CN117169153A - Chlorine gas detection method and system based on ultraviolet absorption spectrum inversion concentration - Google Patents

Abstract

The invention discloses a chlorine gas detection method based on ultraviolet absorption spectrum inversion concentration. By constructing an ultraviolet absorption spectrum data set of chlorine gas, and performing concentration annotation on the ultraviolet absorption spectrum data in the ultraviolet absorption spectrum data set; using the ultraviolet absorption spectrum data after concentration annotation The absorption spectrum data is used to train the concentration inversion model until the weight parameter with the smallest loss function of the concentration inversion model is the optimal concentration inversion model. The real-time collected UV absorption spectrum of the area to be detected is input into the optimal concentration inversion model. , the concentration detection result of chlorine gas can be obtained. The present invention can realize long-distance quantitative monitoring of chlorine gas by using ultraviolet absorption spectrum. The optical path covers a wide range, does not require a large number of points, and is not interfered by wind direction and diffusion process; the present invention can quickly and accurately Realizes the detection of chlorine gas; the present invention simplifies the pretreatment process required by traditional chemometric methods and improves the accuracy and robustness of spectral analysis.

Description

A chlorine gas detection method and system based on ultraviolet absorption spectrum inversion concentration

Technical field

The invention belongs to the technical field of chlorine gas detection, and specifically relates to a chlorine gas detection method and system based on ultraviolet absorption spectrum inversion concentration.

Background technique

The current chlorine leakage detection and alarm devices are mainly gas sensors installed at fixed points, including electrochemical and semiconductor types. The gas can only be detected after it diffuses to the location, and is easily affected by the wind direction and diffusion process. The detection distance is limited to the effective Within the range, a large number of points are needed to expand the detection range and improve detection sensitivity.

Differential absorption spectroscopy (DOAS) technology uses the unique "fingerprint" spectral absorption characteristics of light radiation by gas molecules to achieve qualitative identification of different types of gases in the optical path. Based on the Lambert-Beer law, the attenuation of light intensity is related to The absorption characteristics and concentration of the substance itself are related to the optical path, which can further realize the quantitative inversion of the gas concentration in the optical path. In recent years, it has been widely used for long-distance monitoring of fixed pollution source gases such as SO ₂ , NO ₂ , NH ₃ and so on. However, DOAS technology is currently rarely used in the detection of Cl ₂ , mainly because unlike the fast-changing, zigzag-like characteristic absorption structure of the gas absorption cross-section mentioned above, the absorption characteristics of Cl ₂ 's absorption cross-section present a slowly changing parabolic shape. , it is not suitable to use differential methods to separate characteristic absorption structures, and it is impossible to effectively achieve high-precision detection of chlorine; therefore, a chlorine detection method with higher accuracy and detection speed is needed.

Contents of the invention

The purpose of the present invention is to provide a chlorine gas detection method and system based on ultraviolet absorption spectrum inversion concentration, so as to overcome the problems of low chlorine gas detection efficiency and low accuracy in the existing technology.

A chlorine gas detection method based on ultraviolet absorption spectrum inversion concentration, including the following steps:

S1, construct a UV absorption spectrum data set of chlorine gas, and annotate the concentration of the UV absorption spectrum data in the UV absorption spectrum data set;

S2, use the ultraviolet absorption spectrum data after concentration annotation to train the concentration inversion model until the weight parameter with the smallest loss function of the concentration inversion model is the optimal concentration inversion model;

S3: Input the real-time collected UV absorption spectrum of the area to be detected into the optimal concentration inversion model to obtain the chlorine concentration detection result.

Preferably, the ultraviolet absorption spectrum data set of chlorine gas is experimentally acquired using the DOAS experimental system.

Preferably, the DOAS experimental system includes a deuterium lamp source, an off-axis parabolic reflector, a gas absorption cell, a angular pyramid retroreflector, a spectrometer and a gas distribution device. The incident end of one end of the off-axis parabolic reflector is connected to the deuterium through a quartz optical fiber. Light source, the output end of one end of the off-axis parabolic reflector is connected to the spectrometer through a quartz optical fiber, the transceiver probe at the other end of the off-axis parabolic reflector is connected to one end of the gas absorption cell, and the other end of the gas absorption cell is connected to the angular pyramid retroreflector , the off-axis parabolic reflector, the gas absorption cell and the angular pyramid retroreflector are in a straight line, the input end of the gas distribution device is connected to the nitrogen source and the chlorine source, and the output end of the gas distribution device is connected to the gas absorption cell.

