Analysis method of water quality fingerprint database based on feature recognition

Abstract

The invention belongs to the technical field of water pollutant traceability, and particularly discloses a water quality fingerprint database analysis method based on feature recognition, which relates to the field of water pollutant traceability, and comprises the following steps: cross-comparing feature curved surface information of different upstream and downstream under feature peak whole database data and sorting to obtain pollutant features under different intensities; the pollution source type is identified according to the pollutant difference; sorting the characteristic curved surface information of the water body samples reflecting different nodes, calling a chemical characteristic analysis result, outputting a chemical characteristic sequence, and selecting a first sequence sampling point position of the chemical characteristic sequence corresponding to the spectral characteristics of the identified pollution source type as a pollution source; according to the method, whether the identification result is met or not is analyzed according to the biological characteristics of the pollution source, efficient source tracing is carried out on water pollutants, an accurate pollutant transmission path is output, meanwhile, the corresponding pollutant type is evaluated, the corresponding pollutant diffusion trend is given, and powerful data is provided for follow-up pollutant treatment.

Description

Technical Field

[0001] The present invention relates to the field of water pollutant source tracing, and in particular to an analysis method of a water quality fingerprint database based on feature recognition. Background Art

[0002] The water quality fingerprint database may not be able to respond effectively when processing water samples that do not have water pattern characteristics. For example, it has a weak ability to identify pollutants such as heavy metals. As the types and concentrations of pollutants change, the water quality fingerprint database needs to be continuously updated to maintain accuracy. However, in actual operations, dynamic updates are difficult, especially in complex watershed environments. Summary of the invention

[0003] The present invention provides an analysis method for a water quality fingerprint database based on feature recognition, which solves the problems of the prior art.

[0004] In a first aspect, the present invention provides a method for analyzing a water quality fingerprint database based on feature recognition, comprising: Collect three-dimensional characteristic spectrum data of water samples from multiple nodes upstream and downstream, and obtain the key features of the characteristic spectrum. Key features include characteristic peak position, intensity, and regional intensity integral; Collect chemical and biological characteristics of water samples at corresponding node locations; Cross-compare and sort the characteristic surface information of different upstream and downstream under the characteristic peak full library data, obtain the pollutant characteristics under different intensities, and identify the type of pollution source based on the pollutants; The characteristic surface information of water samples reflecting different nodes is sorted and the chemical feature analysis results are called. The output includes the chemical feature sorting of pH value, chemical oxygen demand, biochemical oxygen demand and total organic carbon. The first sequence sampling point position of the chemical feature sorting corresponding to the spectral feature that identifies the pollution source type is selected as the pollution source, and the biological feature analysis at the pollution source is based on whether it meets the identification results.

[0005] Furthermore, the cross-comparison and sorting of characteristic surface information of different upstream and downstream under the characteristic peak full library data obtains pollutant characteristics under different intensities, and identifies the type of pollution source according to the pollutants, including: Obtain the characteristic intensity distribution of water samples at multiple upstream and downstream nodes within the wavelength range of excitation light and emission light; Extract characteristic surface information of water samples; According to the characteristic surface information, the Euclidean distance or Pearson correlation coefficient algorithm is selected to calculate the similarity between the characteristic surfaces of different water system branches; According to the size and distribution of similarity, the transmission path and impact range of pollution sources in the water system are analyzed.

[0006] Furthermore, according to the size and distribution of the similarity, the transmission path and impact range of the pollution source in the water system are analyzed, and the specific analysis process includes the following: The water system is divided into trunks and branches, and the trunk includes at least one trunk and the branch includes at least one branch. The water flows from the trunk to the branch along the direction of the water flow. The trunk and the branch trunk are distinguished according to the water flow at the intersection of the river network, and the main branch and the branch branch are distinguished according to the water flow at the outflow intersection. The trunk with the largest water flow is selected as the trunk, and the branch with the largest water flow is selected as the main branch; Based on the similarity analysis between the branch roads and the main roads, the similarity and distribution of the main roads to the main roads are compared to obtain the influence ratio of multiple roads on the main roads under different spectral characteristics, and the road with the largest influence ratio of the pollution source type corresponding to the spectral characteristics is selected to trace the source upward; In the process of tracing back to the source, the similarity analysis between the branch trunk and the main branch is continued, and the transmission path of the pollution source in the water system is output.

