Tracing identification method of industrial wastewater in municipal pipe network based on three-dimensional fluorescence spectrum angle algorithm

Abstract

The invention discloses a tracing identification method for industrial wastewater in a municipal pipe network based on a three-dimensional fluorescence spectrum angle algorithm. The tracing identification method is characterized by comprising the following steps: collecting industrial area municipal pipe network confluence well water outside a factory, industrial wastewater raw water in the factory, treated industrial wastewater and a water sample of an enterprise inspection well; mixing the municipal pipe network confluence well water with the industrial wastewater raw water, the treated industrial wastewater and enterprise inspection well water in the factory according to a series of different proportions to obtain three-dimensional fluorescence spectrum data of water samples with different mixing proportions; reading the three-dimensional fluorescence spectrum data obtained after correction in the step 1 in a python environment, flattening the three-dimensional fluorescence spectrum data into a one-dimensional vector, and performing normalization processing on the one-dimensional vector to obtain one-dimensional vectors of each group of mixed water samples; the spectral difference and similarity between samples are compared and analyzed through a spectral angle algorithm, the characteristics of the industrial wastewater are quantitatively evaluated, qualitative recognition of the industrial wastewater in the municipal pipe network is achieved, and the method has the advantages of being efficient, accurate and convenient.

Description

Technical Field

[0001] This invention relates to the field of water pollution source tracing, and in particular to a method for tracing and identifying the source of industrial wastewater in municipal pipe networks based on a three-dimensional fluorescence spectral angle algorithm. Background Technology

[0002] Emergency water pollution incidents pose significant risks to the normal operation of wastewater treatment plants, especially in densely populated urban areas where the impact is wider and the consequences more severe. These incidents typically stem from the illegal discharge or leakage of untreated or inadequately treated industrial wastewater into municipal sewage networks, causing a concentrated influx of pollutants into the wastewater treatment system within a short period. This results in drastic fluctuations in influent water quality and disrupts the stability of the wastewater treatment system. Industrial wastewater, after being treated at the company's pretreatment station, mixes with domestic sewage and flows into the company's discharge manhole before being discharged into the municipal network and ultimately reaching the wastewater treatment plant.

[0003] In recent years, three-dimensional fluorescence spectroscopy (Excitation-Emission Matrix, EEM) technology has been widely used for source tracing analysis and the construction of characteristic databases for industrial wastewater samples due to its high sensitivity and distinct fingerprint characteristics. By collecting three-dimensional fluorescence spectra of industrial wastewater from different sources, pollution source types can be identified to a certain extent, providing technical support for pollution prevention and control. However, in practical applications, due to the large number of enterprises, complex industry categories, and diverse production processes within the service area of ​​wastewater treatment plants, relying solely on three-dimensional fluorescence spectra for qualitative or quantitative analysis is insufficient to accurately characterize the intensity and source tracing similarity of the impact of various industries and processes on municipal water quality. This complexity and uncertainty limit the application depth of traditional spectroscopic analysis methods in emergency pollution source tracing and risk early warning.

[0004] Spectral Angle Mapper (SAM) is a forward-looking approach for quantitatively solving spectral similarity. Based on the similarity analysis of the overall spectral morphology, the SAM algorithm treats the spectral data of each sample as a vector in a high-dimensional space and measures the similarity between samples by calculating the angle between these vectors. A smaller angle indicates that the spectral features of the samples are more similar, and the likelihood of similar origins is higher; conversely, a larger angle indicates significant differences in sample features. The SAM algorithm focuses on spectral shape features and is widely used in hyperspectral remote sensing image classification. Currently, there are no publicly available applications of three-dimensional fluorescence spectral angle algorithms for source identification of industrial wastewater in municipal pipe networks, either domestically or internationally. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide an efficient, accurate and convenient method for tracing and identifying the source of industrial wastewater in municipal pipe networks based on a three-dimensional fluorescence spectral angle algorithm.