Preferably, the deuterium light source is a deuterium light source with a wavelength of 185 to 600 nm; the off-axis angle of the off-axis parabolic reflector is 90°.

Preferably, the gas absorption cell has a tubular structure, and the corner pyramid retroreflector uses a corner pyramid reflector.

Preferably, the quartz optical fiber adopts Y-shaped ultraviolet radiation resistant quartz optical fiber.

Preferably, the gas absorption cell with a tubular structure has a length of 600 mm, a diameter of 20 mm, and UV quartz glass windows at both ends.

Preferably, the wavelength range detected by the spectrometer is 195-524nm, and the resolution is 0.41nm.

Preferably, the collected spectral data are denoised and outliers are processed. The outlier processing uses the interquartile range method to detect the collected spectral data. By calculating the IQR value of the spectral data, that is, the lower quartile of the data set. The median Q2 within the range of the digit Q1 and the upper quartile Q3. Any value exceeding the lower bound Q1-1.5*IQR and the upper bound Q3+1.5*IQR is considered an outlier and is replaced by the median Q2 Outliers.

A chlorine gas detection system based on ultraviolet absorption spectrum inversion concentration, including a data processing module, a model optimization module and a detection module;

The data processing module is used to annotate the concentration of the ultraviolet absorption spectrum data in the ultraviolet absorption spectrum data set;

The model optimization module is used to train the concentration inversion model using the ultraviolet absorption spectrum data after concentration annotation until the weight parameter with the smallest loss function of the concentration inversion model is the optimal concentration inversion model;

The detection module is used to input the real-time collected ultraviolet absorption spectrum of the area to be detected, and obtain the concentration detection result of chlorine based on the real-time collected ultraviolet absorption spectrum of the area to be detected.

Compared with the existing technology, the present invention has the following beneficial technical effects:

The present invention provides a chlorine gas detection method based on the inversion concentration of ultraviolet absorption spectrum, by constructing an ultraviolet absorption spectrum data set of chlorine gas, and performing concentration annotation on the ultraviolet absorption spectrum data in the ultraviolet absorption spectrum data set; using the ultraviolet absorption after concentration annotation The concentration inversion model is trained with spectral data until the weight parameter with the smallest loss function of the concentration inversion model is the optimal concentration inversion model. The UV absorption spectrum of the area to be detected in real time is input into the optimal concentration inversion model. The concentration detection result of chlorine gas can be obtained. The present invention can realize long-distance quantitative monitoring of chlorine gas by using ultraviolet absorption spectrum. The optical path covers a wide range, does not require a large number of points, and is not interfered by wind direction and diffusion process; the present invention can quickly and accurately realize Detection of chlorine gas.

Preferably, the present invention uses wavelet decomposition or S-G filtering for denoising, which will not cause spectral information loss and distortion when removing random noise; the present invention simplifies the preprocessing process required by traditional chemometrics methods and improves the accuracy and accuracy of spectral analysis. robustness.

Description of drawings

In order to make the purpose, technical solutions and advantages of the invention clearer, the invention will be described in further detail below in conjunction with the accompanying drawings.

Figure 1 is a schematic diagram of the chlorine gas detection process based on ultraviolet absorption spectrum in an embodiment of the present invention.

Figure 2 is a schematic diagram of the DOAS experimental system in the embodiment of the present invention.

Figure 3 is a schematic structural diagram of the concentration inversion model based on 1D-CNN in the embodiment of the present invention.

Figure 4a is a least squares inversion result diagram using FFT processing; Figure 4b is an inversion result diagram of the concentration inversion model of the 1D-CNN model of the present invention; Figure 4c is a least squares inversion without denoising the data Result diagram; Figure 4d is the inversion result diagram of the concentration inversion model without denoising the data; Figure 4e is the least squares inversion result diagram of the SVD processing of the data; Figure 4f is the concentration inversion result diagram of the SVD processing of the data. The inversion result diagram of the inversion model; Figure 4g is the least squares inversion result diagram of the wavelet decomposition process of the data; Figure 4h is the inversion result diagram of the concentration inversion model of the wavelet decomposition process of the data; Figure 4i is the inversion result diagram of the data The least squares inversion result diagram of SG sliding filtering processing; Figure 4j is the inversion result diagram of SG sliding filtering processing of the data.

