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

Abstract

The application discloses a combined optical detection method for distinguishing water mist and smoke based on circularly polarized light transmission characteristics, comprising the following steps: S1, generating circularly polarized light and collimating and shaping the same into a light beam to be incident into a sample chamber provided with a medium to be measured; S2, receiving transmitted light transmitted through the sample chamber and measuring Stokes parameters S0, S1, S2 and S3 in real time; S3, calculating a degree of polarization and an 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 mean value and standard deviation characteristics thereof respectively; S5, calculating a combined discriminant for distinguishing water mist and smoke based on the mean value and the standard deviation characteristics; and S6, determining whether the current medium is water mist or smoke according to a comparison result of the combined discriminant and a preset threshold value. The application can realize high-precision distinction between water mist and smoke by combined analysis of time variation characteristics of the degree of polarization and the ellipticity based on circularly polarized light transmission characteristics.

Description

Technical Field

[0001] This invention relates to the fields of optical detection and environmental monitoring, specifically to a combined optical detection method for distinguishing water mist and smoke based on the transmission characteristics of circularly polarized light. Background Technology

[0002] As intelligent fire protection, industrial safety monitoring, and environmental sensing functions are increasingly integrated into drones, vehicles, building monitoring, and satellite platforms, traditional smoke detection methods that rely on scattered light intensity or particle concentration are no longer sufficient to meet the demands for high precision, real-time performance, and distinguishability. Traditional optical smoke detectors mostly rely on changes in scattering intensity to determine visible smoke, but environmental atomization conditions (such as water mist or increased humidity) often trigger false alarms, reducing system stability.

[0003] The development of polarization detection technology offers a new approach to solving this problem. Polarized light can reflect characteristics such as particle size, refractive index, and internal structure of the scattering medium. In particular, the degree of polarization (DOP) can quantify the extent to which light depolarizes after scattering, thus revealing the differences in optical properties 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 polarization characteristics, ellipticity, as an important parameter describing the deviation of the polarization state from the elliptical shape and circular polarization, can also sensitively characterize the phase perturbation and multiple scattering effects caused by the scattering medium.

[0005]

[0006] in , S1, S2, and S3 represent the major and minor axes of the polarization ellipse, respectively. S0 represents the total intensity of the light, reflecting the total energy of all polarized and unpolarized components. S1, S2, and S3 are used to characterize the polarization state of the light beam. Specifically, S1 represents the intensity difference between the horizontal and vertical polarization components, S2 represents the intensity difference between the +45° and -45° linear polarization components, and S3 represents the intensity difference between the right-hand and left-hand circular polarization components. Its physical nature is closely related to the phase delay experienced by the incident light in the scattering medium.

[0007] Water mist is typically composed of near-spherical droplets and exhibits strong symmetry. Its scattering of circularly polarized light maintains the polarization state structure to some extent. In contrast, smoke is composed of irregular solid particles, resulting in a more complex scattering process that easily leads to polarization depolarization and phase perturbation. The two types of aerosols differ in their polarization change mechanisms, but traditional detection methods have not yet utilized this difference for effective differentiation.

[0008] Existing fog and smoke detection methods based on light intensity changes or single degree of polarization (DOP) have significant technical limitations in practical applications: First, when the water fog concentration increases or the ambient light changes, the DOP of the transmitted light shows a similar decreasing trend as that of smoke, making it impossible for discrimination methods relying on a single polarization parameter to effectively distinguish between water fog and smoke; in addition, some polarization detection schemes rely on high-cost devices such as polarization imagers or complex optical path structures, which is not conducive to deployment on miniaturized platforms such as drones, mobile terminals, and sensor nodes; more importantly, existing technologies do not jointly model the dynamic changes in polarization degree and ellipticity, and cannot use their time-series characteristics to reveal the inherent differences in the scattering medium, thus significantly reducing recognition performance under conditions of multiple scattering, changes in medium concentration, or complex background light.

[0009] It is evident that existing technologies are insufficient to meet the practical requirements for stable and reliable differentiation 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 within a sliding window, and comprehensively analyze both through a joint discriminant, thereby improving the stable discrimination capability on miniaturized platforms. Summary of the Invention

[0010] In order to overcome the shortcomings of the prior art, the purpose of this invention is to provide a combined optical detection method for distinguishing water mist and smoke based on the transmission characteristics of circularly polarized light. This method can achieve high-precision distinction between water mist and smoke by combining the analysis of the time-varying characteristics of polarization degree and ellipticity based on the transmission characteristics of circularly polarized light.

