Combined optical detection method for distinguishing water mist and smoke based on circularly polarized light transmission characteristics

Abstract

The invention discloses a combined optical detection method for distinguishing water mist and smoke based on the transmission characteristic of circularly polarized light, which comprises the following steps: S1, generating circularly polarized light, collimating and shaping the circularly polarized light into a light beam, and enabling the light beam to enter a sample chamber filled with a to-be-detected medium; s2, transmitting light penetrating through the sample chamber is received, and Stokes parameters S0, S1, S2 and S3 of the transmitting light are measured in real time; s3, calculating the degree of polarization and the ellipticity of transmission light based on the Stokes parameters; s4, carrying out time sequence analysis on the polarization degree and the ellipticity, and respectively extracting mean value and standard deviation characteristics of the polarization degree and the ellipticity; s5, on the basis of the mean value and standard deviation characteristics, calculating a combined discrimination amount used for distinguishing the water mist and the smoke; and S6, judging whether the current medium is water mist or smoke according to a comparison result of the combined judgment quantity and a preset threshold value. According to the invention, high-precision distinguishing of water mist and smoke can be realized through conjoint analysis of time change characteristics of the polarization degree and the ellipticity based on the transmission characteristics of circularly polarized light.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of optical detection and environmental monitoring, and particularly relates to a combined optical detection method for distinguishing water mist and smoke based on circularly polarized light transmission characteristics. BACKGROUND

[0002] With the increasing integration of intelligent fire fighting, industrial safety monitoring and environmental sensing functions on unmanned aerial vehicles, vehicles, building monitoring and satellite platforms, traditional smoke detection methods relying on scattered light intensity or particle concentration have been difficult to meet the needs of high precision, real-time and distinguishing ability. Traditional optical smoke alarms are mostly based on scattered intensity changes to judge visible smoke, while environmental misting conditions (such as water mist or humidity increase) often cause false alarms, which reduces the system stability.

[0003] The introduction of polarization detection technology provides a new way to solve this problem. Polarized light can reflect the particle size, refractive index and internal structure of the scattering medium, and especially the degree of polarization (DOP) can quantify the degree of depolarization of light after scattering, thereby revealing the differences in optical characteristics between water mist and smoke. By measuring the DOP of transmitted or reflected light, different types of aerosols can be distinguished at the same concentration.

[0004] In addition to the degree of polarization, ellipticity, as an important parameter describing the elliptical shape of the polarization state and the degree of deviation from circular polarization, can also sensitively represent the phase disturbance and multiple scattering effect caused by the scattering medium.

[0005] wherein , respectively represent the major axis radius and the minor axis radius of the polarization ellipse, S0 represents the total intensity of the light, reflecting the total energy of all polarized and non-polarized components; S1, S2 and S3 are used to characterize the polarization state of the light beam. Among them, S1 represents the intensity difference between horizontal and vertical polarization components, S2 represents the intensity difference between +45° and -45° linear polarization components, and S3 represents the intensity difference between right-handed and left-handed circular polarization components, which is closely related to the phase delay experienced by the incident light in the scattering medium.

[0006] Water mist is usually composed of nearly spherical droplets, which has strong symmetry, and its scattering of circularly polarized light maintains the polarization state structure to a certain extent; while smoke is composed of irregular solid particles, and the scattering process is more complex, which easily leads to polarization state depolarization and phase disturbance. The two types of aerosols differ in polarization change mechanism, but the traditional detection method has not effectively distinguished the difference.

[0007] The existing fog and smoke detection methods based on light intensity change or single degree of polarization (DOP) have obvious technical limitations in practical application. First, when the water mist concentration rises or the ambient light changes, the DOP of the transmitted light will show a similar downward trend as the smoke, causing the single polarization parameter-dependent discrimination method to fail to effectively distinguish between water mist and smoke. In addition, some polarization detection schemes rely on high-cost devices such as polarization imagers or complex optical path structures, which are not conducive to deployment on small platforms such as unmanned aerial vehicles, mobile terminals, and sensor nodes. More importantly, the existing technology does not model the dynamic changes of the degree of polarization and the ellipticity, and cannot use the time series characteristics of the two to reveal the inherent differences of the scattering medium, so the recognition performance is significantly reduced under multiple scattering, medium concentration changes, or complex background light conditions.