In the figure, 1. Deuterium light source; 2. Off-axis parabolic reflector; 3. Gas absorption cell; 4. Angular pyramid retroreflector; 5. Spectrometer; 6. Gas distribution device.

Detailed ways

In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only These are some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts should fall within the scope of protection of the present invention.

It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It is to be understood that the data so used are interchangeable under appropriate circumstances so that the embodiments of the invention described herein are capable of being practiced in sequences other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, e.g., a process, method, system, product, or apparatus that encompasses a series of steps or units and need not be limited to those explicitly listed. Those steps or elements may instead include other steps or elements not expressly listed or inherent to the process, method, product or apparatus.

The object of the present invention is to provide a chlorine gas detection method based on ultraviolet absorption spectrum inversion concentration, which specifically includes the following steps:

S1, construct a UV absorption spectrum data set of chlorine gas, and annotate the concentration of the UV absorption spectrum data in the UV absorption spectrum data set;

S2, use the ultraviolet absorption spectrum data after concentration annotation to train the concentration inversion model until the weight parameter with the smallest loss function of the concentration inversion model is the optimal concentration inversion model;

S3: Input the real-time collected UV absorption spectrum of the area to be detected into the optimal concentration inversion model to obtain the chlorine concentration detection result.

The UV absorption spectrum data set of chlorine gas was experimentally obtained using the DOAS experimental system.

This application uses the DOAS experimental system to obtain the absorbance in a specific wavelength range at different concentrations and obtain spectral data at different concentrations.

Before performing concentration annotation on the UV absorption spectrum data in the UV absorption spectrum data set, the UV absorption spectrum data in the UV absorption spectrum data set is de-desiccated. Specifically, wavelet decomposition or S-G filtering is used for denoising, which will not cause spectral information loss and distortion when removing random noise.

The DOAS experimental system used in this application includes a deuterium light source 1, an off-axis parabolic reflector 2, a gas absorption cell 3, a corner pyramid retroreflector 4, a spectrometer 5 and a gas distribution device 6. The incident end of one end of the off-axis parabolic reflector 2 It is connected to the deuterium light source 1 through a quartz optical fiber. The exit end of one end of the off-axis parabolic reflector 2 is connected to the spectrometer 5 through a quartz optical fiber. The transceiver probe at the other end of the off-axis parabolic reflector 2 is connected to one end of the gas absorption cell 3. The gas absorption The other end of the pool 3 is connected to the angular pyramid retroreflector 4. The off-axis parabolic reflector 2, the gas absorption pool 3 and the angular pyramid retroreflector 4 are in a straight line. The input end of the gas distribution device 6 is connected to the nitrogen source and chlorine gas. source, the output end of the gas distribution device 6 is connected to the gas absorption pool 3. The gas distribution device 6 is used to mix nitrogen and chlorine to obtain chlorine mixed gas of different concentrations. The mixed chlorine mixed gas is passed into the gas absorption pool 3 to form a stable chlorine environment, and then the deuterium lamp source 1 is used to provide an ultraviolet light source. The light source passes through the off-axis parabolic reflector 2 and then passes through the gas absorption cell 3 to reach the angular pyramid retroreflector 4. The angular pyramid retroreflector 4 retroreflects the ultraviolet light and enters the spectrometer through the optical fiber public transceiver probe. The spectrometer is used to obtain the characteristic spectrum of chlorine. Thus, spectral data at different concentrations were obtained.

The deuterium light source 1 adopts a deuterium light source with a wavelength of 185 to 600 nm; the off-axis angle of the off-axis parabolic reflector is 90°. The gas absorption cell has a tubular structure, which is conducive to forming a stable and uniform chlorine range in the gas absorption cell, thereby obtaining accurate spectral data of chlorine at different concentrations. Corner pyramid retroreflectors use corner pyramid reflectors.

Both nitrogen and chlorine sources use canned standard gas.

The quartz optical fiber adopts a Y-shaped UV-resistant quartz optical fiber. The Y-shaped UV-resistant quartz optical fiber separates the outgoing optical fiber and the incident optical fiber at both ends to connect the deuterium light source 1 and the spectrometer 5 respectively.