[0007] Furthermore, in the similarity analysis, the similarities between the branch roads and the main roads under different characteristic peaks are determined, and the branch roads that are different from the main roads are selected and associated with the corresponding pollution source types. The mixture of characteristic peaks corresponding to different pollution sources affecting the main branches is continuously separated, and the influence ratios of multiple roads on the characteristic peaks corresponding to different pollution sources are obtained. Each time, the road with the largest influence ratio and the second largest road are selected for upward tracing.

[0008] Furthermore, water samples from upstream and downstream monitoring points are sampled and characteristic spectral analysis is performed based on the timestamp nodes; Calculate the similarity between nodes with different timestamps and analyze the transmission and change trend of pollution sources over time.

[0009] Furthermore, the similarity between nodes with different timestamps is calculated to analyze the transmission and change trend of pollution sources over time, specifically including: Obtain the corresponding transmission path of the pollution source between the trunk road and the branch road, the corresponding sampling point location, analyze the timestamp association information of the next sampling point location under the influence of the water flow velocity, and verify the traceability data results of multiple nodes under the corresponding transmission path based on the timestamp association information. If the data of one node does not match the traceability data results under the transmission path, then correct the path to other trunk roads under the corresponding timestamp that meet the traceability data results.

[0010] Furthermore, the sampling and characteristic spectrum analysis of water samples at upstream and downstream monitoring points based on the timestamp node also includes the verification of the sampling timestamp of the water samples of the transmission path determined: Match the sampling timestamps of water samples along the traceability path to determine whether they meet the pollutant transfer speed under the influence of water flow rate; When the transfer time corresponding to the pollutant transfer speed from the trunk to the branch is consistent with the water sample sampling timestamp under the output transfer path and is within the influence range of the system error, the verification is successful; If the verification fails, the water sample sampling timestamp that meets the influence of water flow velocity is selected to re-analyze the transmission path at the trunk and branch locations of the corresponding nodes.

[0011] Furthermore, chemical characteristic data is collected and analyzed based on the water sample sampling points where the pollutants in the last branch of the transmission path are located. Based on the chemical characteristic analysis results and the water body characteristics, the proportion of pollutants in the next branch corresponding to the water body characteristics is output. At the same time, the time period for the next branch to reach the pollutant accumulation index is evaluated based on the water body characteristics.

[0012] Furthermore, based on the type of pollution source corresponding to the water body characteristics that affect the spread of the pollution source, including: water flow velocity, flow direction, water depth, water body shape, water body temperature and density, the numerical trend of the accumulation of pollution sources to the threshold index over time under the influence of water body characteristics is output.

[0013] Furthermore, while outputting the numerical trend of the pollutant accumulation to the threshold index over time, the downstream water body branch is monitored to see whether there are other branches merging in the middle, and the numerical trend is adjusted under the interference of water body characteristics according to the pollutant spectral characteristic data and chemical characteristic data of the downstream water body branch; The present invention provides an analysis method for a water quality fingerprint database based on feature recognition, which uses water body characteristics in combination with spectral characteristics, chemical characteristics and biological characteristics to efficiently trace water pollutants and output accurate pollutant transmission paths, while evaluating corresponding pollutant types and giving corresponding pollutant diffusion trends, providing powerful data for subsequent pollutant control. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of the present invention, and do not constitute a limitation of the embodiments of the present invention. In the drawings: Figure 1 A flow chart of an analysis method of a water quality fingerprint database based on feature recognition is provided as an exemplary embodiment of the present invention.

[0015] Figure 2 A flow chart of a pollutant source tracing and transfer path analysis method based on a water quality fingerprint database with feature recognition is provided as an exemplary embodiment of the present invention.

[0016] Figure 3 A flow chart of a method for analyzing pollutant diffusion trends in a water quality fingerprint database based on feature recognition is provided as an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0017] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0018] First, the terms involved in the present invention are explained: The water quality characteristic fingerprint database is a system used to store and manage characteristic spectral data of water samples. Its core is to obtain the "water quality fingerprint" of water samples through three-dimensional characteristic spectral technology, and compare it with the characteristic fingerprints of known pollution sources to achieve rapid tracing and identification of pollution sources.

[0019] The water quality characteristic fingerprint database involves the comparative analysis function of public databases and private databases, and also includes the establishment of processes and update mechanisms, which can provide correlation information for the specificity and traceability of the water quality characteristic fingerprint database; In the prior art, the construction process of the water quality characteristic fingerprint database is to collect water samples from different pollution sources (such as industrial wastewater, domestic sewage, etc.), and obtain their three-dimensional characteristic spectra through a characteristic spectrometer, extract the key features of the characteristic spectra, such as the position and intensity of characteristic peaks, enter the collected and extracted characteristic fingerprint data into the database, and classify and manage them, and then quickly identify the suspected pollution sources by comparing the characteristic fingerprints of unknown water samples with the fingerprints of known pollution sources in the database, and update the database regularly to ensure the accuracy and timeliness of the data, especially when the characteristic fingerprints of the pollution sources change significantly.