[0011] To achieve the objective of this invention, the following solution is adopted:

[0012] A combined optical detection method for distinguishing water mist and smoke based on the transmission characteristics of circularly polarized light includes the following steps:

[0013] S1. Generate circularly polarized light, collimate and shape it into a beam, and then incident it into the sample chamber containing the medium to be tested;

[0014] S2. Receive the transmitted light passing through the sample chamber and measure its Stokes parameters S0, S1, S2, and S3 in real time.

[0015] S3. Calculate the polarization degree and ellipticity of the transmitted light based on the Stokes parameters;

[0016] S4. Perform time series analysis on the polarization degree and ellipticity, and extract their mean and standard deviation features respectively;

[0017] S5. Based on the mean and standard deviation characteristics, calculate the joint discriminant value used to distinguish between water mist and smoke;

[0018] S6. Based on the comparison result between the joint discrimination quantity and the preset threshold, determine whether the current medium is water mist or smoke.

[0019] Furthermore, a calibration step is included before performing step S2: dark field calibration and white field calibration are performed on the receiving unit used to receive transmitted light and measure Stokes parameters to eliminate the influence of background light and instrument deviation on the measurement results.

[0020] Furthermore, in step S4, a sliding window method is used for time series analysis. The sliding window contains w consecutive sampling points, and the mean value of the degree of polarization within the window is calculated for each point. and standard deviation and the mean of ellipticity and standard deviation .

[0021] Furthermore, the joint discriminant quantity described in step S5 The calculation formula is:

[0022]

[0023] in, These are the weighting coefficients. This is a bias term.

[0024] Furthermore, the weighting coefficients With bias term The results were obtained through experimental calibration, including collecting sample data under different media environments and adjusting parameters until the discrimination accuracy reached the predetermined requirements.

[0025] Furthermore, in step S6, a single discrimination threshold is set. ,when It was determined to be water mist at that time. It was determined to be smoke.

[0026] Furthermore, the sample chamber is a closed transparent box with light-transmitting holes on both sides for light beams to pass through, and an aerosol injection interface. The light transmittance of the box material is greater than 90%.

[0027] Furthermore, in step S2, a polarimeter is used to measure the Stokes parameters in real time, with a sampling rate of 20Hz to 200Hz.

[0028] Furthermore, in step S1, the linearly polarized light is converted into circularly polarized light by a quarter-wave plate, and the beam is shaped into a collimated beam with a diameter of 3–10 mm by a lens group before being incident into the sample chamber.

[0029] Furthermore, the bottom of the sample chamber has an openable structure for placing a water mist generator or a smoke generator, and the inside of the chamber is equipped with a stirring mechanism to improve the uniformity of aerosol distribution.

[0030] In some embodiments, the light source can be any stable narrowband or broadband light source, which is converted into left- or right-hand circularly polarized light by a polarizer or quarter-wave plate, and then shaped by a lens before being incident on the sample chamber. The sample chamber is a closed transparent box made of a 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 changes of the circularly polarized light with the particulate medium, providing a stable interactive environment for polarization parameter analysis.

[0031] In some embodiments, the receiver is configured with a polarization receiving and analysis unit, employing a polarimeter as the core measuring device. The sampling rate is 20–200 Hz, enabling real-time detection of the polarization state of transmitted light. This instrument can output timestamps, Stokes parameters S0, S1, S2, S3, and other polarization parameters, achieving comprehensive measurement of polarization characteristics.

[0032] In some embodiments, based on the above data, the present invention calculates the DOP and ellipticity of transmitted light, and uses time series feature extraction methods to obtain the average value, fluctuation amplitude, and short-term trend characteristics of DOP, as well as the mean shift, standard deviation, and temporal perturbation characteristics of ellipticity. Water mist samples typically exhibit high and stable DOP values ​​with small ellipticity fluctuations; smoke samples, on the other hand, show a significant decrease and drastic fluctuation in DOP, a significant ellipticity shift, and strong random perturbations. By jointly calculating the above feature quantities within a sliding window and forming a unified discriminant for distinguishing between water mist and smoke, the DOP-ellipticity joint discriminant model of the present invention can possess more robust classification performance. The present invention can significantly improve the accuracy and reliability of fog and smoke identification under complex light fields or dynamic concentration conditions.

