Water quality anomaly tracing method and system based on water quality fluorescence fingerprint

Abstract

The application relates to the technical field of water quality detection, and discloses a water quality anomaly tracing method and system based on water quality fluorescence fingerprints, which comprises the following steps: obtaining fluorescence fingerprint data of water samples by using a three-dimensional fluorescence spectrometer, extracting fluorescence characteristic parameters after pretreatment, and constructing a fluorescence fingerprint spectrum library; when an abnormal alarm is triggered by online water quality monitoring, real-time three-dimensional fluorescence fingerprints are obtained, abnormal components are analyzed and separated, and the contribution proportion is quantitatively analyzed, so that the main pollution source type and the preliminary judgment discharge area are obtained; the abnormal components are compared with the similarity of the spectrum library, and a pollution diffusion path diagram is constructed; according to the preliminary judgment discharge area and the path diagram, secondary sampling comparison is carried out on a suspected pollution source discharge port, the pollution migration path is deduced in reverse according to the similarity and the attenuation relationship of the fluorescence fingerprints of upstream and downstream points, and the specific discharge point of the pollution source and the influence range are obtained. The application shortens the emergency response time, improves the positioning accuracy of the discharge point and the prediction accuracy of the pollution range.

Description

Technical Field

[0001] This invention relates to the field of water quality testing technology, and more specifically, to a method and system for tracing water quality anomalies based on water quality fluorescence fingerprinting. Background Technology

[0002] In water pollution emergency response, quickly and accurately tracing the source of pollution, formulating effective control measures, and blocking the spread of pollution are key steps.

[0003] In recent years, three-dimensional fluorescence spectroscopy has been increasingly applied to water quality tracing due to its advantages such as strong fingerprinting, fast detection speed, and no need for complex pretreatment. Existing methods typically identify pollution types by comparing the collected water sample spectra with standard spectral libraries and utilizing similarity, but these methods still have the following drawbacks: Existing technologies lack a scientific and systematic screening mechanism in the process of screening fluorescence characteristic parameters. They often use a single or a few fluorescence characteristic parameters for comparison, without combining the statistical distribution law and discrimination evaluation of the parameters to screen core parameters. This results in insufficient specificity of the constructed water quality fingerprint spectrum. Under the interference of complex water matrix, it is difficult to effectively separate abnormal pollutant components and is prone to missed detection. In scenarios involving mixed emissions from multiple pollution sources, existing source tracing methods can only qualitatively determine the types of pollution sources, and cannot quantify the emission contribution ratio of each pollution source. This makes it difficult to pinpoint the main pollution sources, greatly hindering the precise implementation of pollution control measures and failing to meet the source tracing needs in mixed pollution scenarios. In tracing the source of water quality anomalies in long-distance watersheds, existing methods mostly rely on the comparison of fluorescent fingerprints at a single monitoring point. They do not make full use of the similarity and decay law of fluorescent fingerprints at upstream and downstream points to continuously correct the source tracing results. Furthermore, they cannot transform the source tracing simulation results into a visualized pollution diffusion path map, resulting in blindness in the location of pollution sources and difficulty in accurately pinpointing the specific location of pollution sources. No effective solutions have yet been proposed to address the problems in the relevant technologies. Summary of the Invention

[0004] To address the problems in related technologies, this invention proposes a method and system for tracing water quality anomalies based on water quality fluorescence fingerprinting, in order to overcome the aforementioned technical problems existing in the existing related technologies.

[0005] Therefore, the specific technical solution adopted by the present invention is as follows: According to a first aspect of the present invention, a method for tracing water quality anomalies based on water quality fluorescence fingerprinting is provided, the method comprising: S1. Using a three-dimensional fluorescence spectrometer, water samples acquired at different times are scanned to obtain fluorescence fingerprint data; S2. Preprocess the fluorescent fingerprint data and extract the fluorescent feature parameters. Based on the statistical distribution and discriminative power of the extracted fluorescent feature parameters, construct a fluorescent fingerprint spectrum library. S3. When an abnormal alarm is triggered by online water quality monitoring, water samples from the abnormal period are acquired in real time, and a three-dimensional fluorescence fingerprint is obtained using a three-dimensional fluorescence spectrometer. S4. Based on the independent contribution of each component of the fluorescence signal to the excitation-emission wavelength pair, the real-time three-dimensional fluorescence fingerprint is analyzed to separate the fluorescence fingerprint characteristics of the abnormal components. By quantitatively analyzing the contribution ratio of the fluorescence fingerprint characteristics of each abnormal component, the main pollution source type and the preliminary emission area are obtained. S5. Compare the fluorescence fingerprint features of the abnormal components with the fluorescence fingerprint spectrum library for similarity, and construct a pollution diffusion path map based on the comparison results, combined with the pollution source emission ledger, hydrogeological conditions and sewage discharge path. S6. Based on the preliminary assessment of the emission area and pollution diffusion path map, conduct secondary sampling and comparison of suspected pollution source emission outlets. Based on the similarity and attenuation relationship of fluorescent fingerprints at upstream and downstream points, reverse the pollutant migration path to obtain the specific emission points and impact range of the pollution source.