This application adopts a tubular structure of a gas absorption pool with a length of 600mm and a diameter of 20mm. Both ends are UV quartz glass windows. The ultraviolet rays pass through the absorption pool after passing through the off-axis parabolic reflector.

The wavelength range detected by the spectrometer is 195-524nm, and the resolution is 0.41nm.

This application uses nitrogen as a balance gas to prepare chlorine gas of different concentrations for experimental testing, and the tail end of the absorption pool is connected to a negative pressure fume hood.

The collected spectral data are denoised; specifically, wavelet decomposition or S-G filtering is used to denoise. To improve the accuracy of data, removing random noise will not cause spectral information loss and distortion.

Perform outlier processing on the collected spectral data, and use the interquartile range method (IQR Method) to detect the collected spectral data. By calculating the IQR value of the spectral data, that is, the lower quartile Q1 of the data set and the upper quartile The median Q2 (Median) within the range of quantile Q3. Any value exceeding the lower bound Q1-1.5*IQR and the upper bound Q3+1.5*IQR is considered an outlier, and the median Q2 is used to replace the outlier.

As shown in Figure 3, the concentration inversion model established in this application uses the 1D-CNN model, adding a maximum pooling layer after every two one-dimensional convolution layers to retain features and reduce the amount of calculation. Each layer of convolution RELU is used as the activation function after the layer. In the concentration inversion model, the number of channels is first increased to 128 and then reduced to 32. The convolution kernel sizes are 7×1, 5×1, and 3×1 respectively, and convolutions of different sizes are used. Kernel to improve the feature extraction and feature representation capabilities of the model. After the convolution operation, the output of the convolution layer is sent to the nonlinear activation function RELU unit to help the model learn the nonlinear characteristics of the data.

Adding a 3×1 maximum pooling layer after each convolutional layer can further reduce the feature dimension and enhance the generalization performance of the model. The output of the last convolutional layer is connected to a flatten layer to convert each channel data into 1 dimension, and then the 1-dimensional output is connected, which is the concentration data of chlorine gas. The data flow is extracted, compressed, and reconstructed layer by layer in the network. structure, a higher-dimensional representation of the input features can be obtained.

The formula of the convolutional layer is described as: Among them/> Represents the weight of the i-th convolution kernel in the L-th convolution layer, /> represents the bias vector of the i-th convolution kernel in the L-th convolution layer, x ^l (j) represents the j-th region data in the L-th convolution layer,/> Represents the output of the i-th convolution kernel in the L-th layer, and is also the input of the j-th region in the i-th channel in the L+1-th layer.

The formula of the RELU activation function is described as: Among them/> Express/> activation value.

The model performance is evaluated through the mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R ² ), and the formula is described as:

The present invention can realize long-distance quantitative monitoring of chlorine gas by using ultraviolet absorption spectrum. It has a wide optical path coverage, does not require a large number of points, and is not interfered by wind direction and diffusion process.

A concentration inversion model based on 1D-CNN is established. Compared with the traditional least square method inversion, the present invention can greatly improve the feature extraction capability of the model, and the inversion accuracy is better than the latter in all aspects, and there is no need to manually extract features, simplifying It eliminates the pre-processing process required by traditional chemometric methods and improves the accuracy and robustness of spectral analysis.

One embodiment of the present invention provides a chlorine gas detection system based on ultraviolet absorption spectrum inversion concentration, including a data processing module, a model optimization module and a detection module;

The data processing module is used to annotate the concentration of the ultraviolet absorption spectrum data in the ultraviolet absorption spectrum data set;

The model optimization module is used to train the concentration inversion model using the ultraviolet absorption spectrum data after concentration annotation until the weight parameter with the smallest loss function of the concentration inversion model is the optimal concentration inversion model;

The detection module is used to input the real-time collected ultraviolet absorption spectrum of the area to be detected, and obtain the concentration detection result of chlorine based on the real-time collected ultraviolet absorption spectrum of the area to be detected.