[0008] Therefore, the existing technology cannot meet the actual needs of stable and reliable discrimination between water mist and smoke in complex environments. Therefore, a method is needed that can jointly calculate the time series of DOP and ellipticity, extract their mean and fluctuation characteristics in a sliding window, and comprehensively analyze them through a joint discriminant, thereby improving the stable discrimination ability on small platforms. SUMMARY

[0009] In order to overcome the shortcomings of the prior art, the purpose of the present application is to provide a joint optical detection method for distinguishing water mist and smoke based on circularly polarized light transmission characteristics, which can achieve high-precision discrimination between water mist and smoke by jointly analyzing the time variation characteristics of the degree of polarization and the ellipticity based on circularly polarized light transmission characteristics.

[0010] To achieve the purpose of the present application, the following scheme is adopted: The joint optical detection method for distinguishing water mist and smoke based on circularly polarized light transmission characteristics comprises the following steps: S1, generating circularly polarized light and collimating and shaping it into a light beam to be incident on a sample chamber containing the medium to be measured; S2, receiving the transmitted light transmitted through the sample chamber and measuring its Stokes parameters S0, S1, S2, S3 in real time; S3, calculating the degree of polarization and the ellipticity of the transmitted light based on the Stokes parameters; S4, performing time series analysis on the degree of polarization and the ellipticity, and extracting their mean and standard deviation characteristics, respectively; S5, calculating a joint discriminant for distinguishing between water mist and smoke based on the mean and standard deviation characteristics; S6, determining whether the current medium is water mist or smoke according to the comparison result of the joint discriminant and the preset threshold.

[0011] Further, before step S2, a calibration step is included: dark field calibration and white field calibration are performed on the receiving unit for receiving the transmitted light and measuring the Stokes parameters, so as to eliminate the influence of background light and instrument bias on the measurement results.

[0012] Further, in step S4, a sliding window method is used for time series analysis, the sliding window contains w consecutive sampling points, the mean value of the degree of polarization in the window is calculated respectively and the standard deviation , and the mean value of the ellipticity and the standard deviation .

[0013] Further, in step S5, the calculation formula of the joint discriminant quantity is as follows: wherein, is a weight coefficient, is a bias term.

[0014] Further, the weight coefficient and the bias term are obtained by experiment calibration, including collecting sample data under different medium environments, and adjusting parameters until the discrimination accuracy reaches the predetermined requirement.

[0015] Further, in step S6, a single discrimination threshold is set, when , it is determined as water mist, and when , it is determined as smoke.

[0016] Further, the sample chamber is a closed transparent box, light transmission holes are arranged on both sides of the box for the light beam to pass through, and an aerosol injection interface is arranged, and the light transmission rate of the box material is greater than 90%.

[0017] Further, in step S2, a polarimeter is used to measure the Stokes parameters in real time, and the sampling rate is 20Hz to 200Hz.

[0018] Further, in step S1, a quarter-wave plate is used to convert linearly polarized light into circularly polarized light, and a lens group is used to shape the light beam into a collimated light beam with a diameter of 3-10mm, and then the light beam is incident into the sample chamber.

[0019] Further, the bottom of the sample chamber has an openable and closable structure for placing a water mist generator or a smoke generator, and a stirring mechanism is arranged inside the box to improve the uniformity of aerosol distribution.

[0020] In some embodiments, the light source can be any stable narrowband or broadband light source, which is converted into left-handed or right-handed circularly polarized light by a polarizer or a quarter-wave plate, and then incident on the measured sample chamber after being shaped by a lens. The sample chamber is a closed transparent box made of high-transmittance material to ensure stable transmission and observation of the light signal. Smoke, water mist or other aerosols can be introduced into the box to cause scattering, absorption and polarization state change of the circularly polarized light and the particle medium, providing a stable interactive environment for polarization parameter analysis.