[0006] According to a second aspect of the present invention, a water quality anomaly tracing system based on water quality fluorescence fingerprinting is provided, the system comprising: The fluorescence fingerprint data acquisition module is used to scan water samples acquired in advance at different times using a three-dimensional fluorescence spectrometer to obtain fluorescence fingerprint data; The fluorescence fingerprint library construction module is used to preprocess fluorescence fingerprint data and extract fluorescence feature parameters. Based on the statistical distribution and discriminative power of the extracted fluorescence feature parameters, a fluorescence fingerprint library is constructed. The real-time abnormal water sample acquisition module is used to acquire water samples during abnormal periods in real time when an abnormal alarm is triggered by online water quality monitoring, and to acquire real-time three-dimensional fluorescence fingerprints using a three-dimensional fluorescence spectrometer. The pollution source preliminary identification module is used to analyze the real-time three-dimensional fluorescence fingerprint based on the independent contribution of each component of the fluorescence signal on the excitation-emission wavelength pair, separate the fluorescence fingerprint features of abnormal components, and obtain the main pollution source type and preliminary emission area by quantitatively analyzing the contribution ratio of the fluorescence fingerprint features of each abnormal component. The pollution diffusion path map construction module is used to compare the fluorescence fingerprint features of abnormal components with the fluorescence fingerprint spectrum library, and construct the pollution diffusion path map based on the comparison results, combined with the pollution source emission ledger, hydrogeological conditions and sewage discharge path. The pollution source precise location module is used to perform secondary sampling and comparison of suspected pollution source emission outlets based on the initial judgment of the emission area and pollution diffusion path map. Based on the similarity and attenuation relationship of the fluorescent fingerprints of upstream and downstream points, the pollutant migration path is reversed to obtain the specific emission point and impact range of the pollution source.

[0007] According to a third aspect of the present invention, a computer device is provided.

[0008] In some embodiments, the computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method described above.

[0009] According to a fourth aspect of the present invention, a computer-readable storage medium is provided.

[0010] In one embodiment, a computer program is stored on the computer-readable storage medium, which, when executed by a processor, implements the steps of the above method.

[0011] The beneficial effects of this invention are as follows: 1. This invention establishes a core feature parameter screening mechanism based on statistical distribution and discrimination evaluation, which realizes the construction of highly specific water quality fingerprint spectrum. It can effectively separate abnormal components under the interference of complex water matrix, and significantly improve the sensitivity and anti-interference ability of pollution source identification.

[0012] 2. This invention uses the contribution ratio calculation under non-negative constraints in conjunction with the hydrodynamic model to accurately identify the main pollution source types and quantify their emission contributions, thus avoiding the limitations of traditional methods that can only make qualitative judgments or are difficult to handle mixed emission sources.

[0013] 3. This invention enables the source tracing process to be continuously corrected based on the similarity decay law of fluorescent fingerprints at upstream and downstream points, and transforms the simulation results into a visualized pollution diffusion path map, effectively solving the problems of blindness in locating pollution sources and false positives in long-distance watersheds.

[0014] 4. This invention solves the problems of delayed source tracing, difficulty in identifying the source, and unclear scope of impact in sudden water pollution incidents. It significantly shortens the emergency response time, improves the accuracy of locating specific emission points and the accuracy of pollution range prediction, and achieves the dual goals of rapid response and precise pollution control in environmental safety supervision. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart of a water quality anomaly tracing method based on water quality fluorescent fingerprinting according to an embodiment of the present invention; Figure 2 This is a schematic diagram of a water quality anomaly tracing system based on water quality fluorescent fingerprinting according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of a computer device according to an exemplary embodiment.

[0017] In the picture: 1. Fluorescent fingerprint data acquisition module; 2. Fluorescent fingerprint spectrum library construction module; 3. Real-time abnormal water sample acquisition module; 4. Initial pollution source identification module; 5. Pollution diffusion path map construction module; 6. Pollution source precise location module. Detailed Implementation

[0018] To further illustrate the various embodiments, the present invention provides accompanying drawings, which are part of the disclosure of the present invention. These drawings are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these drawings, those skilled in the art should be able to understand other possible implementation methods and the advantages of the present invention. The components in the drawings are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0019] According to embodiments of the present invention, a method and system for tracing water quality anomalies based on water quality fluorescence fingerprinting is provided.

