Environment pollution online monitoring method and system

Abstract

The invention relates to the technical field of environmental pollution monitoring, in particular to an on-line environmental pollution monitoring method and system.The method comprises the steps that original acoustic signals of the inner side wall and the outer side wall of a curve are collected in real time in a high-risk monitoring area, propagation path compensation is conducted, and an inner side acoustic characteristic parameter set and an outer side acoustic characteristic parameter set are obtained; analyzing to obtain thickness values and adhesion strength values of biological membranes on the inner side and the outer side of the curve; inner and outer side falling risk factors are obtained through calculation, and a comprehensive falling risk value of the inner and outer side falling risk factors is calculated; when the comprehensive shedding risk value exceeds a preset shedding risk threshold value, generating biological membrane shedding risk early warning information; the monitoring problem of spatial distribution heterogeneity of the biological membrane at the pipe network bend is effectively solved, and accurate early warning of the falling risk of the biological membrane in the bend area is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental pollution monitoring, and in particular to an online environmental pollution monitoring method and system. Background Art

[0002] In the field of environmental pollution monitoring, monitoring the biofilm status of pipe network systems is a key step in ensuring water quality safety and healthy pipeline operation. Pipeline bends experience significant changes in fluid dynamics. The curvature of the pipe network structure causes low-velocity areas on the inside of the bend and high-velocity areas on the outside. This velocity gradient causes differences in the spatial distribution of centrifugal and shear forces, resulting in biofilms with uneven thickness and heterogeneous adhesion strength inside and outside the bend. Abnormal biofilm shedding can carry heavy metals or organic pollutants into water bodies, posing a potential threat to the ecological environment and public health.

[0003] However, in the current pipeline monitoring using acoustic sensors, the acoustic signal produces path distortion due to the curvature structure during propagation through the bend, making it difficult to capture the differentiated characteristics inside and outside the bend. As a result, it is impossible to effectively obtain the spatial distribution data of biofilm thickness and adhesion strength inside and outside the bend, and thus it is impossible to accurately analyze the risk of biofilm detachment.

[0004] In response to the above technical bottlenecks, the present invention proposes an online monitoring method for environmental pollution, which effectively solves the problem of monitoring the spatial distribution heterogeneity of biofilms at pipe bends and realizes accurate early warning of the risk of biofilm shedding in the bend area. Summary of the Invention

[0005] (1) Technical problems to be solved The purpose of the present invention is to provide an online monitoring method and system for environmental pollution, so as to solve the problem of inaccurate monitoring of the spatial distribution heterogeneity of biofilms due to fluid dynamics asymmetry at the bends of pipe networks, and the inability to achieve accurate early warning of the risk of biofilm shedding in the bend area.

[0006] (2) Technical solution To achieve the above objectives, the present invention provides a method for online monitoring of environmental pollution, comprising: S1. Identify the curve structure data in the pipeline network based on the pipeline geographic information topology data, obtain the curvature radius and pipe diameter of each curve based on the curve structure data, and mark the curves with a curvature radius less than or equal to a preset multiple of the pipe diameter as high-risk monitoring areas.

[0007] S2. Deploy a monitoring acoustic sensor array in the high-risk monitoring area to collect the original acoustic signals of the inner and outer walls of the curve in real time; perform propagation path compensation on the original acoustic signals according to the curvature radius and sound wave frequency of the high-risk monitoring area to obtain an inner acoustic feature parameter set and an outer acoustic feature parameter set.

[0008] S3. Analyze the inner acoustic characteristic parameter set and the outer acoustic characteristic parameter set to obtain the biofilm thickness value and the inner adhesion strength value of the inner side of the curve and the biofilm thickness value and the outer adhesion strength value of the outer side of the curve.

[0009] S4. Calculate the centrifugal force enhancement coefficient according to the curvature radius of the curve, and calculate the inner side shedding risk factor according to the centrifugal force enhancement coefficient, the inner side biofilm thickness value, and the inner side adhesion strength value; obtain the shear force sensitivity coefficient of the curve and calculate the outer side shedding risk factor in combination with the outer side biofilm thickness value and the outer side adhesion strength value.

[0010] S5. Calculate a comprehensive shedding risk value based on the inner shedding risk factor and the outer shedding risk factor; when the comprehensive shedding risk value exceeds a preset shedding risk threshold, generate biofilm shedding risk warning information.

[0011] Furthermore, the method of performing propagation path compensation on the original acoustic signal according to the curvature radius and the sound wave frequency of the high-risk monitoring area to obtain the inner acoustic feature parameter set and the outer acoustic feature parameter set includes: The bending angle of the curve in the curve structure data of the high-risk monitoring area is obtained, and the equivalent sound path difference between the inner sound wave propagation path and the outer sound wave propagation path is calculated based on the curvature radius, pipe diameter and bending angle of the curve. The amplitude compensation coefficient and time delay correction amount are obtained based on the equivalent sound path difference analysis.

[0012] The original acoustic signal is decomposed into an inner reflected wave signal component and an outer reflected wave signal component according to a direction of arrival estimation method, and corresponding amplitude compensation coefficients and time delay correction amounts are applied to obtain compensated inner reflected wave signal components and outer reflected wave signal components.

[0013] The compensated inner reflected wave signal component and the outer reflected wave signal component are subjected to time-frequency domain feature extraction to obtain an inner acoustic feature parameter set and an outer acoustic feature parameter set.

[0014] Furthermore, the method for obtaining the amplitude compensation coefficient and the time delay correction value according to the equivalent sound path difference analysis includes: Obtain the current pipe type of the pipe network bend corresponding to the high-risk monitoring area, obtain the propagation speed of the sound wave in the pipe wall medium based on the current pipe type, and convert the equivalent acoustic path difference into a time delay correction value based on the propagation speed; The attenuation base value, frequency index, and frequency-dependent attenuation coefficient corresponding to the current pipe type are obtained, and the amplitude compensation coefficient is calculated by combining the equivalent acoustic path difference and the sound wave frequency component; the attenuation base value, frequency index, and frequency-dependent attenuation coefficient are pre-determined through calibration experiments and stored as a mapping table for different pipe types.

[0015] Furthermore, the method of pre-determining the frequency-dependent attenuation coefficient through a calibration experiment includes: A reference acoustic sensor array is deployed upstream of the straight pipe section in the high-risk monitoring area to collect a baseline acoustic signal without bend distortion in real time; the difference in attenuation slope between the original acoustic signal and the baseline acoustic signal at the bend in the high-risk monitoring area is calculated in the same characteristic frequency band; a dynamic correction factor is generated based on the attenuation slope difference, and the frequency-dependent attenuation coefficient is updated based on the dynamic correction factor.