[0021] In some embodiments, the receiving end is configured with a polarization receiving and analyzing unit, which uses a polarimeter as the core measurement device with a sampling rate of 20-200 Hz to detect the polarization state of the transmitted light in real time. The instrument can output time stamp, Stokes parameters S0, S1, S2, S3 and other polarization parameters to achieve comprehensive measurement of polarization characteristics.

[0022] In some embodiments, based on the above data, the present application calculates the DOP and ellipticity of the transmitted light, and uses time series feature extraction method to obtain the average value, fluctuation amplitude and short-term trend characteristics of DOP, and the mean shift, standard deviation and time disturbance characteristics of ellipticity. Water mist samples usually have high DOP values and stable changes, and small ellipticity fluctuations; smoke samples have a significant decrease in DOP and severe fluctuations, a significant shift in ellipticity and strong random disturbance. By jointly calculating the above feature quantities in a sliding window, a unified discriminant quantity for distinguishing water mist and smoke is formed, which can make the DOP-ellipticity joint discriminant model of the present application have more robust classification performance. The present application can significantly improve the accuracy and reliability of smoke identification under complex light field or dynamic concentration conditions.

[0023] In some embodiments, the output and recording unit is configured, which can draw the data obtained by the polarimeter into real-time curves for monitoring the continuous changes of DOP and ellipticity, and can trigger an alarm when the discrimination result is abnormal. At the same time, the unit supports data storage, network upload and system linkage, thereby constructing a polarization detection and analysis system with simple structure, high reliability and suitable for engineering application.

[0024] Compared with the prior art, the present application has the following advantages: 1. The present application realizes high-precision, non-contact optical differentiation of water mist and smoke. By simultaneously extracting and using the two key polarization parameters of DOP and ellipticity of the transmitted circularly polarized light, the essential differences between water mist (near-spherical droplets) and smoke (irregular solid particles) in depolarization behavior and phase disturbance characteristics are fully reflected, overcoming the high misjudgment rate caused by traditional methods relying only on light intensity or a single polarization parameter.

[0025] 2.The application improves the recognition stability and anti-interference ability in complex environments such as dynamic lighting, concentration change and multiple scattering. By time series analysis of DOP and ellipticity, and extracting the mean and standard deviation features, the dynamic differences and statistical rules of the polarization response of the two types of media can be effectively captured. On this basis, a joint discriminant is constructed for comprehensive judgment, which avoids misjudgment caused by instantaneous fluctuation of a single parameter, and significantly enhances the environmental adaptability of the method.

[0026] 3.The application provides a discriminant model with clear structure, strong interpretability and easy engineering deployment. From optical measurement to feature extraction to discriminant decision, the step logic is coherent and the system is complete. The discriminant is constructed based on features with clear physical meaning (mean, standard deviation), and the model parameters can be calibrated through experiments, which is convenient for quick adaptation and optimization in different application scenarios, and is conducive to implementation and application in practical engineering such as fire warning and industrial safety monitoring.

[0027] 4.The application lays a reliable technical foundation for system function expansion and integration. By real-time acquisition of polarization parameters and output of clear medium type judgment results, the application can seamlessly connect data storage, real-time curve display, network alarm and multi-mode expansion functions, support the construction of intelligent and integrated aerosol type recognition system, and has good practical value and engineering feasibility. BRIEF DESCRIPTION OF DRAWINGS