[0020] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments, such as... Figure 1 As shown, the water quality anomaly tracing method based on water quality fluorescence fingerprinting according to an embodiment of the present invention includes: S1. Using a three-dimensional fluorescence spectrometer, water samples acquired at different times are scanned to obtain fluorescence fingerprint data; It should be explained that the water samples obtained in advance for different periods include: several fixed sampling points are set up in the target monitoring area, and surface water samples and water samples at different depths are collected during the high-water season, normal-water season, and low-water season, with no less than 3 sets of parallel samples collected at each sampling point for each period. During detection, a fluorescence spectrophotometer (such as Hitachi F-7000) is used for three-dimensional fluorescence spectral scanning. The excitation wavelength range is set to 20-500 nm, the emission wavelength range to 250-600 nm, the scanning interval to 5 nm, the scanning speed to 12000 nm / min, and the excitation and emission slit widths to 5 nm. After scanning, the original fluorescence fingerprint matrix data is obtained with the excitation wavelength as the x-axis, the emission wavelength as the y-axis, and the fluorescence intensity as the response value.

[0021] S2. Preprocess the fluorescent fingerprint data and extract the fluorescent feature parameters. Based on the statistical distribution and discriminative power of the extracted fluorescent feature parameters, construct a fluorescent fingerprint spectrum library. In this optional embodiment, the fluorescent fingerprint data is preprocessed, and fluorescent feature parameters are extracted. Based on the statistical distribution and discriminative power of the extracted fluorescent feature parameters, a fluorescent fingerprint library is constructed, including the following steps: S21. The fluorescent fingerprint data is sequentially denoised, normalized and scattered to obtain preprocessed fluorescent fingerprint data, and fluorescent feature parameters are extracted from the preprocessed fluorescent fingerprint data. Specifically, for the raw fluorescence fingerprint data, the fluorescence intensity matrix is ​​first filtered using the Savitzky-Golay convolution smoothing algorithm, with a window width of 11 wavelength points and a polynomial order of 3, to eliminate instrument noise and random fluctuations. Then, normalization is performed, using the maximum fluorescence intensity of the water sample at a specific excitation / emission wavelength pair as a benchmark, normalizing all fluorescence intensity values ​​to the 0-1 range to eliminate the dimensional influence caused by differences in instrument response between different batches. Finally, scattering correction is performed, using Delaunay trigonometric interpolation to identify and remove Rayleigh and Raman scattering regions, and filling the scattering regions by interpolation of adjacent valid data points to restore the true fluorescence signal. This is existing technology and will not be elaborated further.

[0022] When extracting fluorescence characteristic parameters from preprocessed fluorescence fingerprint data, the local maxima on the excitation-emission wavelength pair are identified as fluorescence peaks, and the corresponding excitation wavelength, emission wavelength, and peak intensity are recorded. The half-width at half-maximum (WHM) of the fluorescence peak is calculated, which is the wavelength width corresponding to half the peak intensity. The fluorescence intensity in the parallel and perpendicular directions is obtained by setting a polarizer, and the fluorescence polarization degree is calculated. The fluorescence lifetime is determined using a time-resolved fluorescence mode. The intensity ratio of two different characteristic peaks is selected as the characteristic peak intensity ratio.

[0023] S22. Perform statistical analysis on the extracted fluorescence feature parameters, calculate the statistical distribution of each fluorescence feature parameter, evaluate the discrimination of each fluorescence feature parameter through analysis of variance, and select fluorescence feature parameters with discrimination greater than the preset threshold as core feature parameters. Specifically, when performing statistical analysis on the extracted fluorescence characteristic parameters, the mean, standard deviation, and coefficient of variation of each characteristic parameter in the same type of water sample are calculated to determine their normal fluctuation range. Then, one-way ANOVA is used to evaluate the distinguishing ability of each characteristic parameter among different pollution source categories. The ratio of between-group variance to within-group variance of each characteristic parameter is calculated (F-value). The larger the F-value, the higher the distinguishing ability of the parameter among different pollution sources. Characteristic parameters with F-values ​​greater than a preset threshold (e.g., F>3.5) are selected as core characteristic parameters.

[0024] S23. Based on the core feature parameters, classify and organize the fluorescent fingerprint data to construct a fluorescent fingerprint spectrum library.

[0025] Specifically, background water samples collected at different times and source water samples collected from various typical pollution source outlets are grouped according to pollution source type. Core feature parameters are extracted from each group to form a feature vector. The mean vector and covariance matrix of the feature vectors for that group are calculated to form a fluorescent fingerprint standard template for that type of pollution source. The standard templates for each pollution source type are compiled and stored to form a fluorescent fingerprint spectrum library.