[0016] Furthermore, the method of generating a dynamic correction factor according to the attenuation slope difference and updating the frequency-dependent attenuation coefficient according to the dynamic correction factor includes: The current pipeline environmental parameter set is obtained in real time, including water temperature, fluid turbidity and flow rate; and an environmental impact weight matrix is ​​generated based on the environmental parameter set.

[0017] A dynamic correction factor is calculated based on the attenuation slope difference and the environmental impact weight matrix; the dynamic correction factor is multiplied by a preset learning rate coefficient to obtain a final correction step size; and the final correction step size is superimposed on the current frequency-dependent attenuation coefficient to obtain an updated frequency-dependent attenuation coefficient.

[0018] Furthermore, the method of performing time-frequency domain feature extraction on the compensated inner reflected wave signal component and the outer reflected wave signal component to obtain an inner acoustic feature parameter set and an outer acoustic feature parameter set includes: The compensated inner reflected wave signal component and the outer reflected wave signal component are subjected to short-time Fourier transform to obtain a time-frequency spectrum matrix, and a power spectrum density distribution curve is calculated within a preset characteristic frequency band based on the time-frequency spectrum matrix.

[0019] The acoustic wave reflection coefficient of the biofilm-pipe wall interface is calculated based on the power spectral density distribution curve, and an acoustic impedance spectrum is generated based on the acoustic wave reflection coefficient; within the preset characteristic frequency band of the time-frequency spectrum matrix, the spectrum attenuation curve of each time point is extracted along the frequency axis direction, the envelope line is extracted for the spectrum attenuation curve of each time point, and the slope of the envelope line is fitted by linear regression to generate an attenuation slope parameter; within the preset characteristic frequency band, the resonance peak center frequency of the power spectral density distribution curve is detected, and the resonance peak center frequency is compared with the standard resonance frequency of the same pipe type in the state without biofilm coverage to calculate the resonance frequency shift.

[0020] The acoustic impedance spectrum, the attenuation slope parameter and the resonance frequency shift amount constitute an inner acoustic characteristic parameter set and an outer acoustic characteristic parameter set.

[0021] Furthermore, the method of analyzing the inner acoustic characteristic parameter set and the outer acoustic characteristic parameter set to obtain the inner biofilm thickness value and the inner adhesion strength value of the curve and the outer biofilm thickness value and the outer adhesion strength value of the curve includes: An initial estimate of the biofilm porosity is calculated based on the ratio of the real part to the imaginary part of the acoustic impedance spectrum; the attenuation slope parameter is used to obtain the intermediate value of the biofilm viscoelastic modulus through a pre-stored viscoelastic mapping table; the biofilm density correction factor is calculated based on the resonance frequency shift and the initial estimate of the porosity; the biofilm structural stiffness matrix is ​​generated by coupling the intermediate value of the biofilm viscoelastic modulus and the biofilm density correction factor; and the biofilm thickness value and adhesion strength value are obtained by solving the biofilm structural stiffness matrix.

[0022] Furthermore, the method for obtaining the biofilm thickness value and the adhesion strength value by solving the biofilm structure stiffness matrix includes: The biofilm structural stiffness matrix is ​​decomposed into a symmetric positive definite matrix, and the eigenvector corresponding to its minimum eigenvalue is solved by the inverse power iteration method; the eigenvector is mapped to the initial solution of biofilm thickness and adhesion strength through the dimension conversion coefficient.

[0023] A dimensionless deviation is calculated based on the initial solution of the biofilm thickness and the initial solution of the adhesion strength. When the deviation exceeds a preset deviation threshold, the deviation is introduced as a penalty term into the eigenvalue residual objective function for iteration until the iterative deviation does not exceed the preset deviation threshold and the eigenvalue residual is less than the preset residual threshold. The final biofilm thickness solution and adhesion strength solution are output and recorded as the biofilm thickness value and the adhesion strength value.

[0024] Furthermore, the method of introducing the deviation as a penalty term into the eigenvalue residual objective function for iteration includes: The dimensionless deviation of the current iteration is obtained and multiplied by a preset penalty weight factor to generate a penalty term; and the eigenvalue residual objective function of the current iteration is obtained according to the penalty term and the eigenvalue residual obtained in the previous iteration.

[0025] The eigenvalue residual objective function is solved by a gradient descent algorithm to obtain a new eigenvector; and updated initial solutions of biofilm thickness and adhesion strength are extracted from the new eigenvector for iterative calculation.

[0026] On the other hand, based on the same inventive concept, the present invention also provides an online environmental pollution monitoring system, which includes: a high-risk monitoring area identification module, an acoustic signal acquisition and compensation module, an acoustic characteristic parameter analysis module, a shedding risk factor calculation module, and a shedding risk warning generation module, wherein each module is sequentially connected to each other in communication; The high-risk monitoring area identification module is used to identify the bend structure data in the pipeline network based on the pipeline geographic information topology data, obtain the curvature radius and pipe diameter of each bend based on the said bend structure data, and mark the bends with a curvature radius less than or equal to a preset multiple of the pipe diameter as high-risk monitoring areas.

[0027] The acoustic signal acquisition and compensation module is used to deploy a monitoring acoustic sensor array in the high-risk monitoring area to collect the original acoustic signals of the inner and outer walls of the curve in real time; the propagation path of the original acoustic signal is compensated according to the curvature radius and sound wave frequency of the high-risk monitoring area to obtain the inner acoustic feature parameter set and the outer acoustic feature parameter set.

[0028] The acoustic characteristic parameter analysis module is used to analyze the inner acoustic characteristic parameter set and the outer acoustic characteristic parameter set to obtain the biofilm thickness value and the inner adhesion strength value of the inner side of the curve and the biofilm thickness value and the outer adhesion strength value of the outer side of the curve.

[0029] The shedding risk factor calculation module is used to calculate the centrifugal force enhancement coefficient based on the curvature radius of the curve, and to calculate the inner shedding risk factor based on the centrifugal force enhancement coefficient, the inner biofilm thickness value, and the inner adhesion strength value; and to obtain the shear force sensitivity coefficient of the curve and calculate the outer shedding risk factor in combination with the outer biofilm thickness value and the outer adhesion strength value.

[0030] The shedding risk warning generation module is used to calculate a comprehensive shedding risk value based on the inner shedding risk factor and the outer shedding risk factor; when the comprehensive shedding risk value exceeds a preset shedding risk threshold, a biofilm shedding risk warning message is generated.