[0020] The specific application scenario of the present invention is based on water quality characteristic fingerprint database analysis.

[0021] In the present invention, the source of pollutants can be quickly identified through spectral features, the nature of pollutants can be further verified through chemical features, and biological features can be used to evaluate the ecological health status. The three are combined to form a database information for holistic evaluation of water quality. Since the precise quantitative information provided by chemical features takes a long time, the impact of pollutants is distinguished through biological features, and then the type of pollutants is identified and traced in conjunction with spectral features.

[0022] The present invention provides a method for analyzing a water quality fingerprint database based on feature recognition, aiming to solve the above technical problems in the prior art.

[0023] The technical solution of the present invention and how the technical solution of the present invention solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present invention will be described below in conjunction with the accompanying drawings.

[0024] Embodiment 1: Conduct water pollution sampling and analysis on a river or lake, set up multiple water sampling points, set the sampling frequency of the sampling points according to the runoff velocity, set up multiple dense sampling areas for sampling points with fast runoff velocity, obtain three-dimensional characteristic spectral data, and generate the corresponding water quality fingerprint database. Simultaneously investigate the water body characteristics in the sampling area, and conduct the following pollutant tracing and transmission path analysis, and diffusion trend analysis: Cross-compare and sort the characteristic surface information of different upstream and downstream under the characteristic peak full library data, obtain the pollutant characteristics under different intensities, and identify the type of pollution source based on the pollutants; The characteristic surface information of water samples reflecting different nodes is sorted and the chemical characteristic analysis results are called. The chemical characteristic sorting including pH value, chemical oxygen demand, biochemical oxygen demand and total organic carbon is output. The sampling point position of the first sequence of chemical characteristic sorting corresponding to the spectral characteristics of the pollution source type is selected as the pollution source, and the biological characteristic analysis at the pollution source is based on whether it meets the identification result; Among them, the characteristic surface information can be understood as a multi-dimensional feature expression constructed by spectral data in water sample analysis. Specifically, the characteristic surface information can be regarded as a surface formed by the three-dimensional fluorescence spectrum data of water samples at different excitation wavelengths and emission wavelengths. This surface reflects the spectral response characteristics of water samples at different wavelength combinations.

[0025] During the analysis process, the characteristic surface information can help the present invention to more fully understand the spectral characteristics of the water sample. By comparing the characteristic surface information of different nodes, the present invention can identify the characteristics of pollutants that may be present in the water sample. This comparative analysis can reveal the pollution of the water sample at different locations, thereby helping the present invention to identify the pollution source.

[0026] The characteristic surface information can also be combined with the chemical and biological characteristics of the water sample for a more in-depth analysis. For example, by comparing the characteristic surface information with chemical characteristics such as pH, chemical oxygen demand, biochemical oxygen demand, and total organic carbon, the present invention can more accurately identify the type and location of the pollution source. At the same time, combined with biological feature analysis, the present invention can further verify the accuracy of the identification results.

[0027] In conclusion, characteristic surface information plays an important role in the analysis of water samples. It provides a multi-dimensional and comprehensive analysis method for the present invention, which helps the present invention to understand the pollution status of water bodies and identify pollution sources more comprehensively and accurately.

[0028] like Figure 1 As shown, Step 1, obtaining the characteristic intensity distribution of water samples at multiple upstream and downstream nodes within the wavelength range of the excitation light and the emission light; Step 2, extracting characteristic surface information of water samples; Step 3: Select the Euclidean distance or Pearson correlation coefficient algorithm according to the characteristic surface information to calculate the similarity between the characteristic surfaces of different water system branches; Step 4: Analyze the transmission path and impact range of the pollution source in the water system based on the size and distribution of the similarity.

[0029] In step 3, a suitable algorithm (such as Euclidean distance, Pearson correlation coefficient, etc.) is used to calculate the similarity between the characteristic surfaces of different water system branches. The similarity calculation can be based on the original data of the entire surface or on the extracted key characteristic parameters. According to the size and distribution of the similarity, the transmission path and impact range of the pollution source in the water system are analyzed. Water system branches with higher similarity may be affected by the same pollution source, while branches with lower similarity may be affected by different pollution sources or by dilution, reaction, etc. that enter in the middle.