[0026] In this optional embodiment, the fluorescence characteristic parameters include fluorescence peak position, fluorescence peak intensity, fluorescence polarization degree, fluorescence peak half width at half maximum (FWHM), fluorescence lifetime, and characteristic peak intensity ratio.

[0027] S3. When an abnormal alarm is triggered by online water quality monitoring, water samples from the abnormal period are acquired in real time, and a three-dimensional fluorescence fingerprint is obtained using a three-dimensional fluorescence spectrometer. It should be explained that an online water quality monitoring system is deployed in the target monitoring area. This system includes sensors for conventional water quality indicators such as pH, conductivity, dissolved oxygen, chemical oxygen demand, and ammonia nitrogen, as well as a data acquisition and transmission unit. The system sets alarm thresholds for each indicator. When real-time monitoring data exceeds the set threshold, the system automatically generates an abnormal alarm signal and records the time of the abnormality. This is existing technology and will not be elaborated further. In response to the abnormal alarm signal, an automatic sampling device linked to the online monitoring system is activated. This automatic sampling device is located at the monitoring point and has both timed and triggered sampling modes. Upon receiving an abnormal alarm signal, the automatic sampling device immediately and continuously collects multiple sets of water samples at the alarm time and several subsequent time points. The collected water samples are transported via pipeline to a sample preservation unit, which maintains a low-temperature environment of 4°C to prevent changes in the water samples while awaiting testing. Water samples collected during the abnormal period are retrieved from the sample preservation unit by the operator and immediately scanned using the same three-dimensional fluorescence spectrometer and parameter settings as S1.

[0028] S4. Based on the independent contribution of each component of the fluorescence signal to the excitation-emission wavelength pair, the real-time three-dimensional fluorescence fingerprint is analyzed to separate the fluorescence fingerprint characteristics of the abnormal components. By quantitatively analyzing the contribution ratio of the fluorescence fingerprint characteristics of each abnormal component, the main pollution source type and the preliminary emission area are obtained. In this optional embodiment, the real-time three-dimensional fluorescence fingerprint is analyzed based on the independent contribution of each component of the fluorescence signal to the excitation-emission wavelength pair, the fluorescence fingerprint features of abnormal components are separated, and the main pollution source type and preliminary emission area are obtained by quantitatively analyzing the contribution ratio of the fluorescence fingerprint features of each abnormal component. This includes the following steps: S41. Obtain the real-time three-dimensional fluorescence fingerprint and represent it as a fluorescence intensity matrix on the excitation-emission wavelength pair. Specifically, the fluorescence intensity matrix uses the excitation wavelength and emission wavelength as coordinate axes, and the fluorescence intensity at the corresponding positions as matrix elements.

[0029] S42. Based on the independent contribution of each component of the fluorescence signal to the excitation-emission wavelength pair, the fluorescence intensity matrix is ​​decomposed to obtain several independent components and their corresponding fluorescence fingerprint features. Specifically, the fluorescence intensity matrices of multiple sampling points or different time points are stacked into a three-dimensional data cube. Through iterative least squares fitting, the mixed fluorescence signal is decomposed into several independent components. Each independent component contains a relative concentration score vector and a set of excitation-emission spectral profiles, which are the fluorescence fingerprint features of that component.

[0030] S43. Compare the fluorescent fingerprint characteristics of each independent component with the fluorescent fingerprint characteristics of normal water quality, and identify independent components that deviate from the normal range as abnormal components. Specifically, the similarity between each independent component fingerprint and the normal fingerprint is calculated. If the similarity between an independent component fingerprint and the normal fingerprint is lower than a preset threshold (e.g., less than 0.85), or if the characteristic peak position, peak intensity, or other parameters of the component deviate significantly from the statistical distribution range of normal water quality, then the independent component is identified as an abnormal component.

[0031] S44. Based on the contribution ratio of the fluorescent fingerprint characteristics of each abnormal component, determine the main pollution source type and the preliminary emission area.

[0032] In this optional embodiment, determining the main pollution source type and preliminary emission area based on the contribution ratio of the fluorescent fingerprint characteristics of each abnormal component includes the following steps: S441. Fit the fluorescence intensity matrix by linear combination of the fluorescence fingerprint features of each independent component, and calculate the contribution ratio of each anomalous component in the mixed fluorescence signal under non-negative constraints. Specifically, the fluorescence fingerprint features of all independent components (including normal and abnormal components) are used as basis vectors. The original fluorescence intensity matrix is ​​fitted by a linear combination of these basis vectors. Under the condition of non-negativity constraint (i.e., the contribution ratio of each component is not less than 0), the contribution ratio of each independent component is solved using the non-negative least squares method. This contribution ratio reflects the relative concentration of each component in the mixed water sample.