[0031] (3) Beneficial effects Compared with the prior art, the present invention has the following beneficial effects: 1. Accurately identify high-risk monitoring areas through pipeline geographic information topology data, deploy an acoustic sensor array to collect raw acoustic signals in real time, and then compensate for the signal propagation path by combining the curvature radius and sound wave frequency to obtain a precise set of inner and outer acoustic characteristic parameters. This enables high-precision and efficient monitoring of the biofilm status on the inner wall of pipe network bends, ensuring that the monitoring targets cover the areas with the highest risk of detachment in the pipe network.

[0032] 2. When obtaining amplitude compensation coefficients and time delay corrections, the impact of different pipe types is considered. Calibration experiments are performed to pre-determine relevant parameters and store them in a mapping table. Dynamic correction factors are generated in real time based on the pipeline environmental parameter set to update the frequency-dependent attenuation coefficient. This allows the monitoring method to adapt to different pipeline environments and pipe material conditions, improving monitoring accuracy and adaptability.

[0033] 3. The biofilm thickness and adhesion strength values ​​are derived from the internal and external acoustic characteristic parameter sets. This is then used to calculate the shedding risk factor by comprehensively considering the centrifugal force enhancement coefficient and shear force sensitivity coefficient. The accuracy of the biofilm parameter analysis is further improved by solving the biofilm structural stiffness matrix and introducing penalty term iteration, enabling more accurate and timely generation of biofilm shedding risk warning information. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a flowchart of an online environmental pollution monitoring method according to Example 1 of the present invention.

[0035] Figure 2 This is a schematic diagram of the module composition of an online environmental pollution monitoring system according to Example 2 of the present invention. DETAILED DESCRIPTION

[0036] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0037] Before giving examples, it is necessary to explain the application scenarios of the present invention. The present invention is an online monitoring method and system for environmental pollution, which is applied to pipe bends where the fluid dynamics characteristics change significantly, resulting in biofilms with uneven thickness and heterogeneous adhesion strength on the inside and outside of the bends. Abnormal shedding of biofilms can cause heavy metals or organic pollutants to enter the water body, posing a potential threat to the ecological environment and public health.

[0038] Example 1: Figure 1 As shown, this embodiment provides an online environmental pollution monitoring method, the method comprising: S1. Identify the bend structure data in the pipeline network based on the pipeline geographic information topology data, obtain the curvature radius and pipe diameter of each bend based on the bend structure data, and mark the bends with a curvature radius less than or equal to a preset multiple of the pipe diameter as high-risk monitoring areas; due to their special geometric structure, pipeline bends will cause significant changes in the water flow dynamics characteristics, forming high-risk areas for biofilm shedding. By retrieving the pipeline geographic information topology data, all bend structures in the entire pipeline network are automatically identified. The bend structure data records in detail the three-dimensional coordinates, pipe diameter specifications, and bending parameters of each bend. For each identified bend, the curvature radius and the corresponding pipe diameter parameters are extracted. The smaller the curvature radius, the more obvious the flow velocity gradient and centrifugal force effect at the bend, the greater the difference in the distribution of the biofilm on the inner and outer walls, and the higher the risk of shedding. The preset multiples are set through experiments or simulations.

[0039] S2. Deploy an acoustic sensor array in the high-risk monitoring area to collect raw acoustic signals from the inner and outer walls of the bend in real time. These raw acoustic signals are then path-compensated based on the curvature radius and acoustic frequency of the high-risk monitoring area to generate sets of inner and outer acoustic characteristic parameters. The inner wall of a curved pipe is divided into inner and outer walls based on its characteristics: the inner wall is closer to the center of curvature, and the outer wall is farther from the center of curvature. The acoustic sensor array is distributed in a circular pattern around the pipe, capable of collecting acoustic signals from both the inner and outer walls of the bend. The advantages of acoustic monitoring technology lie in its non-invasiveness and real-time nature, enabling continuous monitoring of changes in the biofilm status of the pipe wall without disrupting normal pipeline operation. The acoustic frequency refers to the frequency of the sound waves actively emitted by the sensor array. Its selection must take into account the pipe material, diameter, fluid medium, and biofilm characteristics. Sound waves of different frequencies experience varying degrees of attenuation during propagation. The selection of the acoustic frequency is verified through acoustic simulation or experimentation to ensure adequate penetration and reflection sensitivity.

[0040] S3. Analyze the inner acoustic characteristic parameter set and the outer acoustic characteristic parameter set to obtain the biofilm thickness value and the inner adhesion strength value of the inner side of the curve and the biofilm thickness value and the outer adhesion strength value of the outer side of the curve.

[0041] S4. Calculate the centrifugal force enhancement coefficient based on the radius of curvature of the curve, and calculate the inner side shedding risk factor based on the centrifugal force enhancement coefficient, the inner side biofilm thickness value, and the inner side adhesion strength value; obtain the shear force sensitivity coefficient of the curve and calculate the outer side shedding risk factor in combination with the outer side biofilm thickness value and the outer side adhesion strength value; the centrifugal force enhancement coefficient reflects the degree of enhancement of the risk of shedding of the biofilm on the inner side of the curve by centrifugal force, and evaluates the shedding risk factor under the action of centrifugal force in combination with the thickness and adhesion strength parameters of the inner side biofilm. Although the flow rate on the inner side of the curve is lower, the centrifugal force effect is more obvious, and the biofilm has the risk of shedding under the action of centrifugal force. Taking into account the centrifugal force enhancement coefficient, biofilm thickness, and adhesion strength, this risk can be more accurately assessed. For example, the greater the centrifugal force enhancement coefficient, and the thicker the inner biofilm and the lower the adhesion strength, the higher the inner side shedding risk factor will be, indicating that the inner biofilm is more likely to fall off under the action of centrifugal force. For the outside of the bend, due to the high flow velocity and concentrated shear force, the shear force sensitivity coefficient is a parameter related to the characteristics of the bend itself, reflecting the sensitivity of the outside of the bend to the shear force. Bends with different curvature radii, pipe diameters and other conditions have different shear force sensitivity coefficients.

[0042] S5. A comprehensive shedding risk value is calculated based on the inner shedding risk factor and the outer shedding risk factor; when the comprehensive shedding risk value exceeds the preset shedding risk threshold, a biofilm shedding risk warning message is generated. The comprehensive shedding risk value is the maximum value or weighted sum of the inner shedding risk factor and the outer shedding risk factor; the biofilm shedding risk warning message is used to notify operation and maintenance personnel to take appropriate preventive or treatment measures, thereby avoiding potential pollution of water quality caused by large-scale shedding of biofilm. The preset shedding risk threshold is a critical value determined by simulating the shedding behavior of biofilm under different working conditions (including curve curvature radius, flow rate, water quality, biofilm type, etc.) in the laboratory and actual pipeline system, collecting a large amount of experimental data and using statistical analysis methods (such as ROC curve, cluster analysis). The preset shedding risk threshold is usually taken as the risk value when the biofilm begins to shed significantly at a confidence level ≥95%, and a certain safety margin can be set based on actual engineering experience.