[0033] In some embodiments, an output and recording unit is configured. This unit can plot the data acquired by the polarimeter into real-time curves to monitor the continuous changes in DOP and ellipticity, and can trigger alarms when abnormal results are detected. Simultaneously, this unit supports data storage, network uploading, and system linkage, thereby constructing a simple, highly reliable polarization detection and analysis system suitable for engineering applications.

[0034] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0035] 1. This invention achieves high-precision, non-contact optical differentiation between water mist and smoke. By simultaneously extracting and utilizing two key polarization parameters—degree of polarization (DOP) and ellipticity—after the transmission of circularly polarized light, it fully reflects the essential differences in depolarization behavior and phase perturbation characteristics between water mist (near-spherical droplets) and smoke (irregular solid particles), overcoming the problem of high misjudgment rates caused by traditional methods that rely solely on light intensity or a single polarization parameter.

[0036] 2. This invention improves the identification stability and anti-interference capability in complex environments such as dynamic lighting, concentration changes, and multiple scattering. By performing time series analysis on DOP and ellipticity and extracting their mean and standard deviation features, the dynamic differences and statistical regularities in the polarization response of the two types of media can be effectively captured. Based on this, a joint discriminant is constructed for comprehensive judgment, avoiding misjudgments caused by instantaneous fluctuations of a single parameter, and significantly enhancing the environmental adaptability of the method.

[0037] 3. This invention provides a discriminant model that is clearly structured, highly interpretable, and easy to deploy in engineering. From optical measurement to feature extraction and then to discriminant decision-making, the steps are logically coherent and the system is complete. The discriminant parameters are constructed based on features with clear physical meaning (mean, standard deviation), and the model parameters can be calibrated experimentally, facilitating rapid adaptation and optimization in different application scenarios. This is beneficial for implementation and application in practical engineering projects such as fire early warning and industrial safety monitoring.

[0038] 4. This invention lays a reliable technical foundation for system function expansion and integration. By acquiring polarization parameters in real time and outputting clear medium type determination results, this invention can seamlessly integrate functions such as data storage, real-time curve display, network alarm, and multi-mode expansion, supporting the construction of an intelligent and integrated aerosol type identification system, and possessing good practical value and engineering feasibility. Attached Figure Description

[0039] Figure 1 This is a flowchart of a combined optical detection method for distinguishing water mist and smoke based on the transmission characteristics of circularly polarized light in an embodiment of the present invention;

[0040] Figure 2 This is an experimental optical path diagram for distinguishing water mist and smoke using circularly polarized light in an embodiment of the present invention;

[0041] Figure 3 This is a comparison of different frames of polarization ellipse trajectories under different media in an embodiment of the present invention;

[0042] Figure 4 This is a graph showing the variation of ellipticity with frame rate under different media in an embodiment of the present invention;

[0043] Figure 5 This is a graph showing the variation of ellipticity with frame rate under different media after linear fitting in an embodiment of the present invention.

[0044] Figure 6 This is a graph showing the change in polarization degree with frame rate under different media in an embodiment of the present invention;

[0045] Figure 7 This is a graph showing the change in polarization degree with frame rate under different media after linear fitting in an embodiment of the present invention;

[0046] Figure 8 This is a graph showing the complete changes in ellipticity and DOP corresponding to changes in smoke concentration in an embodiment of the present invention;

[0047] Figure 9 This is a comparison of ROC curves of the method for distinguishing water mist and smoke under circularly polarized single DOP feature discrimination in an embodiment of the present invention;

[0048] Figure 10 This is a comparison of ROC curves of the water mist and smoke discrimination method under the single ellipticity feature discrimination of circular polarization in the embodiments of the present invention;

[0049] Figure 11 This is a comparison chart of ROC curves for the method of distinguishing water mist and smoke under the combined feature discrimination of circular polarization in the embodiments of the present invention;

[0050] Figure 12 This is a schematic diagram of the sample chamber structure in an embodiment of the present invention;

[0051] Figure 13 This is a side view of the sample chamber in an embodiment of the present invention.

[0052] Reference numerals: 1. Sample chamber; 2. Box body; 3. Openable structure; 4. Stirring mechanism. Detailed Implementation

[0053] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments. It should be noted that, without conflict, the various embodiments or technical features described below can be arbitrarily combined to form new embodiments.