[0006] The technical solution adopted by this invention to solve the above-mentioned technical problems is: a method for tracing and identifying the source of industrial wastewater in municipal pipe networks based on a three-dimensional fluorescence spectral angle algorithm, comprising the following steps: Step 1: Collect water samples from the municipal pipe network manifold outside the industrial zone and from the raw industrial wastewater, treated industrial wastewater, and inspection well water of the enterprises within the factory area; mix the municipal pipe network manifold water with the raw industrial wastewater, treated industrial wastewater, and inspection well water of the enterprises within the factory area in a series of different proportions, obtain the three-dimensional fluorescence spectrum data of the water samples of each mixing proportion, and make corrections. Step 2: Read the three-dimensional fluorescence spectral data obtained after correction in Step 1 in the Python environment, flatten the three-dimensional fluorescence spectral data into a one-dimensional vector, and normalize the one-dimensional vector to obtain the one-dimensional vector of each group of mixed water samples. Step 3: Using the one-dimensional vector of each water sample—mixed with industrial wastewater at different proportions from the municipal sewer network's manifold well water—as the test spectral vector, and the industrial wastewater as the reference spectral vector, calculate the cosine angle between the test spectral vector and the reference spectral vector using a spectral angle mapping algorithm. Convert this angle into a spectral similarity index, obtaining the first linear regression function between the spectral similarity value of the municipal sewer network's manifold well water and the concentration of industrial wastewater in the municipal sewer network's manifold well water. Take the slope of the linear regression function as y. Calculate the spectral similarity between the municipal sewer network's manifold well water sample and the industrial wastewater using the spectral angle mapping algorithm. If the absolute value of the difference between the spectral similarity and the slope is less than or equal to 0.05, it is determined that the enterprise's industrial wastewater has entered the municipal sewer network; if the absolute value of the difference between the spectral similarity and the slope is greater than 0.05, it is determined that the municipal sewer network's manifold well water is not mixed with the enterprise's industrial wastewater. Step 4: Take the one-dimensional vector of each water sample, which is a mixture of municipal pipeline well water and treated industrial wastewater at different ratios, as the test spectral vector, and the fluorescence vector of the treated industrial wastewater as the reference spectral vector. Based on the spectral angle mapping algorithm, calculate the cosine angle between the test spectral vector and the reference spectral vector and convert it into a spectral similarity index. Obtain the second linear regression function of the spectral similarity value between the municipal pipeline well water and the treated industrial wastewater and the concentration of the treated industrial wastewater in the municipal pipeline well water. Substitute the spectral similarity value between the municipal pipeline well water and the treated industrial wastewater to be tested into the second linear regression function to obtain the concentration value of the treated industrial wastewater in the municipal pipeline well water of the industrial area. Step 5: Take the one-dimensional vector of each water sample, which is a mixture of municipal pipeline manifold water and enterprise inspection well water in different proportions, as the test spectral vector, and the fluorescence vector of the sewage from the enterprise inspection well as the reference spectral vector. Based on the spectral angle mapping algorithm, calculate the cosine angle between the test spectral vector and the reference spectral vector and convert it into a spectral similarity index. Obtain the third linear regression function of the spectral similarity value between municipal pipeline manifold water and enterprise inspection well water and the concentration of enterprise inspection well water in municipal pipeline manifold water. Substitute the spectral similarity value between the municipal pipeline manifold water and enterprise inspection well water to be tested into the third linear regression function to obtain the concentration value of enterprise inspection well water in municipal pipeline manifold water of the industrial zone.

[0007] Furthermore, the formula for calculating the cosine angle is: ; In the formula, To test the spectral vector, L is the reference spectral vector, and L is the number of spectral vectors.

[0008] Furthermore, the formula for calculating the spectral similarity index is as follows: .

[0009] Compared with existing technologies, the advantages of this invention are as follows: This invention discloses for the first time a method for tracing and identifying industrial wastewater in municipal pipe networks based on a three-dimensional fluorescence spectral angle algorithm. By combining EEM spectroscopy technology with the spectral angle algorithm, it is innovatively applied to the source tracing scenario of sewage pollutants in municipal pipe networks. This method fully leverages the high sensitivity of EEM spectroscopy technology in identifying various organic substances, and uses the spectral angle algorithm to quantitatively compare fluorescence spectral data, thereby significantly improving the accuracy and discrimination ability of identifying differences in fluorescence spectra of different water samples. This method has the advantages of simple operation, high comparison efficiency, and low computational load. It retains the high sensitivity of three-dimensional fluorescence technology for pollutant identification while significantly improving the efficiency and accuracy of pollution source identification. This invention can be applied to the rapid location of industrial pollution sources in municipal sewage pipe networks, providing reliable technical support for sewage treatment plants to respond to sudden water pollution incidents. Attached Figure Description