[0043] The method of performing propagation path compensation on the original acoustic signal according to the curvature radius and the sound wave frequency of the high-risk monitoring area to obtain the inner acoustic feature parameter set and the outer acoustic feature parameter set includes: The bend angle of the curve in the high-risk monitoring area's curve structure data is obtained. The equivalent acoustic path difference between the inner and outer sound wave propagation paths is calculated based on the curve's curvature radius, pipe diameter, and bend angle. This equivalent acoustic path difference is then analyzed to derive the amplitude compensation coefficient and time delay correction. In a curved structure, the actual propagation path of the sound wave from the emission point to the receiving point is no longer a straight line, but instead propagates along the curved surface of the pipe wall. This path change causes signal amplitude attenuation and time delay. The bend angle determines the actual length difference between the sound wave propagation paths inside and outside the curve. Combined with the known curvature radius and pipe diameter, the equivalent acoustic path difference between the inner and outer sound wave propagation paths can be accurately calculated.

[0044] The original acoustic signal is decomposed into an inner reflected wave signal component and an outer reflected wave signal component based on a direction of arrival estimation method. Corresponding amplitude compensation coefficients and delay corrections are applied to obtain the compensated inner and outer reflected wave signal components. Advanced direction of arrival estimation technology is used for the spatial decomposition of the original acoustic signal. Due to the circular arrangement of the acoustic sensor array, the phase difference and arrival time difference of the received signals from each sensor are analyzed to accurately distinguish the reflected wave components from the inner and outer sides of the curve. Signal components with clear spatial directivity can be isolated in complex acoustic environments. Compensation requires applying corresponding amplitude amplification and delay correction to the inner and outer signal components, respectively. Amplitude compensation primarily addresses energy loss caused by the propagation path, ensuring that the inner and outer signals have comparable energy benchmarks. Delay correction ensures the synchronization of the inner and outer signals on the time axis, eliminating the effects of time offsets caused by geometric path differences.

[0045] The compensated inner reflected wave signal component and the outer reflected wave signal component are subjected to time-frequency domain feature extraction to obtain an inner acoustic feature parameter set and an outer acoustic feature parameter set.

[0046] The method for obtaining the amplitude compensation coefficient and the time delay correction amount according to the equivalent sound path difference analysis includes: The current pipe material type for the corresponding pipe network bend in the high-risk monitoring area is obtained. Based on this current pipe material type, the sound wave propagation velocity in the pipe wall medium is determined. Based on this propagation velocity, the equivalent acoustic path difference is converted into a time delay correction. The time delay correction is linearly positively correlated with the equivalent acoustic path difference. Common pipe material types include ductile iron, steel, and plastic pipes. Each material has its own specific sound wave propagation velocity, attenuation characteristics, and frequency response characteristics. Accurately determining the sound wave propagation velocity is crucial for calculating the time delay correction, as even small velocity errors can accumulate into significant time deviations over long propagation distances. Based on the determined sound wave propagation velocity in the pipe wall medium, the equivalent acoustic path difference can be used to calculate the additional time delay due to the extended actual propagation path, i.e., the time delay correction. A larger equivalent acoustic path difference indicates a longer actual sound wave propagation path, resulting in a larger time delay correction. The two are linearly positively correlated.

[0047] Obtain the attenuation base value, frequency index, and frequency-dependent attenuation coefficient corresponding to the current pipe type, and calculate the amplitude compensation coefficient by combining the equivalent acoustic path difference and the sound wave frequency component; the attenuation base value, frequency index, and frequency-dependent attenuation coefficient are pre-determined through calibration experiments and stored as a mapping table for different pipe types. Amplitude compensation coefficient The calculation formula is: ;in, is the equivalent acoustic path difference, is the frequency component of the sound wave, is the attenuation base value corresponding to the current pipe type, is the frequency index corresponding to the current pipe type, It is the frequency-dependent attenuation coefficient corresponding to the current pipe type. The sound wave frequency component refers to the frequency components obtained by spectral analysis of the collected original acoustic signal. Since the attenuation characteristics of sound waves of different frequencies are different when propagating in the pipe, it is necessary to consider the attenuation of each sound wave frequency component separately. The calculation of the amplitude compensation coefficient adopts a more complex exponential attenuation model, which includes three key parameters: the attenuation base value reflects the basic attenuation characteristics of the pipe type, the frequency-dependent attenuation coefficient reflects the selective attenuation of the pipe type to sound waves of different frequencies, and the frequency index describes the nonlinear characteristics of this frequency dependence. The establishment of the mapping table is a systematic calibration process. Through a large number of calibration experiments in the laboratory and the actual site, the acoustic parameters of different pipes under various conditions are measured and stored in the system in the form of a database to ensure that the corresponding material parameters can be called quickly and accurately during the monitoring process.

[0048] The method of pre-determining the frequency-dependent attenuation coefficient through a calibration experiment includes: A reference acoustic sensor array is deployed upstream of the straight pipe section in the high-risk monitoring area to collect a real-time baseline acoustic signal free of bend distortion. The attenuation slope difference between the original acoustic signal and the baseline acoustic signal at the bend in the high-risk monitoring area is calculated within the same characteristic frequency band. A dynamic correction factor is generated based on this attenuation slope difference, and the frequency-dependent attenuation coefficient is updated based on this dynamic correction factor. The deployment strategy of the reference acoustic sensor array is crucial. Installed in the straight pipe section upstream of the monitoring bend, it ensures a standardized and controllable measurement environment. Sound wave propagation in the straight pipe section is unaffected by bend geometric distortion, providing ideal baseline measurement data. The reference acoustic sensor array and the monitoring acoustic sensor array utilize the same technical configuration to ensure comparability of measurement results. Analysis of the baseline acoustic signal provides a standard reference for attenuation characteristics. Sound wave propagation in the straight pipe section is a standard straight line, and the attenuation behavior follows classical acoustic theory. The baseline attenuation slope spectrum represents the acoustic attenuation characteristics of the pipe material type under ideal conditions. Comparative analysis of measured acoustic signals at bends with baseline acoustic signals reveals the actual impact of bend geometry on acoustic propagation. The calculation of attenuation slope differences not only considers differences in average attenuation levels but also analyzes variations in attenuation characteristics across different frequency bands.

