River pollutant quantitative tracing method and device based on water quality fingerprint and server
By employing dual-spectral synchronous sampling and environmental adaptive correction techniques, combined with constrained optimization algorithms, rapid identification and contribution rate quantification of river pollutants were achieved, solving the timeliness and accuracy problems of traditional source tracing methods and providing high-precision pollutant source tracing results.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- POWERCHINA HUADONG ENG CORP LTD
- Filing Date
- 2026-03-20
- Publication Date
- 2026-06-09
Smart Images
Figure CN121884150B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the technical field of the intersection of environmental monitoring and water pollution control, and in particular to a method, device and server for quantitative tracing of river pollutants based on water quality fingerprinting. Background Technology
[0002] Currently, quantitative source tracing of river pollutants is the core technological support for environmental governance and the scientific basis for precise pollution control solutions. Related technologies suggest that mobile mass spectrometers can be used to quickly scan pollutants and analyze their concentrations. However, the above-mentioned solutions are easily affected by environmental noise, resulting in low accuracy in pollutant source tracing, insufficient timeliness, and long analysis time, which may lead to missing the golden window for handling sudden pollutants. Summary of the Invention
[0003] In view of this, the purpose of the present invention is to provide a method, device and server for quantitative source tracing of river pollutants based on water quality fingerprints, which can significantly improve the timeliness and accuracy of pollutant source tracing.
[0004] In a first aspect, embodiments of the present invention provide a method for quantitative source tracing of river pollutants based on water quality fingerprints. The method includes: performing dual-spectral synchronous sampling processing on the water body to be tested to obtain a spectral image, and constructing an initial water quality fingerprint based on the target quantitative features in the spectral image; constructing a weight function through the product of flow velocity and concentration gradient terms, and performing real-time correction processing on the weight function using an environmental adjustment coefficient to obtain a target weight function, and combining the target weight function with the initial water quality fingerprint to obtain a target water quality fingerprint; matching the target water quality fingerprint with a preset fingerprint database to obtain spectral similarity, and performing analytical processing on the target water quality fingerprint that has passed the spectral similarity verification through a dual physical constraint optimization model and Monte Carlo uncertainty quantification to obtain the pollutant source tracing result.
[0005] In one embodiment, the step of performing dual-spectral synchronous sampling processing on the water body to be tested to obtain a spectral image includes: performing dual-spectral synchronous sampling processing on the water body to be tested using a xenon lamp pulse light source and a dual-grating monochromator to obtain the ultraviolet absorption spectrum and the three-dimensional fluorescence spectrum of the water body to be tested.
[0006] In one embodiment, the step of constructing an initial water quality fingerprint based on target quantization features in a spectral image includes: performing turbidity compensation processing and temperature drift suppression processing on the ultraviolet absorption spectrum and the three-dimensional fluorescence spectrum through a two-dimensional real-time compensation mechanism to obtain a corrected spectrum; extracting the target quantization features in the corrected spectrum, and determining the vector constructed from the extracted target quantization features as the initial water quality fingerprint, wherein the target quantization features include: ultraviolet absorption features, absorption slope features, fluorescence features, and corrosion index.
[0007] In one implementation, the step of using environmental adjustment coefficients to perform real-time correction processing on the weighting function to obtain the target weighting function includes: performing real-time calculation processing on turbidity compensation for ultraviolet attenuation, rainstorm retention of agricultural signals, flow velocity enhancement of surface source characteristics, and pH interference suppression based on real-time measurement information of the current environment to obtain the environmental adjustment coefficients at the current time; and multiplying the environmental adjustment coefficients at the current time with the weighting function dimension by dimension to obtain the target weighting function.
[0008] In one implementation, after the step of combining the target weight function with the initial water quality fingerprint to obtain the target water quality fingerprint, the method includes: collecting real-time hydrological parameters and concatenating the real-time hydrological parameters with the enhanced feature matrix corresponding to the target water quality fingerprint to obtain the target input water quality fingerprint, wherein the real-time hydrological parameters include: flow velocity, water temperature and pH.
[0009] In one implementation, the step of analyzing the target water quality fingerprint that has passed the spectral similarity verification to obtain the pollutant source tracing result by using a dual physical constraint optimization model and Monte Carlo uncertainty quantization includes: inputting the target water quality fingerprint with a spectral similarity greater than a preset similarity threshold as a candidate source, and sending the candidate source to the dual physical constraint optimization model for analysis to obtain the pollutant source tracing result; and performing Monte Carlo uncertainty quantization on the pollutant source tracing result to obtain the target pollutant source tracing result.
[0010] In one implementation, the step of sending candidate sources to a dual physical constraint optimization model for analytical processing to obtain pollutant source tracing results includes: sending candidate sources to the dual physical constraint optimization model for non-negative constraint processing and normalization constraint processing respectively, obtaining the contribution percentage of each pollution source to the current pollution event in the water body to be tested, and determining the contribution percentage as the pollutant source tracing result.
[0011] Secondly, embodiments of the present invention also provide a quantitative source tracing device for river pollutants based on water quality fingerprints. The device includes: a dual-spectrum synchronous sampling module, which performs dual-spectrum synchronous sampling processing on the water body to be tested to obtain a spectral image, and constructs an initial water quality fingerprint based on the target quantitative features in the spectral image; a weight optimization module, which constructs a weight function through the product of flow velocity and concentration gradient, and performs real-time correction processing on the weight function using an environmental adjustment coefficient to obtain a target weight function, so as to combine the target weight function with the initial water quality fingerprint to obtain a target water quality fingerprint; and a pollutant source tracing module, which matches the target water quality fingerprint with a preset fingerprint database to obtain spectral similarity, and performs analytical processing on the target water quality fingerprint that has passed the spectral similarity verification through a dual physical constraint optimization model and Monte Carlo uncertainty quantification to obtain the pollutant source tracing result.