[0054] like Figure 1-11 As shown, this embodiment of the invention provides a combined optical detection method for distinguishing water mist and smoke based on the transmission characteristics of circularly polarized light, including the following steps:

[0055] S1. Generate circularly polarized light, collimate and shape it into a beam, and then incident it into the sample chamber containing the medium to be tested.

[0056] In this embodiment, the testing system used in this invention mainly includes: a light source module, a polarization control device, 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.

[0057] 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. An adjustable quarter-wave plate (λ / 4) is placed after the light source. Left-handed or right-handed circularly polarized light is obtained by adjusting the relative angle between the linear polarization direction and the fast axis of the waveplate. In a preferred configuration, rotating the quarter-wave plate achieves approximately ideal circularly polarized output, and the waveplate is equipped with a precision fine-tuning mechanism and a calibrated angle scale to improve the controllability and repeatability of the polarization state. To ensure a stable and uniform spatial distribution of the beam incident on the sample chamber, this invention employs a lens shaping system (including a collimating lens and a beam expander / telescope objective lens combination) to shape the beam into a collimated beam with a diameter of approximately 3–10 mm. Axial and lateral alignment is achieved through an adjustable optical support, thereby obtaining a stable and reliable incident light field.

[0058] In this embodiment, the sample chamber adopts a closed transparent box structure with internal dimensions on the order of approximately 10–20 cm, which can be adjusted appropriately according to the application scenario. The box material is selected from high-transmittance optical glass or transparent polycarbonate, with a transmittance of over 90% under laser conditions. To further improve the stability of the optical path, small holes with a diameter of 0.5–2 mm are provided on both sides of the sample chamber for beam entry and exit to reduce stray light interference. Light-blocking tape is adhered around the holes, allowing the beam to enter or leave the sample chamber only through the light-transmitting holes, thereby effectively limiting the spot size, avoiding stray light interference, and ensuring that the spot measured at the receiving end is more concentrated and stable. Other sealed interfaces are also reserved on the box for aerosol injection and discharge, while the remaining parts remain optically sealed to ensure that the incident and transmitted light paths are not obstructed or interfered with.

[0059] S2. Receive the transmitted light passing through the sample chamber and measure its Stokes parameters S0, S1, S2, and S3 in real time.

[0060] Understandably, a complete pre-calibration of the polarization receiving system is necessary before the formal experiment to ensure good repeatability and comparability of the measured polarization parameters. Specifically, the polarimeter is first calibrated for both dark and white fields to eliminate the effects of background noise, ambient light leakage, and detector response inhomogeneity, thereby significantly improving the standardization of subsequent Stokes parameter measurements. The experiment uses a PAX1000VIS polarimeter as the core measurement unit, with a sampling rate set from 20Hz to 200Hz. It can output four Stokes parameters (S0, S1, S2, and S3) with timestamps in real time, providing fundamental data support for the calculation of characteristics such as degree of polarization (DOP) and ellipticity. To avoid polarization measurement deviation caused by geometric errors, the optical axis of the polarimeter and the sample chamber exit window must be strictly aligned, and its receiving aperture must match the size of the shaped beam. At the same time, an aperture or filter can be placed in front of the polarimeter to control the numerical aperture and suppress off-axis scattering, and the stability of the incident geometry can be ensured by mechanical fixing to avoid angular drift caused by vibration.

[0061] S3. Calculate the polarization degree and ellipticity of the transmitted light based on the Stokes parameters.

[0062] S4. Perform time series analysis on the polarization degree and ellipticity, and extract their mean and standard deviation features respectively.

[0063] S5. Based on the mean and standard deviation characteristics, calculate the joint discriminant value used to distinguish between water mist and smoke.

[0064] S6. Based on the comparison result between the joint discrimination quantity and the preset threshold, determine whether the current medium is water mist or smoke.

[0065] In this embodiment, the polarimeter can output polarization parameters such as degree of polarization (DOP) and ellipticity in real time, and the data can be exported for subsequent analysis. In this embodiment, the time series data of DOP and ellipticity obtained from the polarimeter are jointly discriminated using a sliding window statistical analysis method to achieve real-time classification of water mist and smoke. The measured values ​​at each time point are:

[0066]

[0067] Define the sliding window size as Samples (e.g.) ), at each time point Calculate the past Mean and standard deviation of each sample:

[0068]

[0069] The above , , , After normalization, the results are then substituted into the discriminant formula:

[0070]

[0071] in, These are the weighting coefficients. This is a bias term.