[0049] The method of generating a dynamic correction factor according to the attenuation slope difference and updating the frequency-dependent attenuation coefficient according to the dynamic correction factor includes: The system acquires a set of current pipeline environmental parameters, including water temperature, fluid turbidity, and flow rate, in real time. An environmental impact weight matrix is ​​generated based on this set of environmental parameters. The water temperature weight factor is positively correlated with the absolute value of the temperature change, the turbidity weight factor is linearly positively correlated with the turbidity value, and the flow rate weight factor is exponentially correlated with the percentage of flow rate deviation from the design value. Water temperature monitoring reflects the impact of temperature changes on acoustic propagation characteristics. Temperature changes directly affect the sound velocity and attenuation characteristics of the pipe wall material, as well as the acoustic properties of the fluid within the pipe. The positive correlation between the water temperature weight factor and the magnitude of the temperature change ensures the system's sensitivity to temperature effects. When the water temperature deviates significantly from the design value, the weight of the temperature factor in the parameter correction increases accordingly. Fluid turbidity monitoring reflects the impact of water quality on the acoustic environment. Increased turbidity indicates a higher concentration of suspended particles in the water, which scatters and absorbs sound waves. The linear positive correlation of the turbidity weight factor reflects the direct and cumulative nature of this effect. Acoustic signal analysis in high turbidity environments requires greater correction. Flow rate monitoring is a key indicator for assessing changes in shear environment and dynamic conditions. Flow rate deviations from the design value not only affect the growth and shedding characteristics of the biofilm, but also change the propagation conditions of sound waves in the flowing medium. The flow rate weighting factor is designed using an exponential relationship, reflecting the nonlinear characteristics of the impact of flow rate changes on the acoustic environment. Significant changes in flow rate will cause the weighting factor to grow rapidly, ensuring the system's sensitive response to changes in hydraulic conditions. The construction of the environmental impact weight matrix adopts a multi-parameter fusion strategy, taking into account the interactions and coupling effects between them through matrix operations.

[0050] A dynamic correction factor is calculated based on the attenuation slope difference and the environmental impact weight matrix; the dynamic correction factor is multiplied by a preset learning rate coefficient to obtain a final correction step size; and the final correction step size is added to the current frequency-dependent attenuation coefficient to obtain an updated frequency-dependent attenuation coefficient. A matrix operation, such as weighted summation or matrix multiplication, is performed on the attenuation slope difference and the environmental impact weight matrix to obtain the dynamic correction factor. The dynamic correction factor integrates the effects of the curve structure and the environmental disturbance. The learning rate coefficient is used to control the magnitude of the correction step size. The correction magnitude is scaled by the learning rate coefficient to ensure the stability and convergence of the correction process. A smaller learning rate coefficient can avoid drastic parameter fluctuations, while a moderate adjustment amplitude ensures timely response to environmental changes. The preset learning rate coefficient is used to control the update step size of the frequency-dependent attenuation coefficient and generally ranges from 0.01 to 0.3. By selecting a pilot area at an actual pipeline monitoring site and setting different learning rate coefficients for real-time monitoring, the accuracy and stability of the monitoring results under different learning rate coefficients are compared. The optimal learning rate coefficient is selected as the preset learning rate coefficient based on the actual monitoring data.

[0051] The method of extracting time-frequency domain features of the compensated inner reflected wave signal component and the outer reflected wave signal component to obtain an inner acoustic feature parameter set and an outer acoustic feature parameter set includes: A short-time Fourier transform (SFT) is performed on the compensated inner and outer reflected wave signal components to generate a time-frequency spectrum matrix. Based on this matrix, a power spectral density (PSD) distribution curve is calculated within a preset characteristic frequency band. Time-frequency domain feature extraction is a key step in converting the compensated acoustic signal into characteristic data useful for biofilm parameter analysis. SFT technology maintains both temporal and frequency resolution, which is particularly important for analyzing non-stationary biofilm acoustic signals. Changes in biofilm state often manifest as temporal evolution of signal characteristics, and simple frequency or time domain analysis cannot fully capture these dynamic characteristics. The SFT matrix provides a rich information foundation for subsequent feature parameter extraction. The preset characteristic frequency band automatically determines the most effective analysis frequency band based on the fundamental frequency and harmonic components of the transmitted sound wave, ensuring that the feature extraction process focuses on the frequency range with the highest information content. The PSD distribution curve provides information on the distribution of acoustic energy in the frequency domain, reflecting the extent of the biofilm's impact on sound waves of different frequencies. Accurate calculation of the PSD requires consideration of the signal's statistical characteristics and background noise, and the reliability of the results is ensured by using a spectral estimation algorithm.

[0052] The acoustic reflection coefficient at the biofilm-wall interface is calculated based on the power spectral density distribution curve, and an acoustic impedance spectrum is generated based on the acoustic reflection coefficient. Within the preset characteristic frequency band of the time-frequency spectrum matrix, the spectrum attenuation curve is extracted at each time point along the frequency axis. The envelope of the spectrum attenuation curve at each time point is extracted, and the slope of the envelope is fitted using linear regression to generate an attenuation slope parameter. The center frequency of the resonance peak of the power spectral density distribution curve is detected within the preset characteristic frequency band, and the resonance frequency shift is calculated by comparing the center frequency of the resonance peak with the standard resonance frequency of the same pipe type without biofilm coverage. Differences in the acoustic properties of the biofilm-wall interface cause partial reflection of sound waves, and the magnitude of the acoustic reflection coefficient is directly related to the physical properties of the biofilm. By comparing the power spectrum differences between the presence and absence of biofilm, the reflection coefficient can be accurately calculated, thereby generating an acoustic impedance spectrum that describes the acoustic properties of the interface. The attenuation slope parameter is extracted using a multi-time point statistical analysis method. The spectrum attenuation curve is continuously extracted throughout the monitoring period. Mathematical methods such as envelope fitting and linear regression are used to obtain a stable and reliable attenuation slope parameter. This time-averaging process effectively suppresses the impact of transient noise and improves the accuracy of parameter estimation. The resonance frequency shift measurement is based on the mechanism by which biofilm affects the resonance characteristics of the pipe wall. Biofilm adhesion alters the effective mass and stiffness of the pipe wall, causing a shift in the resonance frequency. By accurately measuring this shift and comparing it with a standard resonance frequency, the presence of biofilm and its extent can be confirmed. Resonance frequency shift detection technology is highly sensitive and can detect even trace amounts of biofilm.

[0053] The acoustic impedance spectrum, attenuation slope parameter and resonance frequency shift amount constitute an inner acoustic characteristic parameter set and an outer acoustic characteristic parameter set; the preset characteristic frequency band range is dynamically set according to the fundamental frequency and harmonic components of the transmitted sound wave.