[0028] Figure 1 A flowchart of the joint optical detection method for distinguishing water mist and smoke based on circularly polarized light transmission characteristics in the embodiments of the application; Figure 2 An experimental light path diagram for distinguishing water mist and smoke by circularly polarized light in the embodiments of the application; Figure 3 A different frame comparison diagram of polarization elliptical trajectories under different media in the embodiments of the application; Figure 4 A diagram of the change of ellipticity with frame rate under different media in the embodiments of the application; Figure 5 A diagram of the change of ellipticity with frame rate after linear fitting under different media in the embodiments of the application; Figure 6 A diagram of the change of polarization degree with frame rate under different media in the embodiments of the application; Figure 7 A diagram of the change of polarization degree with frame rate after linear fitting under different media in the embodiments of the application; Figure 8 A complete diagram of the change of ellipticity and DOP corresponding to the change of smoke concentration in the embodiments of the application; Figure 9 A ROC curve comparison diagram of the water mist and smoke discrimination method under the single DOP feature discrimination of circularly polarized light in the embodiments of the application; Figure 10 Figure 4 is a ROC curve comparison diagram of the water mist and smoke discrimination method according to an embodiment of the present application, which is based on the single ellipticity feature of circular polarization; Figure 11 Figure 5 is a ROC curve comparison diagram of the water mist and smoke discrimination method according to an embodiment of the present application, which is based on the combined features of circular polarization; Figure 12 Figure 6 is a structural schematic diagram of a sample chamber according to an embodiment of the present application; Figure 13 Figure 7 is a side view of a sample chamber according to an embodiment of the present application.

[0029] Figure 8 is a structural schematic diagram of a sample chamber according to an embodiment of the present application. DETAILED DESCRIPTION

[0030] The present application will be further described below in conjunction with the drawings and specific embodiments. It should be noted that the embodiments described below or the technical features thereof can be combined with each other to form new embodiments without conflict.

[0031] As shown in Figure 1, the combined optical detection method for distinguishing water mist and smoke based on the transmission characteristics of circularly polarized light according to an embodiment of the present application comprises the following steps: Figures 1-11 S1, generating circularly polarized light and collimating and shaping it into a light beam to be incident into a sample chamber containing a medium to be measured. In this embodiment, the test system used by the present application mainly includes: a light source module, a polarization control component, a collimating / shaping lens group, a closed transparent sample chamber (hereinafter referred to as “sample chamber”), a polarization receiving and analysis unit, a data acquisition and signal processing module, and an aerosol generation / control unit.

[0032] In this embodiment, the light source can be a stable narrowband or broadband light source, and its output power is set according to the measurement requirements to avoid thermal disturbance to the aerosol. A tunable quarter-wave plate (λ / 4) can be placed behind the light source to obtain left-handed or right-handed circularly polarized light by adjusting the relative angle between the linear polarization direction and the fast axis of the wave plate; in the preferred setting, rotating the quarter-wave plate can achieve approximately ideal circular polarization output, and the wave plate is equipped with a precision fine-tuning mechanism and has a calibrated angle scale to improve the controllability and repeatability of the polarization state. In order to ensure that the light beam incident into the sample chamber has a stable and uniform spatial distribution, the present application uses a lens shaping system composed of lenses (including a collimating lens and a beam expander / telescopic objective lens combination) to shape the light beam into a collimated light beam with a diameter of about 3-10 mm, and to perform axial and lateral alignment through an adjustable optical support, thereby obtaining a stable and reliable incident light field.

[0033]

[0034] ​In this embodiment, the sample chamber adopts a closed transparent box structure, and the internal size can be about 10-20 cm. The material of the box is selected from high-transmittance optical glass or transparent polycarbonate, and the transmittance under the laser can reach more than 90%. In order to further improve the stability of the optical path, small holes are arranged on both sides of the sample chamber for the entry and exit of the light beam, and the aperture can be 0.5-2 mm, so as to reduce the interference of stray light. In addition, light isolation tape is adhered around the holes, so that only the light beam can pass through the light transmission hole to enter or exit the sample chamber, thereby effectively limiting the size of the light spot, avoiding the interference of stray light, and ensuring that the light spot measured at the receiving end is more concentrated and stable. Other sealed interfaces are reserved on the box for the injection and discharge of aerosols, and the remaining part is kept optically sealed to ensure that the incident light and transmitted light paths are not blocked and disturbed.