[0033] S442. Based on the contribution ratio of each abnormal component in the mixed fluorescence signal, the pollution source type corresponding to the abnormal component with the highest contribution ratio is determined as the main pollution source type. S443. Based on the identified main pollution source types and the spatial distribution of the corresponding abnormal components' fluorescent fingerprint characteristics in the monitoring area, the preliminary emission areas are determined.

[0034] It should be explained that the relative contribution ratio data of the abnormal component in water samples at different locations within the monitoring area are retrieved, and the contribution ratio of each location is spatially interpolated using a Geographic Information System (GIS) to generate a concentration contour map of the component. Continuous areas with higher contribution ratios are then designated as preliminary emission areas where major pollution sources may exist.

[0035] S5. Compare the fluorescence fingerprint features of the abnormal components with the fluorescence fingerprint spectrum library for similarity, and construct a pollution diffusion path map based on the comparison results, combined with the pollution source emission ledger, hydrogeological conditions and sewage discharge path. In this optional embodiment, the fluorescent fingerprint features of the abnormal components are compared with the similarity of the fluorescent fingerprint spectrum library. Based on the comparison results, combined with the pollution source emission ledger, hydrogeological conditions, and discharge paths, a pollution diffusion path map is constructed, including the following steps: S51. Compare the similarity between the fluorescent fingerprint features of the abnormal components and the fluorescent fingerprint features corresponding to each pollution source in the fluorescent fingerprint spectrum library, and calculate the matching degree of each pollution source. Specifically, during the comparison, a comprehensive similarity calculation method combining the cosine of the spectral angle and the weighted Euclidean distance is used, where the weighting coefficients are the discriminative values ​​of the core feature parameters. The comprehensive similarity value between the abnormal component fingerprint and the standard fingerprints of each pollution source is calculated and used as the matching degree of that pollution source. The matching degree ranges from 0 to 1, with values ​​closer to 1 indicating a higher degree of similarity.

[0036] S52. Based on the matching degree of each pollution source, identify the pollution source type with the highest matching degree as the target pollution source type, and retrieve the corresponding sewage outlet location information from the pollution source discharge ledger. Specifically, the pollution source emission ledger is a structured database containing fields such as pollution source name, pollution source type, emission outlet coordinates, emission outlet number, emission method, and emission pattern. The retrieved information includes at least the geographical coordinates and emission outlet number of the emission outlet.

[0037] S53. Based on the retrieved information on the location of the sewage outlets, and in conjunction with the hydrogeological conditions and sewage discharge paths of the monitoring area, a pollution diffusion path map is generated using a geographic information system.

[0038] Specifically, the location of the sewage outlet is taken as the pollution release point, and hydrogeological data of the monitoring area, including river flow direction, velocity distribution, and river cross-sectional morphology, are loaded. Simultaneously, the regional sewage discharge path topology is imported, including the sewage pipe network direction, open / covered pipe distribution, and confluence nodes. In the geographic information system, starting from the release point, based on the water flow direction and velocity data, particle tracking or numerical solutions of the convection-diffusion equations are used to simulate the migration trajectory of pollutants in the water body. The simulated pollutant streamlines and concentration distribution range are overlaid and rendered with the geographic base map to generate a visualized pollution diffusion path map, which marks the main transport channels, diffusion directions, and coverage areas of the pollutants.

[0039] S6. Based on the preliminary assessment of the emission area and pollution diffusion path map, conduct secondary sampling and comparison of suspected pollution source emission outlets. Based on the similarity and attenuation relationship of fluorescent fingerprints at upstream and downstream points, reverse the pollutant migration path to obtain the specific emission points and impact range of the pollution source.

[0040] In this optional embodiment, based on the preliminary assessment of the emission area and pollution diffusion path map, secondary sampling and comparison are performed on the suspected pollution source emission outlets. Based on the similarity and attenuation relationship of the fluorescent fingerprints at upstream and downstream locations, the pollutant migration path is reversed to obtain the specific emission point and affected area of ​​the pollution source, including the following steps: S61. Based on the pollution transmission channels and concentration attenuation gradient zones marked on the pollution diffusion path map, at least one suspected pollution source emission outlet in the preliminary emission area is selected as a target sampling point. Specifically, the pollution diffusion path map marks the pollution transmission channels and concentration decay gradient zones. During the screening process, priority is given to emission outlets located in the center of the high-value area of ​​the concentration isopleths and coinciding with the upstream direction of the pollution transmission channels.