[0054] The method of analyzing the inner acoustic characteristic parameter set and the outer acoustic characteristic parameter set to obtain the inner biofilm thickness value and the inner adhesion strength value of the curve and the outer biofilm thickness value and the outer adhesion strength value of the curve comprises: An initial estimate of biofilm porosity is calculated based on the ratio of the real and imaginary parts of the acoustic impedance spectrum. The attenuation slope parameter is applied to a pre-stored viscoelasticity mapping table to obtain the intermediate value of the biofilm viscoelastic modulus. A biofilm density correction factor is calculated based on the resonance frequency shift and the initial porosity estimate. The biofilm structural stiffness matrix is ​​generated by coupling the intermediate value of the biofilm viscoelastic modulus with the biofilm density correction factor. The pre-stored viscoelasticity mapping table is established through calibration experiments on biofilm samples with different rheological properties. Biofilm thickness and adhesion strength values ​​are obtained by solving the biofilm structural stiffness matrix. The analysis of biofilm physical parameters is used to convert acoustic characteristic data into physically meaningful values ​​for biofilm thickness and adhesion strength. The initial estimate of biofilm porosity utilizes the ratio of the real and imaginary parts of the acoustic impedance spectrum, which reflects the microstructural characteristics of the biofilm, particularly the relative proportion of pores to the solid matrix. Biofilm porosity is a key parameter affecting the acoustic properties of biofilms, influencing not only the propagation speed of sound waves but also the scattering and absorption characteristics of acoustic energy. The porosity estimate obtained from acoustic measurements provides important constraints for subsequent complex analyses. The development of the pre-stored viscoelastic mapping table involves a systematic experimental calibration process. Biofilm samples with varying rheological properties are incubated to determine the relationship between their acoustic attenuation characteristics and mechanical properties. These experiments cover the full spectrum of biofilms, from initially attached, thin biofilms to mature, thick biofilms, and encompass a wide range of biofilm types formed under diverse microbial compositions and environmental conditions. The accuracy of the mapping table directly impacts the reliability of parameter analysis. The calculation of the biofilm density correction factor embodies the principle of multi-parameter coupled analysis. Both the resonance frequency shift and the initial porosity estimate contain density-related information, but they reflect density characteristics from different physical perspectives. By coupling these two independent measurements, a more accurate density correction factor can be obtained, eliminating the systematic errors that may be introduced by single-parameter analysis. Generating the biofilm structural stiffness matrix is ​​a key step in converting acoustic measurement results into a mechanical description. The biofilm structural stiffness matrix integrates the viscoelastic properties, density distribution, and microstructural information of the biofilm, mathematically describing its response to various external forces.

[0055] The method for obtaining the biofilm thickness value and the adhesion strength value by solving the biofilm structure stiffness matrix includes: The biofilm structural stiffness matrix is ​​decomposed into a symmetric positive definite matrix, and the eigenvector corresponding to its minimum eigenvalue is solved using the inverse power iteration method. These eigenvectors are mapped to initial solutions for biofilm thickness and adhesion strength using dimensional conversion coefficients. Advanced numerical analysis techniques are employed in the numerical solution of the biofilm structural stiffness matrix to ensure the stability and convergence of the solution. The symmetric positive definite decomposition of the biofilm structural stiffness matrix is ​​a crucial step in ensuring the stability of numerical calculations. The biofilm structural stiffness matrix possesses the mathematical property of symmetry and positive definiteness, reflecting the fundamental physical characteristics of biofilm materials. Using mathematical methods such as Cholesky decomposition, the original matrix is ​​converted into a form more suitable for numerical calculation, avoiding the numerical instability issues that may arise from direct solution. The application of the inverse power iteration method reflects the need for a precise solution of the minimum eigenvalue and its corresponding eigenvector. The minimum eigenvalue corresponds to the weakest response mode of the biofilm structure, which is closely related to the biofilm shedding mechanism. Through iterative calculations, the true eigenvalue and eigenvector can be approximated with arbitrary precision. The application of dimensional conversion coefficients resolves the correspondence between the mathematical solution and the physical parameters. The components of the eigenvectors have abstract mathematical meanings and need to be mapped into thickness and adhesion strength values ​​with clear physical meanings using appropriate dimensional conversion coefficients. These conversion coefficients are determined based on extensive experimental calibration and theoretical analysis to ensure the accuracy of the conversion process.

[0056] A dimensionless deviation is calculated based on the initial solution for biofilm thickness and the initial solution for adhesion strength. When the deviation exceeds a preset deviation threshold, the deviation is introduced as a penalty term into the eigenvalue residual objective function and iterated until the iterative deviation does not exceed the preset deviation threshold and the eigenvalue residual is less than the preset residual threshold. The final biofilm thickness solution and adhesion strength solution are then output and recorded as the biofilm thickness value and adhesion strength value. Because biofilm parameter analysis involves complex nonlinear relationships, the initial solution often requires iterative optimization to improve its accuracy. The deviation calculation provides a quality assessment standard for the solution. When the deviation exceeds a preset deviation threshold, the iterative optimization procedure is initiated. The preset deviation threshold is used to judge the rationality of the initial solution for biofilm parameters and is usually taken as the 95th percentile of the calibration deviation in historical data or experiments. The preset deviation threshold reflects the allowable error range for biofilm parameter analysis under normal monitoring conditions and can be determined through a large number of calibration experiments, for example, 0.05 to 0.15. The preset residual threshold is used to control the accuracy of the eigenvalue iterative solution. It is usually set based on numerical stability and computing resources, with a value of 1e-2 to 1e-4. It is necessary to ensure the accuracy of biofilm parameter analysis while avoiding excessive iterations that lead to a decrease in computing efficiency.

[0057] The method of introducing the deviation as a penalty term into the eigenvalue residual objective function for iteration includes: The dimensionless deviation of the current iteration is obtained and multiplied by a preset penalty weight factor to generate a penalty term; the eigenvalue residual objective function of the current iteration is obtained based on the penalty term and the eigenvalue residual obtained in the previous iteration; the calculation and weight setting of the penalty term need to find a balance between mathematical convergence and physical constraints. The preset penalty weight factor is used to introduce a deviation constraint into the eigenvalue residual objective function. The value range is 0.1 to 1.0. The specific value can be determined by cross-validation or L-curve method to achieve a balance between goodness of fit and constraint satisfaction. A penalty weight factor that is too small may cause the physical constraint to not work effectively, while a penalty weight factor that is too large may cause difficulties in numerical calculation. The construction of the eigenvalue residual objective function comprehensively considers the dual requirements of mathematical solution accuracy and physical parameter constraints. By minimizing the eigenvalue residual objective function, the solution requirements of the mathematical equation and the rationality constraints of the physical parameters can be met simultaneously.