The present invention conducts experiments to obtain ultraviolet absorption spectrum data of chlorine based on the above-mentioned DOAS experimental system, which specifically includes the following steps:

S1, preheat the deuterium light source 1 and spectrometer 5, test the light source spectrum curve, and set the integration time where the light source curve is close to 80% saturation as the appropriate integration time;

S2: Block the light source to measure the instrument background spectrum I _b (λ), then remove the light source block and record the light source spectrum I _L (λ). Subtract the background spectrum and the light source spectrum from the absorption spectrum measured by the spectrometer I _s (λ) = II _b (λ)-I _L (λ) to eliminate the impact of background noise on measurement accuracy;

S3: Set the chlorine concentration through the gas distribution device 6 and start to continuously flow the configured gas into the gas absorption pool 3. Collect and record 50 sets of absorption spectrum data, then set a new concentration value in the gas distribution device, and record the gas concentration at the same time. Pressure and experimental temperature, wait for 1 minute for the air flow to stabilize and then collect data again.

The method of the present invention extracts the characteristic absorption bands in the 250.03nm ~ 399.92nm band of the chlorine absorption spectrum data set obtained by the experiment, a total of 938 wavelength point data, and then follows the training set: verification set: test set = 0.81: 0.09: 0.1 Divide the spectral data in the three data sets into proportions and standardize the spectral data in the three data sets. The formula is: x _processed = (x-mean)/std. Enter the standardized data into the concentration inversion model for training. First, the concentration inversion model The weight parameters are initialized by During the process, the loss of the verification set is monitored. When the loss of the verification set stops decreasing, the learning rate is automatically reduced. When the loss of the verification set stops decreasing after 50 updates, training is automatically stopped in advance to prevent overfitting of training parameters. The model is implemented based on the Pytorch framework and trained using an Nvidia GeForce RTX 3070Ti GPU.

In order to verify the effect of the present invention, the performance of four denoising algorithms on the ultraviolet absorption spectrum data of chlorine was evaluated, and the concentration inversion performance of the least squares method and the 1D-CNN model on the test set were compared using the evaluation index of the present invention as follows: Table 1.

Table 1

In this embodiment, it can be seen from the comparison results that the concentration inversion performance of the method of the present invention (1D-CNN model) combined with the SG algorithm is the best, with a coefficient of determination of 0.996, MAE of 2.640, RMSE of 4.404, and SMAPE of 8.511%, followed by the combination with Wavelet. Whether it is the least squares regression method or the combination of the 1D-CNN model and the FFT algorithm, the performance is the worst, even worse than the inversion effect without any processing. On the one hand, this is because of the absorption spectrum data corresponding to low concentrations. The absorption is not obvious. The Fourier transform process converts the data from the time domain to the frequency domain and eliminates high-frequency signals, which leads to the loss of this part of the data feature information. On the other hand, the Fourier transform cannot process the spectral signal. The high-frequency mutation signal causes the characteristic information of certain bands to be distorted after Fourier transform. In addition, by horizontally comparing the least squares method and the 1D-CNN model, as shown in Figure 4, a scatter plot is drawn between the test set data described in the above embodiments of the present invention and the model inversion values using different denoising preprocessing algorithms. For comparison, the abscissa is the actual concentration value of chlorine, and the ordinate is the concentration value inverted by the model. In Figure 4a, the least squares inversion result processed by FFT is almost 0 when the concentration is low, and the inversion ability is almost lost at this time. When the concentration is high, the inversion value and the true value can still maintain a certain degree of linearity. , and the inversion results of the concentration inversion model based on the 1D-CNN model of the present invention are shown in Figure 4b. The inversion capability at low concentrations is better than the least squares method, and the inversion results at higher concentrations are also better than the least squares method. Multiplication is more accurate. For the loss and distortion of some feature information caused by FFT, it still has strong feature extraction capabilities, and the coefficient of determination reaches 0.911. Although it is still the worst combination, it far exceeds the coefficient of determination of the least squares method of 0.399. Figure 4c to Figure 4j respectively show the schematic diagram of the model inversion results obtained after processing the data without denoising, SVD, wavelet decomposition and SG sliding filtering. It can be seen that the concentration inversion model based on the 1D-CNN model is overall better than According to the traditional least squares spectral analysis method, the scattered points in the figure are more concentrated on the y=x straight line, which shows that the model inversion value is very close to the real value, which proves the concentration inversion based on 1D-CNN proposed by the present invention. The superiority of the model.

In this embodiment, the chlorine ultraviolet absorption spectrum testing device can obtain and measure the absorption spectrum data of any concentration of chlorine; according to the given concentration inversion model architecture based on 1D-CNN, any user can use Pytorch to implement the model, and After performing pre-processing operations such as outlier detection, replacement and denoising on the original data, the model is trained to perform chlorine concentration inversion. The present invention has the potential to be applied to long-distance chlorine leakage detection and identification and concentration inversion in an open environment.