[0012] Thirdly, embodiments of the present invention also provide a server, including a processor and a memory, the memory storing computer-executable instructions executable by the processor, the processor executing the computer-executable instructions to implement any of the methods provided in the first aspect.
[0013] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing computer-executable instructions, which, when invoked and executed by a processor, cause the processor to implement any of the methods provided in the first aspect.
[0014] The embodiments of the present invention bring the following beneficial effects:
[0015] This invention provides a method, device, and server for quantitative source tracing of river pollutants based on water quality fingerprints. The method involves simultaneous dual-spectral sampling of the water body to be tested to obtain a spectral image. Based on the target quantification features in the spectral image, an initial water quality fingerprint is constructed. Then, a weighting function is constructed using the product of flow velocity and concentration gradients, and the weighting function is corrected in real time using an environmental adjustment coefficient to obtain a target weighting function. This target weighting function is then combined with the initial water quality fingerprint to obtain the target water quality fingerprint. Finally, the target water quality fingerprint is matched with a preset fingerprint database to obtain spectral similarity. The target water quality fingerprint that passes the spectral similarity verification is analyzed using a dual physical constraint optimization model and Monte Carlo uncertainty quantification to obtain the pollutant source tracing result. This invention can achieve rapid identification and contribution rate quantification of pollution sources by fusing ultraviolet, visible absorption spectroscopy, and three-dimensional fluorescence spectroscopy, combined with a constraint optimization analysis algorithm. This solves the technical bottlenecks of traditional source tracing methods, such as poor timeliness, weak anti-interference, and insufficient accuracy of multi-source mixed analysis.
[0016] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.
[0017] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description
[0018] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.
[0019] Figure 1 A schematic diagram of an excitation system provided in an embodiment of the present invention;
[0020] Figure 2 A flowchart illustrating a quantitative source tracing method for river pollutants based on water quality fingerprinting, provided as an embodiment of the present invention;
[0021] Figure 3 A flowchart illustrating a time-division multiplexing control method provided in an embodiment of the present invention;
[0022] Figure 4 A schematic diagram illustrating the specific process of a quantitative source tracing method for river pollutants based on water quality fingerprinting, provided in an embodiment of the present invention;
[0023] Figure 5 A schematic diagram of a quantitative source tracing device for river pollutants based on water quality fingerprinting provided in an embodiment of the present invention;
[0024] Figure 6 This is a schematic diagram of the structure of a server provided in an embodiment of the present invention. Detailed Implementation
[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0026] Currently, quantitative source tracing of river pollutants is a core technological support for environmental governance and a scientific basis for precise pollution control solutions. With the development of smart water management, this technology is driving water quality monitoring from concentration monitoring to source apportionment, becoming a rigid requirement for environmental supervision. Current technology has achieved two major breakthroughs: at the methodological level, three-dimensional fluorescence (EEMs) and ultraviolet spectroscopy (UV-Vis) can analyze dissolved organic matter components, and combined with multivariate statistical models such as PMF and PCA, the source tracing accuracy in industrial areas reaches 70%; at the equipment level, spectral sensors (such as s::can) enable online COD or ammonia nitrogen monitoring, and mobile mass spectrometers support rapid pollutant scanning. However, existing equipment is mostly limited to concentration monitoring, mobile platforms are costly, and an integrated capability for identification, source tracing, and early warning has not yet been formed. Current technologies mainly face the following bottlenecks:
[0027] (1) In terms of accuracy, traditional algorithms are affected by environmental noise, and the contribution rate error is greater than 15%, which makes it difficult to meet the requirements of law enforcement evidence; the lack of timeliness means that laboratory analysis takes 6-24 hours, missing the golden window for handling sudden pollution (less than 2 hours).
[0028] (2) Limited adaptability, manifested in signal attenuation of more than 50% in high turbidity water (greater than 100 NTU), and difficulty in handling dynamic mixed pollution caused by rainfall, limiting application in complex scenarios.
[0029] Based on this, the present invention provides a method, device and server for quantitative source tracing of river pollutants based on water quality fingerprints. By integrating ultraviolet and visible absorption spectroscopy with three-dimensional fluorescence spectroscopy and combining constrained optimization analysis algorithms, it can achieve rapid identification of pollution sources and quantification of contribution rates. This method is mainly applied to emergency response to sudden river pollution incidents, watershed pollution source analysis and environmental supervision and law enforcement. It can solve the technical bottlenecks of traditional source tracing methods, such as poor timeliness, weak anti-interference and insufficient accuracy of multi-source mixed analysis.
[0030] To facilitate understanding of this embodiment, a detailed description of a quantitative source tracing method for river pollutants based on water quality fingerprinting, as disclosed in this embodiment, is provided first. This method is applied to an excitation system. To facilitate understanding of the excitation system, this embodiment provides a schematic diagram of the excitation system's structure, as shown below. Figure 1 As shown, the excitation system consists of a xenon lamp pulsed light source and a dual-grating monochromator. Its excitation wavelength is adjustable in the range of 200 nm to 550 nm. Through the dual-optical-path coaxial design, it achieves the synchronous acquisition of ultraviolet absorption spectrum (200-800 nm, 0.5 nm resolution) and three-dimensional fluorescence spectrum (excitation spectrum Ex: 200–550 nm, emission spectrum Em: 250-650 nm).
[0031] The excitation system also includes a dual-spectrum acquisition module, an environmental compensation unit, and an embedded processor. The dual-spectrum acquisition module is used to simultaneously acquire ultraviolet absorption spectra (200-800 nm, 0.5 nm resolution) and three-dimensional fluorescence spectra (Ex 200-550 nm / Em 250-650 nm) through a dual-optical-path coaxial design, with an acquisition interval of less than 0.1 seconds. The environmental compensation unit is used to integrate an 850 nm near-infrared channel to correct turbidity interference and a PT1000 temperature sensor to suppress drift. The embedded processor is used to execute dynamic weight calculation and constraint optimization algorithms in real time.