[0058] The eigenvalue residual objective function is solved using a gradient descent algorithm to obtain a new eigenvector. From this new eigenvector, the updated initial solutions for biofilm thickness and adhesion strength are extracted and iteratively calculated. The application of the gradient descent algorithm provides an effective approach to objective function optimization. The gradient calculation reflects the sensitivity of the eigenvalue residual objective function to various variables, guiding the search direction of the iterative process. Not only does the deviation measure meet the accuracy requirements, but the changing trend of the eigenvalue residual is also monitored. When multiple convergence indicators meet the requirements simultaneously, the iterative process is considered to have converged, and the final optimization result is output.

[0059] Example 2: Based on the same inventive concept, Figure 2 As shown, this embodiment also provides an online environmental pollution monitoring system, which includes: a high-risk monitoring area identification module, an acoustic signal acquisition and compensation module, an acoustic characteristic parameter analysis module, a shedding risk factor calculation module, and a shedding risk warning generation module, and each module is sequentially connected to each other; The high-risk monitoring area identification module is used to identify the bend structure data in the pipeline network based on the pipeline geographic information topology data, obtain the curvature radius and pipe diameter of each bend based on the said bend structure data, and mark the bends with a curvature radius less than or equal to a preset multiple of the pipe diameter as high-risk monitoring areas.

[0060] The acoustic signal acquisition and compensation module is used to deploy a monitoring acoustic sensor array in the high-risk monitoring area to collect the original acoustic signals of the inner and outer walls of the curve in real time; the propagation path of the original acoustic signal is compensated according to the curvature radius and sound wave frequency of the high-risk monitoring area to obtain the inner acoustic feature parameter set and the outer acoustic feature parameter set.

[0061] The acoustic characteristic parameter analysis module is used to analyze the inner acoustic characteristic parameter set and the outer acoustic characteristic parameter set to obtain the biofilm thickness value and the inner adhesion strength value of the inner side of the curve and the biofilm thickness value and the outer adhesion strength value of the outer side of the curve.

[0062] The shedding risk factor calculation module is used to calculate the centrifugal force enhancement coefficient based on the curvature radius of the curve, and to calculate the inner shedding risk factor based on the centrifugal force enhancement coefficient, the inner biofilm thickness value, and the inner adhesion strength value; and to obtain the shear force sensitivity coefficient of the curve and calculate the outer shedding risk factor in combination with the outer biofilm thickness value and the outer adhesion strength value.

[0063] The shedding risk warning generation module is used to calculate a comprehensive shedding risk value based on the inner shedding risk factor and the outer shedding risk factor; when the comprehensive shedding risk value exceeds a preset shedding risk threshold, a biofilm shedding risk warning message is generated.

[0064] It should be noted that, regarding the system in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0065] Finally, it should be noted that although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments, or make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for online monitoring of environmental pollution, characterized in that: The method comprises: Identify the curve structure data in the pipeline network based on the pipeline geographic information topology data, obtain the curvature radius and pipe diameter of each curve based on the curve structure data, and mark the curves with a curvature radius less than or equal to a preset multiple of the pipe diameter as high-risk monitoring areas; Deploying a monitoring acoustic sensor array in the high-risk monitoring area to collect original acoustic signals from the inner and outer walls of the curve in real time; performing propagation path compensation on the original acoustic signals based on the curvature radius and sound wave frequency of the high-risk monitoring area to obtain an inner acoustic feature parameter set and an outer acoustic feature parameter set; Analyzing the inner acoustic characteristic parameter set and the outer acoustic characteristic parameter set to obtain a biofilm thickness value and an inner adhesion strength value on the inner side of the curve and a biofilm thickness value and an outer adhesion strength value on the outer side of the curve; Calculating the centrifugal force enhancement coefficient based on the curvature radius of the curve, and calculating the inner side shedding risk factor based on the centrifugal force enhancement coefficient, the inner side biofilm thickness value, and the inner side adhesion strength value; obtaining the shear force sensitivity coefficient of the curve and calculating the outer side shedding risk factor based on the outer side biofilm thickness value and the outer side adhesion strength value; A comprehensive shedding risk value is calculated based on the inner shedding risk factor and the outer shedding risk factor; when the comprehensive shedding risk value exceeds a preset shedding risk threshold, biofilm shedding risk warning information is generated.

2. The method for online monitoring of environmental pollution according to claim 1, characterized in that: The method of performing propagation path compensation on the original acoustic signal according to the curvature radius and the sound wave frequency of the high-risk monitoring area to obtain the inner acoustic feature parameter set and the outer acoustic feature parameter set includes: Obtain the bending angle of the curve in the curve structure data of the high-risk monitoring area, calculate the equivalent sound path difference between the inner and outer sound wave propagation paths based on the curvature radius, pipe diameter and bending angle of the curve, and obtain the amplitude compensation coefficient and time delay correction value based on the equivalent sound path difference analysis; Decomposing the original acoustic signal into an inner reflected wave signal component and an outer reflected wave signal component according to a direction of arrival estimation method and applying corresponding amplitude compensation coefficients and time delay correction amounts to obtain compensated inner reflected wave signal components and outer reflected wave signal components; The compensated inner reflected wave signal component and the outer reflected wave signal component are subjected to time-frequency domain feature extraction to obtain an inner acoustic feature parameter set and an outer acoustic feature parameter set.

3. The method for online monitoring of environmental pollution according to claim 2, characterized in that: The method for obtaining the amplitude compensation coefficient and the time delay correction amount according to the equivalent sound path difference analysis includes: Obtain the current pipe type of the pipe network bend corresponding to the high-risk monitoring area, obtain the propagation speed of the sound wave in the pipe wall medium based on the current pipe type, and convert the equivalent acoustic path difference into a time delay correction value based on the propagation speed; The attenuation base value, frequency index, and frequency-dependent attenuation coefficient corresponding to the current pipe type are obtained, and the amplitude compensation coefficient is calculated by combining the equivalent acoustic path difference and the sound wave frequency component; the attenuation base value, frequency index, and frequency-dependent attenuation coefficient are pre-determined through calibration experiments and stored as a mapping table for different pipe types.

4. The method for online monitoring of environmental pollution according to claim 3, characterized in that: The method of pre-determining the frequency-dependent attenuation coefficient through a calibration experiment includes: A reference acoustic sensor array is deployed upstream of the straight pipe section in the high-risk monitoring area to collect a baseline acoustic signal without bend distortion in real time; the difference in attenuation slope between the original acoustic signal and the baseline acoustic signal at the bend in the high-risk monitoring area is calculated in the same characteristic frequency band; a dynamic correction factor is generated based on the attenuation slope difference, and the frequency-dependent attenuation coefficient is updated based on the dynamic correction factor.