[0041] S62. Secondary water samples are collected from the target sampling point and its upstream and downstream points respectively. The secondary sampling fluorescence fingerprint of each point is obtained by a three-dimensional fluorescence spectrometer, and the corresponding fluorescence characteristic parameters of each point are extracted. Specifically, upstream sampling points were selected 50-200 meters upstream of the target sampling point, and downstream sampling points were selected 100-500 meters downstream of the target sampling point. The specific distances were adjusted according to the water flow velocity and river width. Secondary water samples were collected from each point using the same methods and preservation conditions as in S1. After collection, a three-dimensional fluorescence spectrometer was used with the same scanning parameters as in S1 to obtain the secondary sampling fluorescence fingerprints for each point, and the corresponding fluorescence characteristic parameters for each point were extracted according to the method in S22.

[0042] S63. Compare the similarity features of the secondary sampling fluorescent fingerprints at each location with those of the abnormal components, calculate the similarity between each location and the abnormal component fingerprints, analyze the trend of similarity changes and attenuation relationship of the fluorescent fingerprints at upstream and downstream locations, and obtain the analysis results. Specifically, the similarity values ​​of upstream points, target sampling points, and downstream points are arranged in spatial order, and the trend of similarity changes with migration distance is analyzed. If the similarity of both the target sampling point and the downstream point is higher than that of the upstream point, and the similarity decreases with increasing downstream distance, then the presence of pollution input is confirmed.

[0043] S64. Based on the analysis results, if the similarity between the fluorescent fingerprint of the downstream point and the fingerprint of the abnormal component is significantly higher than that of the upstream point, and the similarity decreases with the increase of migration distance, then the suspected pollution source emission outlet is determined to be the actual pollution source emission point. Specifically, if the similarity between the fluorescent fingerprint of the downstream sampling point and the fingerprint of the abnormal component is significantly higher than that of the upstream sampling point (typically requiring a similarity value at least 0.2 higher than the upstream value), and the similarity shows a monotonically decreasing trend with increasing migration distance (i.e., the similarity gradually decreases from the target sampling point downstream), then the suspected pollution source emission point is determined to be the actual pollution source emission point. If the above pattern is not met, other suspected pollution sources are selected for verification.

[0044] S65. Based on the determined actual pollution source emission point, combined with the attenuation gradient of the fluorescence fingerprint of upstream and downstream points and the flow velocity data in the hydrogeological conditions, the migration path of pollutants from the emission point to the monitoring point is reversed, the pollution diffusion range is calculated, and the specific emission point and impact range of the pollution source are obtained.

[0045] Specifically, the actual pollution source emission point is taken as the release source. The similarity attenuation ratio of upstream and downstream points is obtained. Combined with the distance between points and the water flow velocity, the migration time and path of pollutants from the emission point to each monitoring point are inferred. The inverted migration path is compared and corrected with the pollution diffusion path map to finally determine the specific emission point coordinates of the pollution source. The boundary of the pollution impact range is delineated according to the position where the attenuation gradient decays to the background value, and the specific emission point and impact range of the pollution source are output.

[0046] According to one embodiment of the present invention, such as Figure 2 As shown, a water quality anomaly tracing system based on water quality fluorescence fingerprinting is also provided. This system includes: Fluorescent fingerprint data acquisition module 1 is used to scan water samples acquired in advance at different times using a three-dimensional fluorescence spectrometer to obtain fluorescent fingerprint data; The fluorescence fingerprint library construction module 2 is used to preprocess the fluorescence fingerprint data and extract fluorescence feature parameters. Based on the statistical distribution and discriminative power of the extracted fluorescence feature parameters, a fluorescence fingerprint library is constructed. The real-time abnormal water sample acquisition module 3 is used to acquire water samples during abnormal periods in real time when an abnormal alarm is triggered by online water quality monitoring, and to acquire real-time three-dimensional fluorescence fingerprints using a three-dimensional fluorescence spectrometer. The pollution source preliminary judgment module 4 is used to analyze the real-time three-dimensional fluorescence fingerprint based on the independent contribution of each component of the fluorescence signal on the excitation-emission wavelength pair, separate the fluorescence fingerprint features of abnormal components, and obtain the main pollution source type and preliminary emission area by quantitatively analyzing the contribution ratio of the fluorescence fingerprint features of each abnormal component. The pollution diffusion path map construction module 5 is used to compare the fluorescence fingerprint features of abnormal components with the fluorescence fingerprint spectrum library, and construct the pollution diffusion path map based on the comparison results, combined with the pollution source emission ledger, hydrogeological conditions and sewage discharge path. The pollution source precise location module 6 is used to perform secondary sampling and comparison of suspected pollution source emission outlets based on the initial judgment of the emission area and pollution diffusion path map. Based on the similarity and attenuation relationship of the fluorescent fingerprints of upstream and downstream points, the pollutant migration path is reversed to obtain the specific emission point and impact range of the pollution source.