[0032] The system described above achieves microsecond-level dual-spectrum synchronous acquisition through a xenon lamp pulse light source and a dual-optical-path coaxial design, eliminating interference from transient changes in water bodies. At the same time, it improves signal stability in complex environments by utilizing near-infrared turbidity compensation and temperature drift suppression mechanisms. It extracts four types of quantitative indicators, including absorption features and fluorescence features, as the basis for source tracing. Furthermore, it introduces microsecond-level time-division multiplexing technology, which can make the acquisition interval of absorption signals or fluorescence signals less than 0.1 seconds, avoiding interference from transient changes in water bodies in traditional time-division detection.
[0033] based on Figure 1 The schematic diagram of the excitation system shown in this invention provides a detailed description of the quantitative source tracing method for river pollutants based on water quality fingerprinting. (See also...) Figure 2 The diagram shows a flow chart of a quantitative source tracing method for river pollutants based on water quality fingerprinting. The method mainly includes the following steps S202 to S206:
[0034] Step S202: Perform dual-spectral synchronous sampling processing on the water body to be tested to obtain a spectral image, and construct an initial water quality fingerprint based on the target quantification features in the spectral image.
[0035] In one implementation, a dual-spectral synchronous sampling process can be performed on the water sample using a xenon lamp pulsed light source and a dual-grating monochromator to obtain the ultraviolet absorption spectrum and three-dimensional fluorescence spectrum of the water sample. The specific steps are as follows: 1. Initial emission of the xenon lamp: The xenon lamp emits a 200–550 nm broadband ultraviolet pulse light (peak intensity at 254 nm); 2. First-stage grating beam splitting: Outputs monochromatic light in the selected wavelength band (e.g., 254 nm ± 5 nm); 3. Beam splitter optical path allocation: Ultraviolet absorption requires high light intensity to ensure a high signal-to-noise ratio; transmission (80%) is used for ultraviolet absorption detection; fluorescence excitation has a long path, so less energy is allocated, and reflection (20%) is used for fluorescence excitation; 4. Ultraviolet absorption optical path detection: Monochromatic light penetrates the sample cell (10 mm optical path quartz cuvette); contaminants absorb photons, causing attenuation of transmitted light intensity; a photodiode array detects the transmitted light intensity and calculates the absorbance; 5. Fluorescence excitation optical path fine-tuning: The core function of the second grating is primarily secondary purification, compressing the bandwidth from ±5 nm to ±0.25 nm (resolution 0.5). nm); simultaneously ensuring fluorescence excitation and eliminating interference from adjacent wavelengths (e.g., 250 nm photoexcitation of tryptophan to avoid stray light interference from 245 / 255 nm). The sample is excited by monochromatic light (250-650 nm) to emit fluorescence, and a photomultiplier tube array is used to acquire the fluorescence in wavelength segments (5 nm resolution per channel).
[0036] See Figure 3The diagram shows a specific process flow of a quantitative source tracing method for river pollutants based on water quality fingerprinting. Traditional spectroscopic equipment requires switching optical paths to collect absorption and fluorescence spectra separately (with an interval of more than 1 second), which leads to errors caused by transient changes in the water body. However, this invention achieves alternating acquisition of dual spectra in the same optical path and the same water sample through precise timing control.
[0037] Specifically, during the excitation phase (0-5μs): the xenon lamp emits an ultraviolet pulse (λ=254 nm), triggering absorption spectroscopy detection; the detector records the intensity of transmitted light and calculates the absorbance. During the delay phase (5-10 μs): the absorption detection channel is turned off to eliminate afterglow interference from the light source; during the emission phase (10-60 μs): broadband excitation is switched to (Ex=200-550 nm), and the PMT array acquires the fluorescence signal. .
[0038] In summary, the dual-spectrum microsecond-level synchronous acquisition technology, employing a xenon lamp pulsed light source and a dual-optical-path coaxial design, combined with microsecond-level time-division multiplexing (acquisition interval less than 0.1 seconds), achieves simultaneous acquisition of ultraviolet absorption spectra (200-800 nm) and three-dimensional fluorescence spectra (Ex 200-550 nm / Em 250-650 nm), thus solving the characteristic distortion problem caused by transient changes in water bodies.
[0039] Step S204: Construct a weighting function using the product of flow velocity and concentration gradient, and perform real-time correction on the weighting function using an environmental adjustment coefficient to obtain a target weighting function. Combine the target weighting function with the initial water quality fingerprint to obtain the target water quality fingerprint.
[0040] In one implementation, the environment-adaptive dynamic weighting mechanism can be based on the velocity-concentration gradient coupling term (v·). C) The dynamic weighting function corrects the feature matrix in real time through environmental adjustment coefficients: turbidity compensates for ultraviolet attenuation, rainstorms preserve agricultural signals, flow velocity enhances surface source features, and pH interference is suppressed, thereby improving the accuracy of tracing the source of rainstorms with high turbidity.
[0041] Step S206: Match the target water quality fingerprint with the preset fingerprint database to obtain spectral similarity. Then, through a dual physical constraint optimization model and Monte Carlo uncertainty quantification, analyze the target water quality fingerprint that has passed the spectral similarity verification to obtain the pollutant source tracing result.