5. The method for online monitoring of environmental pollution according to claim 4, characterized in that: The method of generating a dynamic correction factor according to the attenuation slope difference and updating the frequency-dependent attenuation coefficient according to the dynamic correction factor includes: Acquire the current pipeline environmental parameter set in real time, including water temperature, fluid turbidity and flow rate; generate an environmental impact weight matrix based on the environmental parameter set; A dynamic correction factor is calculated based on the attenuation slope difference and the environmental impact weight matrix; the dynamic correction factor is multiplied by a preset learning rate coefficient to obtain a final correction step size; and the final correction step size is superimposed on the current frequency-dependent attenuation coefficient to obtain an updated frequency-dependent attenuation coefficient.

6. The method for online monitoring of environmental pollution according to claim 2, characterized in that: The method of extracting time-frequency domain features of the compensated inner reflected wave signal component and the outer reflected wave signal component to obtain an inner acoustic feature parameter set and an outer acoustic feature parameter set includes: A time-frequency spectrum matrix is ​​obtained by performing short-time Fourier transform on the compensated inner reflected wave signal component and the outer reflected wave signal component, and a power spectrum density distribution curve is calculated within a preset characteristic frequency band according to the time-frequency spectrum matrix; The acoustic wave reflection coefficient of the biofilm-pipe wall interface is calculated based on the power spectrum density distribution curve, and an acoustic impedance spectrum is generated based on the acoustic wave reflection coefficient; within the preset characteristic frequency band of the time-frequency spectrum matrix, a spectrum attenuation curve is extracted along the frequency axis at each time point, an envelope is extracted from the spectrum attenuation curve at each time point, and an attenuation slope parameter is generated by linear regression fitting the slope of the envelope; within the preset characteristic frequency band, the resonance peak center frequency of the power spectrum density distribution curve is detected, and the resonance peak center frequency is compared with a standard resonance frequency of the same pipe type in a state without biofilm coverage to calculate the resonance frequency shift; The acoustic impedance spectrum, the attenuation slope parameter and the resonance frequency shift amount constitute an inner acoustic characteristic parameter set and an outer acoustic characteristic parameter set.

7. The method for online monitoring of environmental pollution according to claim 6, characterized in that: The method of analyzing the inner acoustic characteristic parameter set and the outer acoustic characteristic parameter set to obtain the inner biofilm thickness value and the inner adhesion strength value of the curve and the outer biofilm thickness value and the outer adhesion strength value of the curve comprises: An initial estimate of the biofilm porosity is calculated based on the ratio of the real part to the imaginary part of the acoustic impedance spectrum; the attenuation slope parameter is used to obtain the intermediate value of the biofilm viscoelastic modulus through a pre-stored viscoelastic mapping table; the biofilm density correction factor is calculated based on the resonance frequency shift and the initial estimate of the porosity; the biofilm structural stiffness matrix is ​​generated by coupling the intermediate value of the biofilm viscoelastic modulus and the biofilm density correction factor; and the biofilm thickness value and adhesion strength value are obtained by solving the biofilm structural stiffness matrix.

8. The method for online monitoring of environmental pollution according to claim 7, characterized in that: The method for obtaining the biofilm thickness value and the adhesion strength value by solving the biofilm structure stiffness matrix includes: The biofilm structural stiffness matrix is ​​decomposed into a symmetric positive definite matrix, and the eigenvector corresponding to the minimum eigenvalue thereof is solved by the inverse power iteration method; the eigenvector is mapped into an initial solution of biofilm thickness and an initial solution of adhesion strength through a dimension conversion coefficient; A dimensionless deviation is calculated based on the initial solution of the biofilm thickness and the initial solution of the adhesion strength. When the deviation exceeds a preset deviation threshold, the deviation is introduced as a penalty term into the eigenvalue residual objective function for iteration until the iterative deviation does not exceed the preset deviation threshold and the eigenvalue residual is less than the preset residual threshold. The final biofilm thickness solution and adhesion strength solution are output and recorded as the biofilm thickness value and the adhesion strength value.

9. The method for online monitoring of environmental pollution according to claim 8, characterized in that: The method of introducing the deviation as a penalty term into the eigenvalue residual objective function for iteration includes: Obtain the dimensionless deviation of the current iteration and multiply it by a preset penalty weight factor to generate a penalty term; obtain the eigenvalue residual objective function of the current iteration based on the penalty term and the eigenvalue residual obtained in the previous iteration; The eigenvalue residual objective function is solved by a gradient descent algorithm to obtain a new eigenvector; and updated initial solutions of biofilm thickness and adhesion strength are extracted from the new eigenvector for iterative calculation.

10. An online environmental pollution monitoring system, characterized in that: The system includes: a high-risk monitoring area identification module, an acoustic signal acquisition and compensation module, an acoustic characteristic parameter analysis module, a shedding risk factor calculation module, and a shedding risk warning generation module, and each module is sequentially connected to each other; A high-risk monitoring area identification module is used to identify the curve structure data in the pipeline network based on the pipeline geographic information topology data, obtain the curvature radius and pipe diameter of each curve based on the curve structure data, and mark the curves with a curvature radius less than or equal to a preset multiple of the pipe diameter as high-risk monitoring areas; An acoustic signal acquisition and compensation module is configured to deploy an acoustic sensor array in the high-risk monitoring area to collect raw acoustic signals from the inner and outer walls of the curve in real time; and to perform propagation path compensation on the raw acoustic signals based on the curvature radius and sound wave frequency of the high-risk monitoring area to obtain a set of inner and outer acoustic characteristic parameters. an acoustic characteristic parameter parsing module, configured to parse the inner acoustic characteristic parameter set and the outer acoustic characteristic parameter set to obtain a biofilm thickness value and an inner adhesion strength value on the inner side of the curve and a biofilm thickness value and an outer adhesion strength value on the outer side of the curve; A shedding risk factor calculation module is used to calculate the centrifugal force enhancement coefficient based on the curvature radius of the curve, and to calculate the inner shedding risk factor based on the centrifugal force enhancement coefficient, the inner biofilm thickness value, and the inner adhesion strength value; and to obtain the shear force sensitivity coefficient of the curve and calculate the outer shedding risk factor in combination with the outer biofilm thickness value and the outer adhesion strength value; The shedding risk warning generation module is used to calculate a comprehensive shedding risk value based on the inner shedding risk factor and the outer shedding risk factor; when the comprehensive shedding risk value exceeds a preset shedding risk threshold, a biofilm shedding risk warning message is generated.

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