[0047] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 3As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores static and dynamic information data. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements the steps in the above method embodiments.

[0048] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the computer device to which the present invention is applied. A specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0049] In addition, the present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0050] In addition, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0051] This invention is not limited to the structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this invention is limited only by the appended claims.

Claims

1. A water quality anomaly source tracing method based on water quality fluorescence fingerprint, characterized in that, The method includes: S1. Using a three-dimensional fluorescence spectrometer, water samples acquired at different times are scanned to obtain fluorescence fingerprint data; S2. Preprocess the fluorescent fingerprint data and extract the fluorescent feature parameters. Based on the statistical distribution and discriminative power of the extracted fluorescent feature parameters, construct a fluorescent fingerprint spectrum library. S3. When an abnormal alarm is triggered by online water quality monitoring, water samples from the abnormal period are acquired in real time, and a three-dimensional fluorescence fingerprint is obtained using a three-dimensional fluorescence spectrometer. S4. Based on the independent contribution of each component of the fluorescence signal to the excitation-emission wavelength pair, the real-time three-dimensional fluorescence fingerprint is analyzed to separate the fluorescence fingerprint characteristics of the abnormal components. By quantitatively analyzing the contribution ratio of the fluorescence fingerprint characteristics of each abnormal component, the main pollution source type and the preliminary emission area are obtained. S5. Compare the fluorescence fingerprint features of the abnormal components with the fluorescence fingerprint spectrum library for similarity, and construct a pollution diffusion path map based on the comparison results, combined with the pollution source emission ledger, hydrogeological conditions and sewage discharge path. S6. Based on the pollution transmission channels and concentration attenuation gradient bands marked on the pollution diffusion path map, at least one suspected pollution source outlet is selected as a target sampling point within the initially determined emission area. Secondary water samples are collected from the target sampling point and its upstream and downstream points. The secondary sampling fluorescence fingerprints of each point are obtained using a three-dimensional fluorescence spectrometer, and the corresponding fluorescence characteristic parameters of each point are extracted. The similarity between the secondary sampling fluorescence fingerprints of each point and the fluorescence fingerprints of the abnormal components is compared, and the similarity between each point and the abnormal component fingerprints is calculated. The trend of similarity change and attenuation relationship of the fluorescence fingerprints of upstream and downstream points are analyzed to obtain the analysis results. According to the analysis results, if the similarity between the fluorescence fingerprint of the downstream point and the abnormal component fingerprint is significantly higher than that of the upstream point, and the similarity decreases with the increase of migration distance, then the suspected pollution source outlet is determined to be the actual pollution source outlet. Based on the determined actual pollution source outlet, combined with the attenuation gradient of the fluorescence fingerprints of upstream and downstream points and the flow velocity data in the hydrogeological conditions, the migration path of pollutants from the outlet to the monitoring point is reversed, the pollution diffusion range is calculated, and the specific outlet and impact range of the pollution source are obtained.

2. The water quality anomaly tracing method based on water quality fluorescence fingerprint according to claim 1, characterized in that, The process of preprocessing fluorescent fingerprint data and extracting fluorescent feature parameters, and constructing a fluorescent fingerprint library based on the statistical distribution and discriminative power of the extracted fluorescent feature parameters, includes the following steps: S21. The fluorescent fingerprint data is sequentially denoised, normalized and scattered to obtain preprocessed fluorescent fingerprint data, and fluorescent feature parameters are extracted from the preprocessed fluorescent fingerprint data. S22. Perform statistical analysis on the extracted fluorescence feature parameters, calculate the statistical distribution of each fluorescence feature parameter, evaluate the discrimination of each fluorescence feature parameter through analysis of variance, and select fluorescence feature parameters with discrimination greater than the preset threshold as core feature parameters. S23. Based on the core feature parameters, classify and organize the fluorescent fingerprint data to construct a fluorescent fingerprint spectrum library.

3. The method for tracing water quality anomalies based on water quality fluorescence fingerprinting according to claim 2, characterized in that, The fluorescence characteristic parameters include fluorescence peak position, fluorescence peak intensity, fluorescence polarization degree, fluorescence peak half width at half maximum (FWHM), fluorescence lifetime, and characteristic peak intensity ratio.