[0030] Among them, preprocessing includes: Characteristic peak extraction: Extract information such as the position and intensity of characteristic peaks from characteristic spectra. Characteristic peaks can represent specific pollutants or pollution sources.

[0031] Characteristic peak comparison: Compare the characteristic peaks of different water system branches and analyze the similarities and differences of the characteristic peaks. Similar characteristic peaks may indicate the same pollution source, while different characteristic peaks may indicate different pollution sources or a mixture of pollution sources.

[0032] Source tracing analysis: Based on the comparison results of characteristic peaks, the transmission path and impact range of the pollution source can be determined. The intensity changes of characteristic peaks can reflect the dilution and diffusion of the pollution source in the water system.

[0033] The transfer path analysis process of traceable pollutants is as follows: like Figure 2 As shown, Step a: Based on the similarity analysis between the branch trunk road and the main branch road, the similarity size and distribution of the main trunk road to the main branch road are compared; Step b, obtaining the influence ratio of multiple trunk roads on the main branch road under different spectral characteristics, selecting the trunk road with the largest influence ratio of the pollution source type corresponding to the spectral characteristics and tracing the source upward; Step c: In the process of tracing back to the source, continuously loop step b to output the transmission path of the pollution source in the water system.

[0034] The process of pollutant diffusion trend analysis is as follows: Step A, obtaining the corresponding transmission path of the pollution source between the trunk road and the branch road, and the corresponding sampling point location; Step B, collecting and analyzing chemical characteristic data according to the water sample sampling point where the pollutants in the last branch of the transmission path are located; Step C: output the pollutant proportion of the corresponding water body characteristics in the next branch according to the chemical characteristic analysis results and water body characteristics; Step D: evaluate the time period for the next branch to reach the pollutant accumulation index based on the water body characteristics, and while outputting the numerical trend of the pollutant accumulation to the threshold index over time, monitor whether the downstream water body branch has other branches that merge in the middle; Step E: adjusting the numerical trend under the interference of water characteristics according to the pollutant spectral characteristic data and chemical characteristic data of the downstream water body branch.

[0035] In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are only illustrative, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0036] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0037] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0038] Those skilled in the art will appreciate that the embodiments of the present invention may be provided as methods or systems. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects.

[0039] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0040] The above are only embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.

[0041] Those skilled in the art will readily appreciate other embodiments of the present invention after considering the invention disclosed herein in the specification and examples. The present invention is intended to cover any variations, uses or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art that are not disclosed by the present invention. The specification and examples are to be considered exemplary only, and the true scope and spirit of the present invention is indicated by the claims above.

[0042] It should be understood that the present invention is not limited to the exact construction that has been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A method for analyzing a water quality fingerprint database based on feature recognition, characterized in that: include: Collect three-dimensional characteristic spectrum data of water samples from multiple nodes upstream and downstream, and obtain the key features of the characteristic spectrum. Key Features Including characteristic peak position, intensity and regional intensity integration; Collect chemical and biological characteristics of water samples at corresponding node locations; Cross-compare and sort the characteristic surface information of different upstream and downstream under the characteristic peak full library data, obtain the pollutant characteristics under different intensities, and identify the type of pollution source based on the pollutants; The characteristic surface information of water samples reflecting different nodes is sorted and the chemical feature analysis results are called. The output includes the chemical feature sorting of pH value, chemical oxygen demand, biochemical oxygen demand and total organic carbon. The first sequence sampling point position of the chemical feature sorting corresponding to the spectral feature that identifies the pollution source type is selected as the pollution source, and the biological feature analysis at the pollution source is based on whether it meets the identification results.

2. The method for analyzing a water quality fingerprint database based on feature recognition according to claim 1 is characterized in that: The cross-comparison and sorting of characteristic surface information of different upstream and downstream under the characteristic peak full library data obtains pollutant characteristics under different intensities, and identifies the type of pollution source according to the pollutants, including: Obtain the characteristic intensity distribution of water samples at multiple upstream and downstream nodes within the wavelength range of excitation light and emission light; Extract characteristic surface information of water samples; According to the characteristic surface information, the Euclidean distance or Pearson correlation coefficient algorithm is selected to calculate the similarity between the characteristic surfaces of different water system branches; According to the size and distribution of similarity, the transmission path and impact range of pollution sources in the water system are analyzed.