[0035] S2, receiving the transmitted light through the sample chamber and measuring the Stokes parameters S0, S1, S2, S3 in real time.

[0036] It can be understood that before the formal experiment, the polarization receiving system needs to be completely pre-calibrated to ensure that the measured polarization parameters have good repeatability and comparability. Specifically, first, the dark field (Dark) and white field (White) calibration of the polarimeter is performed to eliminate the influence of factors such as background noise, ambient light leakage, and uneven detector response, thereby significantly improving the standardization of subsequent Stokes parameter measurement. In the experiment, a PAX1000VIS type polarimeter is used as the core measurement unit, and the sampling rate is set to 20Hz to 200Hz, which can output the four Stokes parameters S0, S1, S2, S3 with timestamps in real time, providing basic data support for the calculation of polarization degree (DOP), ellipticity and other characteristics. In order to avoid the polarization measurement deviation caused by geometric error, the optical axis of the polarimeter and the exit window of the sample chamber need to be strictly aligned, and the receiving aperture needs to match the size of the shaped light beam; at the same time, an aperture or filter can be configured in front of the polarimeter to control the numerical aperture and suppress off-axis scattering, and the stability of the incident geometry is ensured by mechanical fixation to avoid angle drift caused by vibration.

[0037] S3, calculating the polarization degree and ellipticity of the transmitted light based on the Stokes parameters.

[0038] S4, performing time series analysis on the polarization degree and ellipticity, and extracting their mean value and standard deviation characteristics respectively.

[0039] S5, calculating the joint discriminant quantity for distinguishing water mist and smoke based on the mean value and standard deviation characteristics.

[0040] S6, determining whether the current medium is water mist or smoke according to the comparison result of the joint discriminant quantity and the preset threshold.

[0041] In the embodiment, the polarimeter can output polarization parameters such as DOP (Degree of Polarization) and Ellipticity in real time, and can export data for subsequent analysis. In the embodiment, the time series data of DOP (Degree of Polarization) and Ellipticity obtained from the polarimeter are jointly discriminated by a sliding window statistical analysis method to realize real-time classification of water mist and smoke. Let the measurement value at the first time point be: The size of the sliding window is defined as samples (for example ), and the mean and standard deviation of the past samples are calculated at each time point . After normalization, the above , , , are brought into the discriminant formula: where is the weight coefficient, and is the bias term.

[0042] Since the output of the model is a linear score (i.e. in log-odds form), it does not have a range of 0 to 1 and cannot be directly described as "probability". For ease of understanding and display, it is converted to a probability form , and this probability value is only used for result explanation.

[0043] The weight coefficient and the bias term are obtained through the experiment process. The specific steps are as follows: based on the labeled water mist and smoke sample data, run the discriminant program and calculate the output results and the corresponding evaluation indicators (such as accuracy). In each round of experiment, the parameters are updated according to the classification results and evaluation indicators, so that the model discriminant characteristics gradually approach the target classification rule. Through multiple rounds of parameter optimization and performance verification, the correct rate and stability of the model on different types of samples are comprehensively compared, and finally a set of weight parameters with the best overall performance is selected as the recommended configuration of the embodiment for subsequent testing and application deployment.

[0044] Since there is a significant difference between smoke and water mist , the present application realizes the differentiation of the two types of media by setting a single discriminant threshold : when , it is determined as smoke, and when​ The threshold value is determined as water mist. The threshold value The threshold value can be determined according to actual experimental calibration to adapt to different measurement environments and sensor characteristics, so as to ensure the accuracy and stability of the discrimination method under complex light field conditions.