[0042] In one implementation, the spectral signals of pollutants in a river are essentially a linear superposition of the characteristics of each pollution source. The key lies in accurately quantifying the contribution ratio of each pollution source. Traditional methods (such as positive definite matrix factorization (PMF)) suffer from nonlinear superposition of mixed pollutant spectra, leading to least squares solution distortion. Furthermore, the lack of consideration for physical constraints such as nonnegativity and normalization often results in negative contribution rates or nonphysical solutions with a total exceeding 100%. Therefore, this invention proposes a constraint optimization model that integrates physical rules and environmental dynamics. Its mathematical essence is solving the spectral mixing equation. When water containing multiple pollutants flows through a monitoring point, the detected ultraviolet-fluorescence spectrum M is:
[0043]
[0044] in, The measured spectral matrix, (Including dynamic weighted features, m=6); To contaminate the fingerprint database, (n is the number of sources), such as the strong absorption peak of chemical plant wastewater at 278 nm and the protein-like fluorescence peak of domestic sewage at 280 / 350 nm, which are stored in the fingerprint database after laboratory calibration. For contribution rate vectors, That is, the proportion of the total pollution source to the total pollution; To observe noise, such as turbidity scattering and temperature drift.
[0045]
[0046] Compared to the traditional least squares method, this invention introduces dual constraints to ensure the reasonableness of the tracing results:
[0047] (1) Nonnegativity constraint: To ensure that the pollution contribution rate is physically reasonable;
[0048] (2) Normalization constraint: The overall contribution rate is 100%.
[0049] In summary, the dual-constraint optimization-Monte Carlo quantization model, by incorporating nonnegativity constraints ( ≥0) and normalization constraint ( The optimized algorithm solves the problem of negative contribution rates or exceeding 100%; combined with the Monte Carlo method (1000 times ±5% noise perturbation) to generate a 95% confidence interval error band, the analysis error of multi-source mixed pollution is compressed to ±5%, and the output results meet the requirements of judicial evidence.
[0050] The quantitative source tracing method for river pollutants based on water quality fingerprinting provided in this invention integrates ultraviolet-visible absorption spectroscopy and three-dimensional fluorescence spectroscopy, combined with a constrained optimization analytical algorithm, to solve the problems of poor timeliness, weak anti-interference, and insufficient analytical accuracy of multi-source mixed analysis in traditional source tracing methods. In addition, an environmentally adaptive dynamic weighting mechanism is introduced, which constructs a weighting function based on the product of flow velocity and concentration gradient, and corrects the feature matrix in real time through environmental adjustment coefficients. This compensates for high turbidity attenuation, preserves agricultural signals, enhances non-point source characteristics, and suppresses pH interference, generating a source tracing input matrix that is adaptable to complex scenarios such as rainstorms and high turbidity.
[0051] In the pollution source contribution quantification stage, candidate sources with high matching degree are screened by spectral similarity, and the contribution rate is solved by a dual-constraint optimization model. The uncertainty is quantified by combining the Monte Carlo method, and the multi-source mixed analysis error is compressed to within ±5%, ensuring that the results meet the requirements of judicial evidence. This realizes an integrated process from water quality fingerprint collection and feature extraction to pollution source quantification. It can quickly identify the source of sudden pollution and quantify the contribution rate of industrial, agricultural and domestic pollution sources. It provides core technical support for emergency response to sudden river pollution, precise pollution control in watersheds and environmental supervision and law enforcement, and promotes the upgrade of water quality monitoring from concentration monitoring to source analysis.
[0052] See Figure 4 The diagram shows a specific process flow of a quantitative source tracing method for river pollutants based on water quality fingerprinting. This invention also provides an implementation method for pollutant source tracing, as detailed in (1) to (3) below:
[0053] (1) Multidimensional water quality fingerprinting: For complex water environments in the field, a two-dimensional real-time compensation mechanism is established: Through the two-dimensional real-time compensation mechanism, turbidity compensation and temperature drift suppression are performed on the ultraviolet absorption spectrum and the three-dimensional fluorescence spectrum to obtain the corrected spectrum. Then, the target quantitative features in the corrected spectrum are extracted and processed, and the vector constructed by the extracted target quantitative features is determined as the initial water quality fingerprint. The target quantitative features include: ultraviolet absorption features, absorption slope features, fluorescence features and corrosion index.
[0054] In one implementation, turbidity compensation includes: adding an 850 nm near-infrared reference channel, according to the formula... Absorbance was corrected to ensure that the characteristic peak fluctuation in 300 NTU high-turbidity water was less than 5%. To correct the absorbance, The original absorbance measurement value is given, and α is the turbidity-absorbance conversion coefficient. The intensity of the scattered light from the 850 nm near-infrared channel is used as a reference signal to characterize the level of turbidity.
[0055] The turbidity conversion coefficient α was determined through a standard turbidity gradient experiment: First, a kaolin suspension (0~500 NTU, calibrated with a turbidity meter) was prepared, and the raw absorbance value at the target wavelength (e.g., 278 nm) was measured simultaneously. Scattering intensity with 850 nm near-infrared channel Based on the principle that turbidity interference is linearly related to scattered light intensity, according to the formula... calculate.
[0056] In another implementation, temperature drift suppression includes: integrating a PT1000 sensor, via... Calibrate fluorescence intensity (β= (0.8% / ℃), controlling the signal drift within ±2.5% in the 10~30℃ temperature range. Among them, The corrected fluorescence intensity, This is the original fluorescence intensity measurement value. This is the temperature compensation coefficient. This is the measured temperature.
[0057] In addition, four types of quantization features are extracted based on the corrected spectrum, including:
[0058] Ultraviolet absorption characteristics and absorption slope characteristics: characteristic absorbance values and slope indices at 254 nm and 280 nm (Absorbance slope in the 275–295 nm band), where, The absorbance at a wavelength of 295 nm. The absorbance at a wavelength of 275 nm is a slope that is inversely proportional to the molecular weight of the dissolved organic matter, and can therefore be used to characterize the molecular weight of the organic matter.
[0059] Fluorescence characteristics: Intensity ratio of protein-like substances to humic substances:
[0060]
[0061] in Excitation at 280 nm and emission at 350 nm fluorescence intensity; Fluorescence intensity at 350 nm excitation and 450 nm emission.