[0072] Since the model's output is a linear score (i.e., log odds), it doesn't have a range from 0 to 1 and cannot be directly described as a "probability." For ease of understanding and demonstration, it is converted into a probabilistic form using the sigmoid function. This probability value is only used for result description.

[0073]

[0074] Weighting coefficient and bias terms The results were obtained through experiments. The specific steps are as follows: Based on labeled water mist and smoke sample data, a discrimination program was run to calculate its output and corresponding evaluation metrics (such as accuracy). In each round of experiments, the parameters were updated according to the classification results and evaluation metrics, gradually bringing the model's discrimination characteristics closer to the target classification rules. Through multiple rounds of parameter tuning and performance verification, the accuracy and stability of the model on different types of samples were comprehensively compared. Finally, the set of weight parameters that performed best overall was selected and used as the recommended configuration for subsequent testing and application deployment in this embodiment.

[0075] Given the smoke and water mist There are significant differences; this invention sets a single discrimination threshold. To distinguish between the two types of media: when It was determined to be smoke when The threshold value is then determined to be water mist. It can be determined based on actual experimental calibration to adapt to different measurement environments and sensor characteristics, thereby ensuring the accuracy and stability of the discrimination method under complex optical field conditions.

[0076] Experimental example:

[0077] This experimental example compares three discrimination methods. The first is a single-feature discrimination method for circularly polarized DOP, which only uses the circularly polarized DOP. and Features. The second type is the single feature discrimination method for circular polarization ellipticity, which only uses the ellipticity of circular polarization. and Features. The third method is the joint discrimination method of circular polarization DOP and ellipticity features proposed in this invention, which simultaneously utilizes the four-dimensional features of circular polarization DOP and ellipticity. , , , In the joint discrimination scheme, the contributions of each statistical feature are not entirely consistent under different sampling conditions. To address this issue, an adaptive weight training strategy based on machine learning is adopted. This strategy automatically obtains the weight coefficients of each feature through supervised learning and adjusts the weights based on the strength of the features.

[0078] Regarding the training and testing datasets, the training and testing datasets for this experiment consist of manually labeled raw observation datasets. Each file in the dataset corresponds to an independent acquisition process, and its overall attributes (smoke or water mist) are manually verified and labeled. This labeling method ensures that the training samples have clear and reliable category labels, providing a reliable basis for the calibration of model weights and thresholds.

[0079] During the training phase, this invention first extracts statistical features from hundreds of training files manually labeled as "smoke" or "water mist." Each data file in the training dataset contains 50 rows of raw 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 size to 1. Multiple window samples are generated from each file, and the required statistical features are calculated within each window. For each window sample, the corresponding mean and standard deviation are calculated based on the selected features. The arithmetic mean of all DOP values ​​within the window. Its standard deviation; This represents the average absolute ellipticity within 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.

[0080] 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.

[0081] 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:

[0082]

[0083] 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.

[0084] 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.

[0085] Compared to the discrimination method using only DOP, the joint approach improves accuracy by approximately 8.6 percentage points and overall discriminative power (AUC) by approximately 0.018, representing a relative gain of approximately 1.8%. Compared to the approach using only Ellipticity, the joint method improves accuracy by approximately 23.4 percentage points and AUC by approximately 0.311, corresponding to a relative increase of approximately 45%. Overall, the joint feature approach achieves significant improvements in both accuracy and discriminative stability, with the most significant improvement among both single-feature methods.

[0086] Experimental Data List

[0087]

[0088] like Figure 12 and 13As shown, in this embodiment, the sample chamber 1 includes a box 2 and an openable structure 3 located at the bottom of the box 2. The interior of the box 2 is connected to the interior of the openable structure 3. The water mist generator and the smoke generator can be directly placed in the openable structure 3. By controlling their start-up time and working duration, the water mist or smoke can be uniformly filled into the entire space of the box 2 in a short time. After reaching the set concentration, the water mist generator and the smoke generator are turned off and the external aerosol replenishment is stopped, thereby obtaining a stable and controlled testing environment. If necessary, a stirring mechanism 4, such as a small fan, can be installed inside the box 2 to further improve the spatial uniformity of particle distribution. To ensure experimental repeatability, 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.