[0010] Figure 1 In the middle section, 'a' represents the location map of enterprises A and B and the municipal pipeline manifold, and 'b' represents the wastewater discharge flow diagram and sampling point map inside and outside the enterprise's factory area. Figure 2 The reference spectrum and test spectrum are for pharmaceutical company A. The reference spectrum consists of the fluorescence spectra of raw industrial wastewater, treated industrial wastewater, and inspection wells within the company's plant area. The test spectrum consists of the fluorescence spectra of raw industrial wastewater, treated industrial wastewater, and inspection wells within the company's plant area mixed with different concentration ratios of water from the municipal sewer network's manifold. Figure 3 The SAM calculation results for different mixed wastewater ratios for Company A are shown below. Red represents the experimental results of the ratio of raw industrial wastewater to municipal sewer manhole water; green represents the experimental results of the ratio of treated industrial wastewater to municipal sewer manhole water; and blue represents the experimental results of the ratio of wastewater from the company's inspection well to municipal sewer manhole water. Figure 4 Fitting curves for SAM calculation results of different mixed wastewater ratios for Company A; Figure 5 The reference spectrum and test spectrum are for pen manufacturing company B. The reference spectrum consists of the fluorescence spectra of raw industrial wastewater, treated industrial wastewater and manholes in the company's factory area. The test spectrum consists of the fluorescence spectra of raw industrial wastewater, treated industrial wastewater and manholes in the company's factory area mixed with different concentration ratios of municipal pipeline manifold water. Figure 6 The SAM calculation results for different mixed wastewater ratios for Company B are shown below. Red represents the experimental results of the ratio of raw industrial wastewater to municipal sewer well water; green represents the experimental results of the ratio of treated industrial wastewater to municipal sewer well water; and blue represents the experimental results of the ratio of wastewater from the company's inspection well to municipal sewer well water. Figure 7 Fitting curves for SAM calculation results of different mixed wastewater ratios for Company B. Detailed Implementation

[0011] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Specific Implementation Example 1 Taking pharmaceutical company A as an example, samples were collected from its raw industrial wastewater, treated industrial wastewater, and water from the company's inspection wells within the factory area (a mixture of treated industrial wastewater and domestic sewage). Sampling was also conducted in the municipal sewer network manifolds throughout the industrial zone (sampling points are listed below). Figure 1 As shown in Figures A and B), wastewater from the municipal manifold was mixed with various water samples from within the factory area to simulate the effect of tracing the source of the wastewater using three-dimensional fluorescence imaging technology. Figure 2As shown, the experiment of Company A was divided into three groups: (1) The ratio of municipal pipeline water in the industrial zone to raw industrial wastewater was 1:1, 3:1, 5:1, 10:1 and no raw industrial wastewater was added. (2) The ratio of municipal pipeline water in the industrial zone to treated industrial wastewater was 1:1, 3:1, 5:1, 10:1 and no treated industrial wastewater was added. (3) The ratio of municipal pipeline water in the industrial zone to sewage from the enterprise inspection wells in the factory area was 1:1, 3:1, 5:1, 10:1 and no sewage from the enterprise inspection wells was added. The above water samples were subjected to three-dimensional fluorescence detection. The fluorescence data of the blank samples were subtracted from the sample fluorescence data to remove Raman scattering effects. The fluorescence intensity of Rayleigh scattering lines was set to missing, and the data of the triangular region in the fluorescence spectrum where the emission wavelength was less than the excitation wavelength was set to zero, resulting in corrected three-dimensional fluorescence spectral data. The scanning conditions for the three-dimensional fluorescence measurement were as follows: Hitachi F-4600 fluorophotometer, excitation wavelength 220-450 nm, scan interval 5 nm, emission wavelength 260-600 nm, scan interval 1 nm, slit width 5 nm, scan speed 2400 nm·min. -1 Fluorescence spectrum as follows Figure 3 As shown.

[0013] In the Python environment, the three-dimensional fluorescence spectral data (EEM matrix format) of 15 sets of blank water samples after correction were read, flattened into one-dimensional vectors, and normalized to obtain one-dimensional vectors of 15 different sewage samples. In experiment (1), 5 sets of fluorescence vectors were used as test spectral vectors. , Industrial wastewater raw water is used as a reference spectral vector In experiment (2), the five groups of fluorescence vectors were used as test spectral vectors. , The fluorescence vector of the treated industrial wastewater was used as the reference spectral vector. In experiment (3), the five groups of fluorescence vectors were used as test spectral vectors. , The fluorescence vector of wastewater from the enterprise's inspection well is used as a reference spectral vector. ; In the Python environment, the spectral angle mapping function `spectral_angle_mapper(x, y)` is called to calculate the cosine angle between the test spectral vector and the reference spectral vector `Sy` based on the spectral angle mapping algorithm. , The calculation formula is ; In the formula, To test the spectral vector, L is the reference spectral vector, and L is the number of spectral vectors.