[0062] Humus Index:
[0063]
[0064] in, Fluorescence intensity integral in the emission wavelength range of 435–480 nm (humic region), and fluorescence intensity integral in the emission wavelength range of 300–345 nm (non-humic region).
[0065] (2) Adaptive feature extraction: Based on the real-time measurement information of the current environment, the turbidity compensation for ultraviolet attenuation, the retention of agricultural signals in rainstorms, the enhancement of surface source features by flow velocity and the suppression of pH interference are calculated in real time to obtain the environmental adjustment coefficient at the current time. Then, the environmental adjustment coefficient at the current time is multiplied by the weight function dimension by dimension to obtain the target weight function. In one embodiment, real-time hydrological parameters are collected and the real-time hydrological parameters are spliced with the enhanced feature matrix corresponding to the target water quality fingerprint to obtain the target input water quality fingerprint. The real-time hydrological parameters include flow velocity, water temperature and pH.
[0066] Specifically, based on the four types of quantitative features collected from water quality fingerprints, a basic feature matrix (i.e., the initial water quality fingerprint) is constructed. for:
[0067]
[0068] Traditional source tracing models, due to their use of fixed-weight feature matrices, exhibit significant errors under complex hydrological conditions. This invention addresses this by real-time acquisition of flow velocity (v) and pollutant concentration gradient (…). Environmental parameters such as pH and water temperature (T) are used to construct a dynamic weighting function. .
[0069] The dynamic weighting function automatically weights key peaks based on flow rate and pH (e.g., increasing the weight of protein-like peaks during heavy rain), and is compatible with simultaneous analysis of more than three types of pollution sources, providing robust input for subsequent constrained optimization algorithms. A flow rate-concentration gradient product term is introduced (…). As a weighted control variable, it physically represents the rate of change of pollutant flux per unit time. Using the sigmoid function, the weighting function can be defined as:
[0070]
[0071] Where v is the water flow velocity; For pollutant concentration gradient; This is the velocity-gradient coupling coefficient; To activate the threshold offset; pH sensitivity coefficient; Interpolate the current pH value from the reference pH value, i.e. ; , , Calculations were performed by fitting historical data.
[0072] Based on different quantization characteristics, the weighting functions are further differentiated, as shown in Table 1 below:
[0073] Table 1. Weighting Function Table
[0074]
[0075] After fusing the weight vectors, the enhanced feature matrix (i.e., the target water quality fingerprint) is considered based on the dynamic weights. (Overcoming the interference of complex environmental characteristics in the field) is as follows:
[0076]
[0077] Further combining hydrological parameters, the source tracing model achieves environmental adaptation. The source tracing input matrix (i.e., the target input water quality fingerprint) is as follows:
[0078]
[0079] Due to the complex and dynamic characteristics of water pollution monitoring, traditional static weighting can lead to feature distortion under extreme conditions such as heavy rain and high turbidity: ultraviolet absorption is attenuated by sediment scattering, fluorescence signals are diluted by rainfall, while non-point source pollution becomes more prominent with increased flow velocity. Without dynamic compensation for environmental interference, the source of pollution will be severely misjudged. By using dynamic correction with environmental adjustment coefficients—including turbidity compensation for optical attenuation (a1), preservation of agricultural characteristics during heavy rain (a2), flow velocity enhancement of non-point source signals (a3), and pH interference suppression (a4)—the accuracy of source tracing is improved, solving the problem of decoupling environmental noise from actual pollution signals.
[0080] Turbidity-compensated optical attenuation (a1): When river water becomes turbid (due to increased sediment), ultraviolet light is blocked by suspended particles, causing the sensor to measure pollutant absorption values that are lower than the actual values. For example, in a laboratory setting, adding sediment to a water sample with known pollutant concentrations revealed that a turbidity of 50 NTU was measured at a 5% underestimation; a turbidity of 100 NTU was measured at a 12% underestimation; and a turbidity of 200 NTU was measured at a 38% underestimation. Therefore, based on the experimental data, a1 = 0.8 + 0.04 × (turbidity / 100).
[0081] During periods of heavy rainfall, agricultural characteristics (a2) are preserved: Theoretically, the weight of this characteristic should be reduced due to high turbidity (because of measurement inaccuracies). However, analysis of historical heavy rainfall events shows that reducing the weight would lead to missed detection of pesticide pollution in farmland. Therefore, during periods of heavy rainfall, some measurement error is accepted, and agricultural pollution signals are preserved, with a2 maintained at 1.0.
[0082] Velocity enhances area source signal (a3): Heavy rain washes organic matter from pig farms and septic tanks into rivers. These pollutants contain a large amount of protein, causing a sudden increase in their fluorescence characteristics (fluorescence ratio R). Heavy rain also resuspends pollutants (animal feces, domestic sewage) deposited in the riverbed; the greater the flow velocity, the greater the release. Experimental data shows that for every 1 m / s increase in flow velocity, the fluorescence ratio signal intensity increases by an average of about 10%. Therefore, a3 = 1.0 + 0.1 × flow velocity.
[0083] pH interference suppression (a4): Theoretically, the pH of water affects humic matter measurements as follows: acidic conditions (pH less than 7) lead to lower measured values, while alkaline conditions (pH greater than 7) lead to higher measured values. However, experimental results show that during heavy rainfall, pH fluctuates drastically (pH 5.8-7.5). After theoretical compensation, the results become even more unstable because the rainwater introduces soil colloids, microorganisms, and other interfering substances, disrupting the applicable conditions of the laboratory calibration model. Therefore, based on multiple sets of heavy rainfall data, it was found that leaving a4 unadjusted is closer to the actual pollution source than adjusting it; maintaining a4 at 1.0 is recommended.