[0089] This invention presents a fog and smoke detection method based on the combined characteristics of the degree of polarization and ellipticity of circularly polarized light transmission. This method solves the problems of high misjudgment rate and weak anti-interference ability caused by relying solely on scattered light intensity or a single polarization parameter in existing technologies. It utilizes the differences in depolarization behavior of circularly polarized light in different aerosol media, measures the Stokes parameter of the transmitted light, and performs sample value analysis, fluctuation amplitude assessment, and time trend fitting of the degree of polarization (DOP). Simultaneously, it introduces ellipticity to quantify the phase perturbation and polarization ellipticity caused by the scattering medium, thereby achieving high-precision and high-reliability differentiation between water fog and smoke. In a further improvement of this invention, the time series data of DOP and ellipticity are jointly calculated to extract their mean, standard deviation, and short-term variation characteristics, and a simplified joint discriminant (i.e., a comprehensive discriminant) is constructed to distinguish between water fog and smoke. By jointly analyzing the stability and attenuation characteristics of DOP, as well as the shift and perturbation characteristics of ellipticity, the stability, consistency, and anti-interference ability of the discrimination results are effectively improved. This invention utilizes DOP to reflect the degree of depolarization and ellipticity to reflect the phase mixing characteristics of polarization states. It can maintain reliable recognition accuracy under dynamic lighting, concentration changes, multiple scattering, or complex light field conditions, and does not require high-cost polarization imaging equipment. It has a simple structure, fast response, and is suitable for scenarios such as fire early warning, industrial safety monitoring, and environmental aerosol detection.

[0090] The embodiments of the present invention have the following technical effects:

[0091] 1. This invention selects degree of polarization (DOP) and ellipticity as key physical parameters for distinguishing water mist from smoke, making full use of the different polarization characteristics of the two types of aerosol media under circularly polarized light transmission to achieve non-contact, high-precision water mist / smoke differentiation.

[0092] 2. By conducting dark field and white field correction experiments when measuring Stokes parameters, this invention can effectively eliminate the influence of background light and instrument deviation on the measurement results, thereby obtaining more accurate and reliable DOP and ellipticity data and improving the accuracy of distinguishing water mist and smoke.

[0093] 3. This invention calculates the mean and variance of DOP and ellipticity using a sliding window, and constructs a linear joint score with weights. With bias It can automatically find the most suitable parameters through machine learning. The model structure is intuitive and highly interpretable, making it suitable for rapid deployment and parameter calibration in real-world engineering environments.

[0094] 4. The receiving device of the present invention can draw polarization curves in real time and dynamically monitor changes. It also has data storage, network transmission and alarm triggering functions. It supports multi-wavelength and multi-angle expansion in the future, thereby improving the system's environmental adaptability, practical value and engineering feasibility.

[0095] 5. This invention improves the stability and controllability of the system measurement by building a self-constructed polarization measurement experimental platform. The sample chamber is a closed transparent box of specified dimensions, made of highly transparent polycarbonate (transmittance >90%). The box is equipped with an aerosol injection interface and transparent micropores machined on both symmetrical sides, with light-blocking tape applied 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 DOP and ellipticity measurements.

[0096] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the embodiments described. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of this 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 through the sample chamber and measuring Stokes parameters S0, S1, S2 and S3 in real time; before this step 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 deviation on the measurement results; 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 the ellipticity, and extracting the mean value and the standard deviation characteristics thereof respectively; wherein the time series analysis is performed by using a sliding window method, the sliding window containing continuous w sampling points, and the mean value and the standard deviation of the degree of polarization in the window are calculated respectively and the mean value and the standard deviation of the ellipticity in the window are calculated respectively ; S5、based on the mean and standard deviation features, calculate a joint discriminant quantity for distinguishing between water mist and smoke; the joint discriminant quantity The calculation formula is: wherein, is a weight coefficient, is a bias term; S6, determining whether the current medium is water mist or smoke according to the comparison result of the joint discriminant and the 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, 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.

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, A single discrimination threshold is set in step S6 When , it is determined to be water mist, and when , it is determined to be smoke.

4. 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, 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 is greater than 90%.

5. 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.

6. 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.

7. 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 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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