[0014] The 15 included angle values ​​of company A are processed by a function. Fifteen spectral similarity indices were calculated. 1~ 15 The 15 included angle values ​​of company A are obtained through a function. Fifteen spectral similarity indices were calculated. 1~ 15 Based on the concentration ratio of mixed wastewater as The spectral angular similarity is along the y-axis, for (0.1, ...). 2) (0.2, 3), (1 / 3, 4) (0.5, 5) Perform linear regression; for (0, 6), (0.1, 7), (0.2, 8), (1 / 3, 9), (0.5, 10 Perform linear regression on (0, 11 (0.1, 12 (0.2, 13 (1 / 3, 14 (0.5, 15 Linear regression was performed.

[0015] Spectral similarity of different mixed wastewater ratios of Company A The calculation results are as follows Figure 3 As shown. By Figure 3 It can be seen that the spectral angle algorithm exhibits high sensitivity in determining whether industrial wastewater is mixed into municipal pipelines. When no industrial wastewater is added to the municipal pipeline manifold, its spectral angle similarity with that of industrial wastewater from Company A is only 0.10; when the volume concentration of industrial wastewater reaches 10%, the similarity significantly increases to 0.38, indicating that the algorithm has good responsiveness to whether industrial wastewater is mixed into municipal sewage. Further establishing a linear relationship yields the following results: Figure 4 As shown, y = 0.367 + 0.197x, R 2=0.93 (Formula 1), where x is the concentration of raw industrial wastewater in the municipal sewer manifold of the industrial zone, and y is the spectral similarity value between the municipal sewer manifold water and the raw industrial wastewater. The three-dimensional fluorescence spectral data of the municipal wastewater sample to be tested are obtained and processed to obtain a one-dimensional vector S. In the Python environment, the spectral angle mapping function spectral_angle_mapper(x,y) is called. Based on the spectral angle mapping algorithm, the spectral vector S to be tested and the raw industrial wastewater are calculated as the test spectral vectors. The cosine angle between them, through the function Calculate similarity index , take x=0 in formula 1, It is 0.367, when If so, it is determined that the company's industrial wastewater has entered the municipal sewer network; if If the result is positive, it can be determined that there is no risk of leakage of the raw industrial wastewater from the enterprise.

[0016] Depend on Figure 3 It can be seen that there is a linear relationship between the similarity and concentration ratio between the municipal sewer water and the treated industrial wastewater of Company A. Therefore, a linear regression equation was established for analysis. The results are as follows: Figure 4 As shown, the linear relationship between the similarity and concentration ratio between the municipal sewer well water and the treated industrial wastewater of Company A is as follows: y = 0.041 + 0.366x (Formula 2), R² = 0.97, where x is the concentration of the treated industrial wastewater in the municipal sewer well water of the industrial area, and y is the spectral similarity value between the municipal sewer well water and the treated industrial wastewater. The three-dimensional fluorescence spectral data of the municipal wastewater sample to be tested are obtained and processed to obtain a one-dimensional vector S. In the Python environment, the spectral angle mapping function spectral_angle_mapper(x, y) is called. Based on the spectral angle mapping algorithm, the spectral vector S to be tested and the treated industrial wastewater as a reference spectral vector are calculated. The cosine angle between them, through the function Calculate the similarity index y, substitute it into Formula 2, and calculate the concentration of treated industrial wastewater in the municipal pipe network manifold water sample to be tested.