[0084] based on The source input matrix is adjusted as follows:
[0085]
[0086] (3) Pollution source contribution quantification: The source contribution rate is solved by a constrained optimization model. Targets with spectral similarity greater than a preset similarity threshold are input into the water quality fingerprint and identified as candidate sources. The candidate sources are then sent to a dual physical constraint optimization model for analysis to obtain the pollutant source tracing results. Monte Carlo uncertainty quantification is then performed on the pollutant source tracing results to obtain the target pollutant source tracing results. In one implementation, the candidate sources can be sent to the dual physical constraint optimization model for non-negative constraint processing and normalization constraint processing respectively to obtain the contribution percentage of each pollution source to the current pollution event in the water body to be tested. The contribution percentage is then determined as the pollutant source tracing result. For details, see below:
[0087] Step 1: Calculate the measured spectrum Spectral similarity (correlation coefficient) with fingerprint database:
[0088]
[0089] Candidate sources with a match degree greater than 0.8 with the current pollution characteristics are selected. For example, when an absorption peak of 278 nm and fluorescence peaks of 280 / 350 nm are detected, farmland sources with only humic characteristics are automatically excluded, while chemical plants and domestic sewage sources are retained.
[0090] Step 2: Constraint optimization to solve for the contribution rate. The objective is to minimize the measured spectrum. Mixed spectra with theoretical The differences, while satisfying physical constraints:
[0091] (1) Starting from the initial assumptions (such as the average contribution rate of each source), gradually adjust along the descent direction of the objective function. Value. Let the initial value of the contribution rate be... (Uniform distribution); Calculate the gradient of the objective function Thus, the residuals are determined. The direction of fastest descent; move the step size along the negative gradient direction. Iterative calculation and will Project onto the feasible region, scale proportionally until the sum equals 1, satisfying the condition. , .
[0092] If satisfied Stop iteration and output the percentage of each pollution source. .
[0093] Step 3: Monte Carlo uncertainty quantification. Add ±5% random noise to the measured spectrum M to generate 1000 sets of perturbation data. For each group Contribution rate of repeated solutions Determine the final pollution percentage: The error range is expressed as twice the standard deviation (i.e., the 95% confidence interval). .
[0094] In practical applications, if a river experiences mixed pollution after a rainstorm, and the detected flow velocity is v=2.1m / s, the turbidity is 150 NTU, and the pollutant concentration increases by approximately 0.6 mg / L per minute (as a pollutant concentration gradient),... (Input values), pollutant source tracing analysis needs to be carried out to implement governance responsibilities. See (1) to (6) below for details:
[0095] (1) Data acquisition. The UV absorption A(254) detected by the spectral probe was 0.95 (normal value 0.2), and the UV absorption peak (254 nm) was abnormally high; the fluorescence intensity ratio, (Normal value 1.0), indicating strong protein fluorescence (indicating aquaculture wastewater). Humus index HIX = 3.2.
[0096] (2) Calculate the environmental adjustment coefficient. Based on the high turbidity environment caused by heavy rain, the increased turbidity necessitates a reduction in the ultraviolet absorption weight to enhance the signal's anti-interference capability. Therefore, the ultraviolet absorption adjustment coefficient... The scouring flow velocity increases non-point source pollution, necessitating an increase in the fluorescence ratio weight to enhance the proportion of non-point source pollution. Therefore, the fluorescence ratio adjustment coefficient... ;the remaining .
[0097] (3) Calculate the basic weights. Analyze the velocity-gradient coupling coefficient by fitting historical data. Offset from activation threshold Among them, the basic weight k1=1.10, b1=0.62 (UV absorption); basic weights k2=0.95, b2=0.70 (absorption slope); basic weights k3=1.25, b3=0.58 (fluorescence ratio); basic weights k4=0.15, b4=0.80 (humification index).
[0098] Input parameter v = 2.1 m / s, ,product The basic weights are calculated as follows:
[0099]
[0100] (4) According to the final weight calculation formula Calculate the final weights (i.e., the target weight function). See Table 2 below for details:
[0101] Table 2. Final Weight Table
[0102]
[0103] (5) Construct the source matrix:
[0104]
[0105] (6) Pollution source analysis. By matching with the fingerprint database, the feature vector of chemical plant A is [0.98, 0.008, 1.1, 1.9], with a matching degree of 0.91; the feature vector of pig farm B is [0.35, 0.025, 3.0, 2.8], with a matching degree of 0.96; and the feature vector of farmland runoff is [0.20, 0.030, 0.6, 5.2], with a matching degree of 0.45.
[0106] Furthermore, the contribution rate of each pollution source was calculated. The contribution rate of chemical plant A was 32.5% ± 2.8%, the contribution rate of pig farm B was 58.7% ± 3.2%, and the contribution rate of farmland runoff was 8.8% ± 5.6%.
[0107] In summary, this invention can solve the signal distortion problem in high turbidity (300 NTU fluctuation less than 5%) and temperature change (±2.5% drift) scenarios by using microsecond-level ultraviolet and fluorescence dual-spectral synchronous acquisition technology combined with near-infrared turbidity compensation and real-time temperature calibration mechanisms. Furthermore, by introducing a flow rate and concentration gradient coupled weighting function (v· (C) can achieve adaptive enhancement of pollution characteristics under complex hydrological conditions such as rainstorms and tides.
[0108] Furthermore, this invention employs a dual physical constraint optimization algorithm (nonnegativity and normalization) and Monte Carlo uncertainty quantification to compress the analytical error of the contribution rate of multi-source mixed pollution to within ±5%, and the output results have judicial-grade credibility, providing core technical support for emergency response to sudden pollution and precise pollution control.