[0045] Experimental example: This experimental example compares three discrimination methods. The first is a circular polarization DOP single feature discrimination method that only uses the and features of the circular polarization DOP. The second is a circular polarization Ellipticity single feature discrimination method that only uses the and features of the circular polarization ellipticity. The third is the circular polarization DOP and Ellipticity feature joint discrimination method proposed by the present application, which simultaneously uses the four-dimensional features of the circular polarization DOP and ellipticity , , , In the joint discrimination scheme, the contribution of each statistical feature under different sampling conditions is not completely consistent. To solve this problem, an adaptive weight training strategy of machine learning is used to automatically obtain the weight coefficients of each feature, and the weight is respectively enhanced or weakened according to the strength of the feature.

[0046] Regarding the data set for training and testing, the training and testing data sets of this experimental example are composed of original observation data sets labeled one by one by artificial. The files in each data set correspond to an independent collection process, and the overall attributes (smoke or water mist) are confirmed and marked by artificial. This labeling method ensures that the training samples have clear and reliable class labels, providing a reliable basis for the calibration of model weights and thresholds.

[0047] In the training phase, the present application first extracts statistical features from hundreds of training files labeled as "smoke" or "water mist" by artificial. In the training data set, each data file contains 50 rows of original data measured frame by frame. To enhance the diversity and robustness of the calibration samples, the sliding window size is set to 10 and the step is set to 1, and multiple window samples are generated from each file, and the required statistical features are calculated in the window. For each window sample, the corresponding mean and standard deviation are calculated according to the selected features. Among them, is the arithmetic mean of all DOP values in the window, is the standard deviation thereof; is the mean absolute ellipticity in the window, The standard deviation is used. Based on these labeled training samples, a set of feature coefficients that best distinguish between the two types of media is automatically learned through a logistic regression model, thus obtaining the optimal weight parameters. After obtaining the weight parameters, a discrimination threshold for distinguishing between smoke and water mist needs to be determined. During the training phase, discrimination scores are calculated for all training samples, and ROC curves are plotted in conjunction with the real labels; then, the optimal threshold T is selected from the ROC curves using the Youden exponent method. This threshold corresponds to the best balance between detection rate and false alarm rate and serves as a fixed discrimination limit for subsequent testing phases.

[0048] During the testing phase, for each file containing 50 rows of data in the test folder, statistical features are first calculated on the entire 50 rows of data in the same manner as during training (if a feature is missing, it is handled according to the preset recording strategy), and then substituted into the discriminant formula determined during the training phase to calculate... Finally, With the threshold obtained from training The comparison process completes the classification of the file as water mist / smoke. This method achieves strict consistency between training and testing through a unified feature definition and discrimination mechanism, ensuring the reliability and deployability of the model in practical applications.

[0049] The total number of documents involved in the evaluation is defined as follows: The number of files correctly predicted in these files is Accuracy is calculated using the following formula: To quantify the stability of AUC under different sample perturbations, this embodiment uses the bootstrap method (repeatedly sampling the test data (1000 times), calculating AUC once each time, obtaining the AUC distribution, and taking the middle 95% range = 95% CI) to estimate its 95% confidence interval, so as to reflect the statistically robust performance of the discriminant model.

[0050] The results of this test are as follows: the accuracy of the single feature discrimination method for circularly polarized DOP is 0.859375, and the AUC is 0.979; the accuracy of the single feature discrimination method for circularly polarized Ellipticity is 0.7109375, and the AUC is 0.686; and the accuracy of the joint discrimination method for circularly polarized DOP and Ellipticity features proposed in this invention is 0.9453125, and the AUC is 0.997.

[0051] Compared with the discriminant method using only DOP, the accuracy of the joint scheme is improved by about 8.6 percentage points, and the overall discriminant ability (AUC) is improved by about 0.018, which represents a relative gain of about 1.8%. Compared with the scheme using only Ellipticity, the joint method improves the accuracy by about 23.4 percentage points, and the AUC is improved by about 0.311, which corresponds to a relative increase of about 45%. Overall, the joint features have achieved significant improvement in accuracy and discriminant stability, with the largest improvement in the two single-feature methods.