[0017] Depend on Figure 3 It can be seen that there is a linear relationship between the similarity and concentration ratio between the water samples from the municipal pipeline manifold and the inspection wells in Company A's factory area. Therefore, a linear regression equation was established for analysis. The results are as follows: Figure 4As shown, the linear relationship between the similarity and concentration ratio between the municipal pipeline manifold water sample and the factory inspection well water sample of Company A is as follows: y = 0.315 + 0.277x (Formula 2), R² = 0.98, where x is the concentration of treated industrial wastewater in the municipal pipeline manifold water sample, and y is the spectral similarity value between the municipal pipeline manifold water sample and the factory inspection well water sample. Based on the spectral angle mapping algorithm, the spectral vector S to be measured and the fluorescence vector of the wastewater from the company's inspection well are calculated as reference spectral vectors. The cosine angle between them, through the function Calculate the similarity index y, substitute it into Formula 3, and calculate the concentration of sewage from the manhole in the municipal sewer network sample to be tested. For example, when the similarity is 0.4, the concentration of industrial wastewater treated by Company A in the mixed water body accounts for 98%, while the concentration of this component in the manhole water sample accounts for 30.68%. Specific Implementation Example 2 Pen manufacturer B followed company A's experimental procedure and set up the same experiment to verify the accuracy of the industrial wastewater source tracing technology. Their experimental setup included a three-dimensional fluorescence spectrum, as shown in the image. Figure 5 As shown.

[0019] Spectral similarity of different mixed wastewater ratios of Company B The calculation results are as follows Figure 6 As shown. By Figure 6 It can be seen that the similarity between the municipal sewer well water in the industrial zone and the raw industrial wastewater from Company B also increased significantly when the concentration of the raw industrial wastewater was 10%, rising from 0.15 before addition to 0.32, further verifying the high sensitivity of the spectral angle algorithm to the mixing of industrial wastewater. Further results are as follows: Figure 7 As shown, y = 0.309 + 0.081x, R² = 0.97 (Formula 4), where x is the concentration of raw industrial wastewater in the municipal sewer manifold of the industrial zone, and y is the spectral similarity value between the municipal sewer manifold water and the raw industrial wastewater. The three-dimensional fluorescence spectral data of the municipal sewer manifold water sample to be tested are obtained and processed to obtain a one-dimensional vector S. In the Python environment, the spectral angle mapping function spectral_angle_mapper(x, y) is called. Based on the spectral angle mapping algorithm, the spectral vector S to be tested and the raw industrial wastewater are calculated as the test spectral vectors. The cosine angle between them, through the function Calculate similarity index , take x=0 in formula 1, It is 0.309, when If so, it is determined that the company's industrial wastewater has entered the municipal sewer network; if If the result is positive, it can be determined that there is no risk of leakage of the raw industrial wastewater from the enterprise.

[0020] Depend on Figure 6It can be seen that the similarity between the municipal sewer well water in the industrial zone and the treated industrial wastewater from Company B exhibits a linear relationship with their concentration ratios. Therefore, a linear relationship was established for analysis. The results are as follows: Figure 7 As shown, y = 0.280 + 0.265x (Formula 5), ​​R² = 0.98, where x is the concentration of treated industrial wastewater in the municipal sewer manifold sample, and y is the similarity between the municipal sewer manifold water and the treated industrial wastewater from company B. The three-dimensional fluorescence spectral data of the municipal wastewater sample to be tested are obtained and processed to obtain a one-dimensional vector S. In the Python environment, the spectral angle mapping function spectral_angle_mapper(x, y) is called. Based on the spectral angle mapping algorithm, the spectral vector S to be tested and the treated industrial wastewater as a reference spectral vector are calculated. The cosine angle between them, through the function Calculate the similarity index y, substitute it into Formula 5, and calculate the concentration of treated industrial wastewater in the municipal pipe network manifold water sample of the industrial area to be tested.

[0021] Depend on Figure 6 It can be seen that there is a linear relationship between the similarity and concentration ratio between the municipal pipeline runoff well water in the industrial zone and the inspection well water sample from plant B. Therefore, a linear relationship was established for analysis. The results are as follows: Figure 7 As shown, y = 0.447 + 0.064x (Formula 6), R² = 0.92, where x is the concentration of treated industrial wastewater in the municipal sewer manifold water sample, and y is the similarity between the municipal sewer manifold water sample and the factory inspection well water sample. Based on the spectral angle mapping algorithm, the spectral vector S to be measured and the fluorescence vector of the wastewater from the factory inspection well are calculated as reference spectral vectors. The cosine angle between them, through the function Calculate the similarity index y, substitute it into Formula 6, and calculate the concentration of manhole wastewater in the municipal pipe network runoff sample of the industrial area to be tested. For example, when the similarity is 0.5, the concentration ratio of the treated industrial wastewater is 83.02%, and the concentration ratio of the manhole wastewater is 82.81%.