[0109] Regarding the quantitative source tracing method for river pollutants based on water quality fingerprinting provided in the foregoing embodiments, this invention provides a quantitative source tracing device for river pollutants based on water quality fingerprinting, see [link to relevant documentation]. Figure 5 The diagram shows a structural schematic of a river pollutant quantitative tracing device based on water quality fingerprinting. The device includes the following parts:
[0110] The dual-spectral synchronous sampling module 502 performs dual-spectral synchronous sampling processing on the water body to be tested to obtain a spectral image, and constructs an initial water quality fingerprint based on the target quantification features in the spectral image;
[0111] The weight optimization module 504 constructs a weight function through the product of flow velocity and concentration gradient, and uses an environmental adjustment coefficient to perform real-time correction of the weight function to obtain a target weight function. The target weight function is then combined with the initial water quality fingerprint to obtain the target water quality fingerprint.
[0112] The pollutant source tracing module 506 matches the target water quality fingerprint with a preset fingerprint database to obtain spectral similarity. Then, through a dual physical constraint optimization model and Monte Carlo uncertainty quantification, it analyzes the target water quality fingerprint that has passed the spectral similarity verification to obtain the pollutant source tracing result.
[0113] The above-mentioned quantitative source tracing device for river pollutants based on water quality fingerprinting provided in this application embodiment can significantly improve the timeliness and accuracy of pollutant source tracing.
[0114] In one embodiment, when performing the step of performing dual-spectral synchronous sampling processing on the water body to be tested to obtain a spectral image, the aforementioned dual-spectral synchronous sampling module 502 is further used to: perform dual-spectral synchronous sampling processing on the water body to be tested using a xenon lamp pulse light source and a dual-grating monochromator to obtain the ultraviolet absorption spectrum and three-dimensional fluorescence spectrum of the water body to be tested.
[0115] In one embodiment, when constructing an initial water quality fingerprint based on target quantization features in the spectral image, the aforementioned dual-spectrum synchronous sampling module 502 is further configured to: perform turbidity compensation processing and temperature drift suppression processing on the ultraviolet absorption spectrum and the three-dimensional fluorescence spectrum through a dual-dimensional real-time compensation mechanism to obtain a corrected spectrum; extract the target quantization features in the corrected spectrum, and determine the vector constructed from the extracted target quantization features as the initial water quality fingerprint, wherein the target quantization features include: ultraviolet absorption features, absorption slope features, fluorescence features, and corrosion index.
[0116] In one embodiment, when performing the step of real-time correction of the weight function using environmental adjustment coefficients to obtain the target weight function, the weight optimization module 504 is further configured to: perform real-time calculations on turbidity compensation for ultraviolet attenuation, rainstorm retention of agricultural signals, flow velocity enhancement of surface source characteristics, and pH interference suppression based on real-time measurement information of the current environment to obtain the environmental adjustment coefficients at the current time; and multiply the environmental adjustment coefficients at the current time by the weight function dimension by dimension to obtain the target weight function.
[0117] In one embodiment, after performing the step of combining the target weight function with the initial water quality fingerprint to obtain the target water quality fingerprint, the weight optimization module 504 is further used to: collect real-time hydrological parameters and concatenate the real-time hydrological parameters with the enhanced feature matrix corresponding to the target water quality fingerprint to obtain the target input water quality fingerprint, wherein the real-time hydrological parameters include: flow velocity, water temperature and pH.
[0118] In one embodiment, when performing the step of analyzing the target water quality fingerprint that has passed the spectral similarity verification through a dual physical constraint optimization model and Monte Carlo uncertainty quantization to obtain the pollutant source tracing result, the pollutant source tracing module 506 is further configured to: identify the target input water quality fingerprint with a spectral similarity greater than a preset similarity threshold as a candidate source, and send the candidate source to the dual physical constraint optimization model for analysis to obtain the pollutant source tracing result; and perform Monte Carlo uncertainty quantization on the pollutant source tracing result to obtain the target pollutant source tracing result.
[0119] In one embodiment, when performing the step of sending candidate sources to the dual physical constraint optimization model for analytical processing to obtain pollutant source tracing results, the pollutant source tracing module 506 is further configured to: send candidate sources to the dual physical constraint optimization model for non-negative constraint processing and normalization constraint processing respectively, obtain the contribution percentage of each pollution source to the current pollution event of the water body to be tested, and determine the contribution percentage as the pollutant source tracing result.
[0120] The device provided in this embodiment of the invention has the same implementation principle and technical effect as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the aforementioned method embodiment.
[0121] This invention provides a server, specifically, the server includes a processor and a storage device; the storage device stores a computer program, which, when run by the processor, executes the method described in any of the above embodiments.
[0122] Figure 6This is a schematic diagram of the structure of a server provided in an embodiment of the present invention. The server 100 includes: a processor 60, a memory 61, a bus 62, and a communication interface 63. The processor 60, the communication interface 63, and the memory 61 are connected through the bus 62. The processor 60 is used to execute executable modules, such as computer programs, stored in the memory 61.
[0123] The memory 61 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 63 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc.
[0124] Bus 62 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 6 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.
[0125] The memory 61 is used to store programs. After receiving an execution instruction, the processor 60 executes the program. The method executed by the device for defining the flow process disclosed in any of the foregoing embodiments of the present invention can be applied to the processor 60 or implemented by the processor 60.
[0126] Processor 60 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 60 or by instructions in software form. Processor 60 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 61. Processor 60 reads the information in memory 61 and, in conjunction with its hardware, completes the steps of the above method.
[0127] The computer program product of the readable storage medium provided in the embodiments of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the foregoing method embodiments. For specific implementation, please refer to the foregoing method embodiments, which will not be repeated here.
[0128] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0129] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.