Claims (10)

1. A chlorine gas detection method based on ultraviolet absorption spectrum inversion concentration, which is characterized by including the following steps: S1, construct a UV absorption spectrum data set of chlorine gas, and annotate the concentration of the UV absorption spectrum data in the UV absorption spectrum data set; S2, use the ultraviolet absorption spectrum data after concentration annotation to train the concentration inversion model until the weight parameter with the smallest loss function of the concentration inversion model is the optimal concentration inversion model; S3: Input the real-time collected UV absorption spectrum of the area to be detected into the optimal concentration inversion model to obtain the chlorine concentration detection result. 2. A chlorine gas detection method based on ultraviolet absorption spectrum inversion concentration according to claim 1, characterized in that the ultraviolet absorption spectrum data set of chlorine gas is obtained experimentally using a DOAS experimental system. 3. A chlorine gas detection method based on ultraviolet absorption spectrum inversion concentration according to claim 2, characterized in that the DOAS experimental system includes a deuterium light source (1), an off-axis parabolic reflector (2), a gas Absorption cell (3), corner pyramid retroreflector (4), spectrometer (5) and gas distribution device (6), the incident end of one end of the off-axis parabolic reflector (2) is connected to the deuterium light source (1) through a quartz optical fiber , the output end of one end of the off-axis parabolic reflector (2) is connected to the spectrometer (5) through a quartz optical fiber, and the transceiver probe at the other end of the off-axis parabolic reflector (2) is connected to one end of the gas absorption pool (3). The gas absorption pool The other end of (3) is connected to the corner pyramid retroreflector (4), the off-axis parabolic reflector (2), the gas absorption cell (3) and the corner pyramid retroreflector (4) are in a straight line, and the gas distribution device ( The input end of 6) is connected to the nitrogen gas source and the chlorine gas source, and the output end of the gas distribution device (6) is connected to the gas absorption pool (3). 4. A chlorine gas detection method based on ultraviolet absorption spectrum inversion concentration according to claim 3, characterized in that the deuterium light source (1) adopts a deuterium light source of 185~600nm; the off-axis parabolic reflector of the off-axis The angle is 90°. 5. A chlorine gas detection method based on ultraviolet absorption spectrum inversion concentration according to claim 3, characterized in that the gas absorption cell is a tubular structure, and the corner pyramid retroreflector adopts a corner pyramid reflector. 6. A chlorine gas detection method based on ultraviolet absorption spectrum inversion concentration according to claim 3, characterized in that the quartz optical fiber adopts a Y-shaped ultraviolet radiation resistant quartz optical fiber. 7. A chlorine gas detection method based on ultraviolet absorption spectrum inversion concentration according to claim 3, characterized in that the length of the gas absorption pool using a tubular structure is 600mm, the diameter is 20mm, and both ends are UV quartz glass windows. . 8. A chlorine gas detection method based on ultraviolet absorption spectrum inversion concentration according to claim 1, characterized in that the wavelength range detected by the spectrometer is 195-524nm, and the resolution is 0.41nm. 9. A chlorine gas detection method based on ultraviolet absorption spectrum inversion concentration according to claim 1, characterized in that the collected spectral data are subjected to denoising processing and outlier processing, and the outlier processing adopts the interquartile range method. Detect the collected spectral data and calculate the IQR value of the spectral data, that is, the median Q2 within the range of the lower quartile Q1 and the upper quartile Q3 of the data set. Anything exceeding the lower bound Q1-1.5*IQR and the upper bound Q3+1.5*IQR values are considered outliers, and the median Q2 is used to replace the outliers. 10. A chlorine gas detection system based on ultraviolet absorption spectrum inversion concentration, characterized by including a data processing module, a model optimization module and a detection module; The data processing module is used to annotate the concentration of the ultraviolet absorption spectrum data in the ultraviolet absorption spectrum data set; The model optimization module is used to train the concentration inversion model using the ultraviolet absorption spectrum data after concentration annotation until the weight parameter with the smallest loss function of the concentration inversion model is the optimal concentration inversion model; The detection module is used to input the real-time collected ultraviolet absorption spectrum of the area to be detected, and obtain the concentration detection result of chlorine based on the real-time collected ultraviolet absorption spectrum of the area to be detected.