3. The method for analyzing a water quality fingerprint database based on feature recognition according to claim 2 is characterized in that: According to the size and distribution of similarity, the transmission path and impact range of the pollution source in the water system are analyzed, and the specific analysis process includes the following: The water system is divided into trunks and branches, and the trunk includes at least one trunk and the branch includes at least one branch. The water flows from the trunk to the branch along the direction of the water flow. The trunk and the branch trunk are distinguished according to the water flow at the intersection of the river network, and the main branch and the branch branch are distinguished according to the water flow at the outflow intersection. The trunk with the largest water flow is selected as the trunk, and the branch with the largest water flow is selected as the main branch; Based on the similarity analysis between the branch roads and the main roads, the similarity and distribution of the main roads to the main roads are compared to obtain the influence ratio of multiple roads on the main roads under different spectral characteristics, and the road with the largest influence ratio of the pollution source type corresponding to the spectral characteristics is selected to trace the source upward; In the process of tracing back to the source, the similarity analysis between the branch trunk and the main branch is continued, and the transmission path of the pollution source in the water system is output.

4. The method for analyzing a water quality fingerprint database based on feature recognition according to claim 3 is characterized in that: In the similarity analysis, the similarities between branch roads and main roads under different characteristic peaks are determined. The branch roads that are different from the main roads are selected and associated with the corresponding pollution source types. The mixture of characteristic peaks corresponding to different pollution sources affecting the main roads is continuously separated. The influence ratio of multiple roads on the characteristic peaks corresponding to different pollution sources is obtained. Each time, the road with the largest influence ratio and the second largest road are selected for upward tracing.

5. The method for analyzing a water quality fingerprint database based on feature recognition according to claim 4 is characterized in that: Also includes: Sampling and characteristic spectrum analysis of water samples at upstream and downstream monitoring points are performed based on timestamp nodes; Calculate the similarity between nodes with different timestamps and analyze the transmission and change trend of pollution sources over time.

6. The method for analyzing a water quality fingerprint database based on feature recognition according to claim 5 is characterized in that: The calculation of the similarity between nodes with different timestamps and the analysis of the transmission and change trend of pollution sources over time specifically include: Obtain the corresponding transmission path of the pollution source between the trunk road and the branch road, the corresponding sampling point location, analyze the timestamp association information of the next sampling point location under the influence of the water flow velocity, and verify the traceability data results of multiple nodes under the corresponding transmission path based on the timestamp association information. If the data of one node does not match the traceability data results under the transmission path, then correct the path to other trunk roads under the corresponding timestamp that meet the traceability data results.

7. The method for analyzing a water quality fingerprint database based on feature recognition according to claim 5 is characterized in that: The sampling and characteristic spectrum analysis of water samples at upstream and downstream monitoring points based on the timestamp nodes also includes the verification of the sampling timestamps of the water samples that determine the transmission path: Match the sampling timestamps of water samples along the traceability path to determine whether they meet the pollutant transfer speed under the influence of water flow rate; When the transfer time corresponding to the pollutant transfer speed from the trunk to the branch is consistent with the water sample sampling timestamp under the output transfer path and is within the influence range of the system error, the verification is successful; If the verification fails, the water sample sampling timestamp that meets the influence of water flow velocity is selected to re-analyze the transmission path at the trunk and branch locations of the corresponding nodes.

8. The method for analyzing a water quality fingerprint database based on feature recognition according to claim 6 is characterized in that: Chemical characteristic data is collected and analyzed based on the water sample sampling points where the pollutants in the last branch of the transmission path are located. Based on the chemical characteristic analysis results and the water body characteristics, the proportion of pollutants in the next branch corresponding to the water body characteristics is output. At the same time, the time period for the next branch to reach the pollutant accumulation index is evaluated based on the water body characteristics.

9. The method for analyzing a water quality fingerprint database based on feature recognition according to claim 8 is characterized in that: According to the type of pollution source, the water body characteristics that affect the spread of pollution sources include: water flow velocity, flow direction, water depth, water body shape, water body temperature and density, and the numerical trend of the accumulation of pollution sources to the threshold index over time under the influence of water body characteristics is output.

10. The method for analyzing a water quality fingerprint database based on feature recognition according to claim 9, characterized in that: While outputting the numerical trend of the pollutant accumulation to the threshold index over time, the downstream water body branch is monitored to see whether there are other branches merging in the middle, and the numerical trend is adjusted under the interference of water body characteristics based on the spectral characteristic data and chemical characteristic data of the pollutants in the downstream water body branch.

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