Claims
1. A quantitative source tracing method for river pollutants based on water quality fingerprinting, characterized in that, The method includes: A dual-spectral synchronous sampling process is performed on the water body to be tested to obtain a spectral image, and an initial water quality fingerprint is constructed based on the target quantization features in the spectral image. A weighting function is constructed by multiplying the flow velocity and concentration gradients, and the weighting function is corrected in real time using an environmental adjustment coefficient to obtain a target weighting function. The target weighting function is then combined with the initial water quality fingerprint to obtain the target water quality fingerprint. The target water quality fingerprint is matched with a preset fingerprint database to obtain spectral similarity. The target water quality fingerprint that passes the spectral similarity verification is then analyzed using a dual physical constraint optimization model and Monte Carlo uncertainty quantification to obtain pollutant source tracing results. The step of combining the target weight function with the initial water quality fingerprint to obtain the target water quality fingerprint includes: collecting real-time hydrological parameters and concatenating the real-time hydrological parameters with the enhanced feature matrix corresponding to the target water quality fingerprint to obtain the target input water quality fingerprint. The real-time hydrological parameters include: flow velocity, water temperature and pH. The step of analyzing the target water quality fingerprint that has passed the spectral similarity verification through a dual physical constraint optimization model and Monte Carlo uncertainty quantization to obtain the pollutant source tracing result includes: inputting the target with a spectral similarity greater than a preset similarity threshold into the water quality fingerprint, determining it as a candidate source, and sending the candidate source to the dual physical constraint optimization model for analysis to obtain the pollutant source tracing result; performing Monte Carlo uncertainty quantization on the pollutant source tracing result to obtain the target pollutant source tracing result. The step of sending the candidate sources to the dual physical constraint optimization model for analytical processing to obtain the pollutant source tracing results includes: sending the candidate sources to the dual physical constraint optimization model for non-negative constraint processing and normalization constraint processing respectively, obtaining the contribution percentage of each pollution source to the current pollution event of the water body to be tested, and determining the contribution percentage as the pollutant source tracing results.
2. The method for quantitative source tracing of river pollutants based on water quality fingerprinting according to claim 1, characterized in that, The step of performing dual-spectral synchronous sampling processing on the water body to be tested to obtain a spectral image includes: By using a xenon lamp pulsed light source and a dual-grating monochromator to perform dual-spectral synchronous sampling of the water body under test, the ultraviolet absorption spectrum and three-dimensional fluorescence spectrum of the water body under test are obtained.
3. The method for quantitative source tracing of river pollutants based on water quality fingerprinting according to claim 1, characterized in that, The step of constructing an initial water quality fingerprint based on the target quantization features in the spectral image includes: By employing a dual-dimensional real-time compensation mechanism, turbidity compensation and temperature drift suppression are applied to the ultraviolet absorption spectrum and the three-dimensional fluorescence spectrum to obtain the corrected spectrum. The target quantization features in the corrected spectrum are extracted and processed, and the vector constructed from the extracted target quantization features is determined as the initial water quality fingerprint. The target quantization features include: ultraviolet absorption features, absorption slope features, fluorescence features, and corrosion index.
4. The method for quantitative source tracing of river pollutants based on water quality fingerprinting according to claim 1, characterized in that, The step of performing real-time correction processing on the weight function using an environmental adjustment coefficient to obtain the target weight function includes: Based on real-time measurement information of the current environment, the environmental adjustment coefficients for the current moment are calculated and processed in real time to compensate for turbidity-induced ultraviolet attenuation, preserve agricultural signals during rainstorms, enhance surface source characteristics through flow velocity, and suppress pH interference. The target weight function is obtained by multiplying the environmental adjustment coefficient at the current moment with the weight function dimension by dimension.
5. A quantitative source tracing device for river pollutants based on water quality fingerprinting, characterized in that, The device includes: The dual-spectral synchronous sampling module performs dual-spectral synchronous sampling processing on the water body to be tested to obtain a spectral image, and constructs an initial water quality fingerprint based on the target quantization features in the spectral image; The weight optimization module constructs a weight function through the product of flow velocity and concentration gradient, and performs real-time correction processing on the weight function using an environmental adjustment coefficient to obtain a target weight function. The target weight function is then combined with the initial water quality fingerprint to obtain the target water quality fingerprint. The pollutant source tracing module matches the target water quality fingerprint with a preset fingerprint database to obtain spectral similarity. Then, through a dual physical constraint optimization model and Monte Carlo uncertainty quantification, it analyzes the target water quality fingerprint that has passed the spectral similarity verification to obtain the pollutant source tracing result. The step of combining the target weight function with the initial water quality fingerprint to obtain the target water quality fingerprint includes: collecting real-time hydrological parameters and concatenating the real-time hydrological parameters with the enhanced feature matrix corresponding to the target water quality fingerprint to obtain the target input water quality fingerprint. The real-time hydrological parameters include: flow velocity, water temperature and pH. The step of analyzing the target water quality fingerprint that has passed the spectral similarity verification through a dual physical constraint optimization model and Monte Carlo uncertainty quantization to obtain the pollutant source tracing result includes: inputting the target with a spectral similarity greater than a preset similarity threshold into the water quality fingerprint, determining it as a candidate source, and sending the candidate source to the dual physical constraint optimization model for analysis to obtain the pollutant source tracing result; performing Monte Carlo uncertainty quantization on the pollutant source tracing result to obtain the target pollutant source tracing result. The step of sending the candidate sources to the dual physical constraint optimization model for analytical processing to obtain the pollutant source tracing results includes: sending the candidate sources to the dual physical constraint optimization model for non-negative constraint processing and normalization constraint processing respectively, obtaining the contribution percentage of each pollution source to the current pollution event of the water body to be tested, and determining the contribution percentage as the pollutant source tracing results.
6. A server, characterized in that, The method includes a processor and a memory, the memory storing computer-executable instructions executable by the processor, the processor executing the computer-executable instructions to implement the method of any one of claims 1 to 4.
7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions that, when invoked and executed by a processor, cause the processor to perform the method described in any one of claims 1 to 4.