[0052] List of experimental data As shown in Figure 12 and 13 In this embodiment, since the sample chamber 1 of the embodiment includes a box 2 and an openable and closable structure 3 arranged at the bottom of the box 2, the inside of the box 2 communicates with the inside of the openable and closable structure 3, and the water mist generator and the smoke generator can be directly placed in the openable and closable structure 3. By controlling the start time and working time of the water mist generator and the smoke generator, the water mist or smoke can be uniformly filled in the entire box 2 space in a short time; after reaching the set concentration, the water mist generator and the smoke generator are turned off and the external aerosol supplement is stopped, thereby obtaining a stable and controlled test environment. If necessary, a stirring mechanism 4, such as a small fan, can be arranged inside the box 2 to further improve the spatial uniformity of particle distribution. In order to ensure the repeatability of the experiment, the sample chamber needs to be cleaned before each experiment during multiple tests, including evacuation or replacement with clean air, to restore stable and consistent initial conditions.

[0053] The fog and smoke detection method based on the combined characteristics of the circularly polarized light transmission degree of polarization and ellipticity of the present application solves the problems of high misjudgment rate and weak anti-interference ability caused by relying only on scattered light intensity or single polarization degree parameter in the prior art. It utilizes the difference in depolarization behavior of circularly polarized light in different aerosol media, measures the Stokes parameters of the transmitted light, and analyzes the sampling value, evaluates the fluctuation amplitude, and fits the time trend of the polarization degree (DOP), and introduces the ellipticity to quantify the phase disturbance and polarization elliptical degree caused by the scattering medium, thereby realizing high-precision and high-reliability differentiation of water mist and smoke. In the further improvement of the present application, the time series data of DOP and ellipticity are jointly calculated, the mean, standard deviation and short-term change characteristics are extracted, and a simplified joint discriminant quantity (i.e. comprehensive discriminant quantity) for distinguishing water mist and smoke is constructed. Through the joint analysis of the stability, attenuation characteristics of DOP and the shift and disturbance characteristics of ellipticity, the stability, consistency and anti-interference ability of the discrimination result are effectively improved. The present application utilizes DOP to reflect the depolarization degree and utilizes ellipticity to reflect the polarization state phase mixing characteristics, and can still maintain reliable identification accuracy under dynamic light, concentration change, multiple scattering or complex light field conditions, and does not require high-cost polarization imaging equipment, has simple structure and fast response, and is suitable for fire warning, industrial safety monitoring and environmental aerosol detection scenes.

[0054] The embodiments of the present application have the following technical effects: 1. The present application selects polarization degree (DOP) and ellipticity as the key physical parameters for distinguishing water mist and smoke, fully utilizes the different polarization characteristics of the two types of aerosol media under circularly polarized light transmission, and realizes non-contact and high-precision water mist / smoke differentiation.

[0055] 2. The present application can effectively eliminate the influence of background light and instrument deviation on the measurement results by performing dark field and white field correction experiments when measuring Stokes parameters, so as to obtain more accurate and reliable DOP and ellipticity data and improve the precision of distinguishing water mist and smoke.

[0056] 3. The present application calculates the mean and variance of DOP and ellipticity through a sliding window, and constructs a linear joint score with weights and bias The most suitable parameters can be automatically found by machine learning, the model structure is intuitive and has strong interpretability, and is suitable for rapid deployment and parameter calibration in actual engineering environment.

[0057] 4. The receiving end equipment of the present application can draw the polarization degree curve in real time and dynamically monitor the changes, and has data storage, network transmission and alarm triggering functions, and supports subsequent multi-wavelength and multi-angle expansion, thereby improving the environmental adaptability, practical value and engineering feasibility of the system.

[0058] 5、The present application improves the stability and controllability of the system measurement by building a polarization measurement experiment platform. The sample chamber is a closed transparent box with a specified size, and the material is high-transmittance transparent polycarbonate (transmittance > 90%). The box is provided with an aerosol injection interface, and light-transmitting micro-holes are processed on the symmetric two sides, and light isolation tapes are attached around the holes to limit the light spot. The aerosol concentration can be controlled by the filling time, thereby improving the accuracy and reliability of the DOP and ellipticity measurement.