4. The method for tracing water quality anomalies based on water quality fluorescence fingerprinting according to claim 1, characterized in that, The process of analyzing real-time three-dimensional fluorescence fingerprints based on the independent contributions of each component of the fluorescence signal to the excitation-emission wavelength pair, separating the fluorescence fingerprint features of anomalous components, and obtaining the main pollution source types and preliminary emission areas by quantitatively analyzing the contribution ratio of the fluorescence fingerprint features of each anomalous component includes the following steps: S41. Obtain the real-time three-dimensional fluorescence fingerprint and represent it as a fluorescence intensity matrix on the excitation-emission wavelength pair. S42. Based on the independent contribution of each component of the fluorescence signal to the excitation-emission wavelength pair, the fluorescence intensity matrix is ​​decomposed to obtain several independent components and their corresponding fluorescence fingerprint features. S43. Compare the fluorescent fingerprint characteristics of each independent component with the fluorescent fingerprint characteristics of normal water quality, and identify independent components that deviate from the normal range as abnormal components. S44. Based on the contribution ratio of the fluorescent fingerprint characteristics of each abnormal component, determine the main pollution source type and the preliminary emission area.

5. The method for tracing water quality anomalies based on water quality fluorescence fingerprinting according to claim 4, characterized in that, The process of determining the main pollution source type and preliminary emission area based on the contribution ratio of the fluorescent fingerprint characteristics of each abnormal component includes the following steps: S441. Fit the fluorescence intensity matrix by linear combination of the fluorescence fingerprint features of each independent component, and calculate the contribution ratio of each anomalous component in the mixed fluorescence signal under non-negative constraints. S442. Based on the contribution ratio of each abnormal component in the mixed fluorescence signal, the pollution source type corresponding to the abnormal component with the highest contribution ratio is determined as the main pollution source type. S443. Based on the identified main pollution source types and the spatial distribution of the corresponding abnormal components' fluorescent fingerprint characteristics in the monitoring area, the preliminary emission areas are determined.

6. The method for tracing water quality anomalies based on water quality fluorescence fingerprinting according to claim 1, characterized in that, The process of comparing the fluorescent fingerprint features of abnormal components with a fluorescent fingerprint database, and constructing a pollution diffusion path map based on the comparison results, combined with pollution source emission records, hydrogeological conditions, and discharge routes, includes the following steps: S51. Compare the similarity between the fluorescent fingerprint features of the abnormal components and the fluorescent fingerprint features corresponding to each pollution source in the fluorescent fingerprint spectrum library, and calculate the matching degree of each pollution source. S52. Based on the matching degree of each pollution source, identify the pollution source type with the highest matching degree as the target pollution source type, and retrieve the corresponding sewage outlet location information from the pollution source discharge ledger. S53. Based on the retrieved information on the location of the sewage outlets, and in conjunction with the hydrogeological conditions and sewage discharge paths of the monitoring area, a pollution diffusion path map is generated using a geographic information system.

7. A water quality anomaly tracing system based on water quality fluorescence fingerprinting, used to implement the water quality anomaly tracing method based on water quality fluorescence fingerprinting as described in any one of claims 1-6, characterized in that, The system includes: The fluorescence fingerprint data acquisition module is used to scan water samples acquired in advance at different times using a three-dimensional fluorescence spectrometer to obtain fluorescence fingerprint data; The fluorescence fingerprint library construction module is used to preprocess fluorescence fingerprint data and extract fluorescence feature parameters. Based on the statistical distribution and discriminative power of the extracted fluorescence feature parameters, a fluorescence fingerprint library is constructed. The real-time abnormal water sample acquisition module is used to acquire water samples during abnormal periods in real time when an abnormal alarm is triggered by online water quality monitoring, and to acquire real-time three-dimensional fluorescence fingerprints using a three-dimensional fluorescence spectrometer. The pollution source preliminary identification module is used to analyze the real-time three-dimensional fluorescence fingerprint based on the independent contribution of each component of the fluorescence signal on the excitation-emission wavelength pair, separate the fluorescence fingerprint features of abnormal components, and obtain the main pollution source type and preliminary emission area by quantitatively analyzing the contribution ratio of the fluorescence fingerprint features of each abnormal component. The pollution diffusion path map construction module is used to compare the fluorescence fingerprint features of abnormal components with the fluorescence fingerprint spectrum library, and construct the pollution diffusion path map based on the comparison results, combined with the pollution source emission ledger, hydrogeological conditions and sewage discharge path. The pollution source precise location module is used to perform secondary sampling and comparison of suspected pollution source emission outlets based on the initial judgment of the emission area and pollution diffusion path map. Based on the similarity and attenuation relationship of the fluorescent fingerprints of upstream and downstream points, the pollutant migration path is reversed to obtain the specific emission point and impact range of the pollution source.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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