[0022] From a practical application perspective, the three-dimensional fluorescence spectral angle algorithm can not only be used for qualitative identification of the problem of illegal discharge or leakage of high-concentration industrial wastewater, but also for quantitative estimation of the mixing ratio of treated industrial wastewater and sewage from inspection wells. Therefore, this method can assess the proportion of industrial wastewater mixed into municipal pipe networks, providing technical support for pollution source tracing and quantitative supervision.

[0023] The foregoing description is not intended to limit the invention, nor is the invention limited to the examples given. Any changes, modifications, additions, or substitutions made by those skilled in the art within the scope of the invention should also be considered within the protection scope of the invention.

Claims

1. A method for tracing and identifying the source of industrial wastewater in municipal pipe networks based on a three-dimensional fluorescence spectral angle algorithm, characterized in that... Includes the following steps: Step 1: Collect water samples from the municipal pipe network manifold outside the industrial zone and from the raw industrial wastewater, treated industrial wastewater, and inspection well water of the enterprises within the factory area; mix the municipal pipe network manifold water with the raw industrial wastewater, treated industrial wastewater, and inspection well water of the enterprises within the factory area in a series of different proportions, obtain the three-dimensional fluorescence spectrum data of the water samples of each mixing proportion, and make corrections. Step 2: Read the three-dimensional fluorescence spectral data obtained after correction in Step 1 in the Python environment, flatten the three-dimensional fluorescence spectral data into a one-dimensional vector, and normalize the one-dimensional vector to obtain the one-dimensional vector of each group of mixed water samples. Step 3: Using the one-dimensional vector of each water sample—mixed with industrial wastewater at different proportions from the municipal sewer network's manifold well water—as the test spectral vector, and the industrial wastewater as the reference spectral vector, calculate the cosine angle between the test spectral vector and the reference spectral vector using a spectral angle mapping algorithm. Convert this angle into a spectral similarity index, obtaining the first linear regression function between the spectral similarity value of the municipal sewer network's manifold well water and the concentration of industrial wastewater in the municipal sewer network's manifold well water. Take the slope of the linear regression function as y. Calculate the spectral similarity between the municipal sewer network's manifold well water sample and the industrial wastewater using the spectral angle mapping algorithm. If the absolute value of the difference between the spectral similarity and the slope is less than or equal to 0.05, it is determined that the enterprise's industrial wastewater has entered the municipal sewer network; if the absolute value of the difference between the spectral similarity and the slope is greater than 0.05, it is determined that the municipal sewer network's manifold well water is not mixed with the enterprise's industrial wastewater. Step 4: Take the one-dimensional vector of each water sample, which is a mixture of municipal pipeline well water and treated industrial wastewater at different ratios, as the test spectral vector, and the fluorescence vector of the treated industrial wastewater as the reference spectral vector. Based on the spectral angle mapping algorithm, calculate the cosine angle between the test spectral vector and the reference spectral vector and convert it into a spectral similarity index. Obtain the second linear regression function of the spectral similarity value between the municipal pipeline well water and the treated industrial wastewater and the concentration of the treated industrial wastewater in the municipal pipeline well water. Substitute the spectral similarity value between the municipal pipeline well water and the treated industrial wastewater to be tested into the second linear regression function to obtain the concentration value of the treated industrial wastewater in the municipal pipeline well water of the industrial area. Step 5: Take the one-dimensional vector of each water sample, which is a mixture of municipal pipeline manifold water and enterprise inspection well water in different proportions, as the test spectral vector, and the fluorescence vector of the sewage from the enterprise inspection well as the reference spectral vector. Based on the spectral angle mapping algorithm, calculate the cosine angle between the test spectral vector and the reference spectral vector and convert it into a spectral similarity index. Obtain the third linear regression function of the spectral similarity value between municipal pipeline manifold water and enterprise inspection well water and the concentration of enterprise inspection well water in municipal pipeline manifold water. Substitute the spectral similarity value between the municipal pipeline manifold water and enterprise inspection well water to be tested into the third linear regression function to obtain the concentration value of enterprise inspection well water in municipal pipeline manifold water of the industrial zone.

2. The method for tracing and identifying the source of industrial wastewater in municipal pipe networks based on a three-dimensional fluorescence spectral angle algorithm according to claim 1, characterized in that... The formula for calculating the cosine angle is: ； In the formula, To test the spectral vector, L is the reference spectral vector, and L is the number of spectral vectors.

3. The method for tracing and identifying the source of industrial wastewater in municipal pipe networks based on a three-dimensional fluorescence spectral angle algorithm according to claim 2, characterized in that: The formula for calculating the spectral similarity index is as follows: .

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