[0059] The above is a specific description of the preferred implementation of the present application, but the present application is not limited to the described embodiments. Those skilled in the art can make various equivalent modifications or replacements without departing from the spirit of the present application, and these equivalent modifications or replacements are all included in the scope defined by the claims of the present application.

Claims

1. A combined optical detection method for distinguishing water mist smoke based on the transmission characteristics of circularly polarized light, characterized in that, The method comprises the following steps: S1, generating circularly polarized light and collimating it into a light beam to be incident on a sample chamber containing a medium to be measured; S2, receiving transmitted light that has passed through the sample chamber and measuring its Stokes parameters S0, S1, S2, and S3 in real time; S3, calculating the degree of polarization and ellipticity of the transmitted light based on the Stokes parameters; S4, performing time series analysis on the degree of polarization and ellipticity, and extracting their mean value and standard deviation characteristics, respectively; S5, calculating a joint discriminant for distinguishing between water mist and smoke based on the mean value and standard deviation characteristics; S6, determining whether the current medium is water mist or smoke based on a comparison between the joint discriminant and a preset threshold.

2. The combined optical detection method for distinguishing water mist smoke based on circularly polarized light transmission characteristics according to claim 1, characterized in that, Before step S2 is performed, a calibration step is further included: dark field calibration and white field calibration are performed on a receiving unit for receiving transmitted light and measuring Stokes parameters, so as to eliminate the influence of background light and instrument bias on the measurement results.

3. The combined optical detection method for distinguishing water mist smoke based on circularly polarized light transmission characteristics according to claim 1, characterized in that, In step S4, time series analysis is performed using a sliding window method, the sliding window containing consecutive w sampling points, the mean value of the degree of polarization and the standard deviation , and the mean value of the ellipticity and the standard deviation are calculated for each window.

4. The combined optical detection method for distinguishing water mist smoke based on circularly polarized light transmission characteristics according to claim 3, characterized in that, The combined discriminant quantity in step S5 The calculation formula is: wherein, is a weight coefficient, is a bias term.

5. The combined optical detection method for distinguishing water mist smoke based on circularly polarized light transmission characteristics according to claim 4, characterized in that, The weight coefficient With bias term Obtained by experiment calibration, including collecting sample data under different medium environment, adjusting parameters until the discrimination accuracy reaches the predetermined requirement.

6. The combined optical detection method for distinguishing water mist smoke based on circularly polarized light transmission characteristics according to claim 4, characterized in that, In step S6 a single discrimination threshold is set When water mist is determined, when smoke is determined.

7. The combined optical detection method for distinguishing water mist smoke based on circularly polarized light transmission characteristics according to claim 1, characterized in that, The sample chamber is a closed transparent box, both sides of the box are provided with light transmission holes for the light beam to pass through, and an aerosol injection interface is arranged, and the light transmission rate of the box material is greater than 90%.

8. The combined optical detection method for distinguishing water mist smoke based on circularly polarized light transmission characteristics according to claim 1, characterized in that, In step S2, a polarimeter is used to measure the Stokes parameters in real time, and the sampling rate is 20 Hz to 200 Hz.

9. The combined optical detection method for distinguishing water mist smoke based on circularly polarized light transmission characteristics according to claim 1, characterized in that, In step S1, a quarter-wave plate is used to convert linearly polarized light into circularly polarized light, and a lens group is used to shape the light beam into a collimated light beam with a diameter of 3-10 mm to be incident on the sample chamber.

10. The combined optical detection method for distinguishing water mist smoke based on circularly polarized light transmission characteristics according to claim 7, characterized in that, The bottom of the sample chamber has an openable structure for placing a water mist generator or a smoke generator, and a stirring mechanism is arranged inside the box to improve the uniformity of aerosol distribution.

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