adjusting the detection accuracy of the smoke sensor based on the second band signal; The differential signal processing technology is used to process the first band signal, distinguish and identify the fused particles of smoke and dust, and introduce the fused particles into the maze measurement cavity; The smoke concentration and dust concentration are measured by an optical detection device in the maze measurement chamber; Automatically adjust the width of the labyrinth channel in the labyrinth measurement chamber according to the smoke concentration to perform spatial segmentation of smoke particles and dust particles; The smoke concentration in the maze measurement chamber is used to give a fire warning, and the cleaning device added to the maze measurement chamber is activated for cleaning according to the smoke and dust concentrations.

2. The smoke sensor maze optimization design and dust pollution resistance method according to claim 1 is characterized in that: The transmitting and receiving of the first and second band signals to the air detection area is specifically: Detecting fusion particles of smoke and dust through the first band optical signal; The water vapor particles are detected by the second wavelength band light signal.

3. The smoke sensor maze optimization design and dust pollution resistance method according to claim 1 is characterized in that: The smoke sensor detection accuracy adjustment based on the second band signal includes: The second band signal is compared with a preset band signal threshold, and when the second band signal is higher than the preset band signal threshold, the meteorological environment humidity is uploaded through the cloud network; Matching the meteorological environment humidity in a preset humidity-alarm parameter mapping table to obtain the alarm accuracy limit data of the corresponding smoke sensor in the air detection area, wherein the alarm accuracy limit data includes the alarm timing number threshold and the alarm delay time, and the alarm accuracy limit data is used to map the accuracy status of the smoke detector; The humidity-alarm parameter mapping table includes meteorological environment humidity and alarm precision limiting data, and the humidity-alarm parameter mapping table is used to represent the mapping relationship between different meteorological environment humidity and alarm precision limiting data.

4. The smoke sensor maze optimization design and dust pollution resistance method according to claim 1 is characterized in that: The labyrinth measurement chamber includes a water vapor adsorption device, an airflow control device, a smoke channel and a dust channel; The water vapor adsorption device is used to screen out water vapor particles; The airflow control device is used to introduce smoke particles into or out of the smoke channel from an inlet on one side, and introduce dust particles into or out of the dust channel from an inlet on the other side; The smoke channel is used to gather smoke particles; The dust channel is used to collect dust particles.

5. The smoke sensor maze optimization design and dust pollution resistance method according to claim 4 is characterized in that: The differential signal processing technology is used to process the first band signal, distinguish and identify the fused particles of smoke and dust, and introduce the fused particles into the maze measurement cavity, specifically: The first band signal is differentially processed by using differential signal processing technology to determine the signal reflection intensity of smoke particles and dust particles; Determine the low-speed airflow and high-speed airflow in the maze measurement cavity according to the signal reflection intensities of the smoke particles and the dust particles; An airflow control instruction of an airflow control device is generated according to the low-speed airflow and the high-speed airflow, and particles are introduced through the airflow control device.

6. The smoke sensor maze optimization design and dust pollution resistance method according to claim 5 is characterized in that: The method of measuring the smoke concentration and dust concentration in the maze measurement chamber by an optical detection device includes: measuring the optical signal intensity of smoke particles and dust particles corresponding to the smoke channel and the dust channel by the optical detection device in the maze measurement chamber, and comparing the optical signal intensity with the signal reflection intensity of the smoke particles and the dust particles to determine the guide deviation coefficient of the smoke channel and the guide deviation coefficient of the dust channel; The smoke concentration of the smoke channel is generated by correcting the incoming dust particle concentration under the guide deviation coefficient of the smoke channel; The incoming smoke particle concentration is corrected under the guidance deviation coefficient of the dust channel to generate the dust concentration of the dust channel.

7. A smoke sensor maze optimization design and dust pollution resistance method according to claim 6, characterized in that: The guide deviation coefficient of the smoke channel is obtained by dividing the optical signal intensity of the smoke particles in the dust channel by the optical signal intensity of the smoke particles in the smoke channel; The guide deviation coefficient of the dust channel is obtained by dividing the optical signal intensity of the dust particles in the smoke channel by the optical signal intensity of the dust particles in the dust channel.

8. The smoke sensor maze optimization design and dust pollution resistance method according to claim 1 is characterized in that: The method of automatically adjusting the width of the labyrinth channel in the labyrinth measurement chamber according to the smoke concentration to perform spatial segmentation of smoke particles and dust particles includes: Compare the smoke concentration with the dust concentration to obtain the concentration ratio of smoke particles to dust particles, and map it to the smoke channel space ratio; The width of the maze channel in the maze measurement chamber is controlled according to the space proportion of the smoke channel to achieve spatial segmentation of smoke particles and dust particles.

9. The smoke sensor maze optimization design and dust pollution resistance method according to claim 1 is characterized in that: The fire early warning judgment of the smoke concentration in the maze measurement chamber includes: Obtain the smoke concentration in the smoke channel, and determine whether the smoke concentration in the smoke channel in the maze measurement cavity reaches the fire warning standard according to a preset smoke concentration determination model; When the smoke concentration in the smoke channel in the maze measurement chamber reaches the fire warning standard, a fire warning is issued.

10. A smoke sensor maze optimization design and dust pollution resistance method according to claim 4, characterized in that: The cleaning device added in the maze measurement chamber is activated for cleaning according to the smoke concentration and the dust concentration, specifically: the cleaning device is installed in linkage with the airflow control device, and after the fire warning judgment is completed, the airflow is led out of the particles in the smoke channel and the dust channel through the airflow control device, and the particle retention amount in each channel is detected at the same time; When at least one of the particle accumulation amounts in the smoke channel and the dust channel exceeds a preset accumulation amount, the cleaning device is started to perform cleaning processing.

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