An analysis method for a water quality fingerprint database based on feature recognition

Abstract

The present invention belongs to the technical field of water pollutant source tracing, and specifically discloses an analysis method for a water quality fingerprint database based on feature recognition, which relates to the field of water pollutant source tracing. The method includes cross-comparing and sorting the characteristic surface information of different upstream and downstream under the full library data of characteristic peaks to obtain pollutant characteristics at different intensities, and identifying the type of pollution source according to the differences in pollutants; sorting and calling the chemical characteristic analysis results of the characteristic surface information of water samples reflecting different nodes, outputting the chemical characteristic sorting, selecting the position of the sampling point of the first sequence of chemical characteristics corresponding to the spectral characteristics for identifying the type of pollution source as the pollution source, and analyzing whether the biological characteristics at the pollution source conform to the identification results. The present invention efficiently traces water pollutants, outputs an accurate pollutant transfer path, evaluates the corresponding pollutant types at the same time, and gives the corresponding pollutant diffusion trend, providing powerful data for subsequent 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:

[0005] Collect three-dimensional characteristic spectrum data of water samples from multiple nodes upstream and downstream, and obtain the key features of the characteristic spectrum.

[0006] Key features include characteristic peak position, intensity, and regional intensity integral;

[0007] Collect chemical and biological characteristics of water samples at corresponding node locations;

[0008] 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;

[0009] 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.

[0010] 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:

[0011] Obtain the characteristic intensity distribution of water samples at multiple upstream and downstream nodes within the wavelength range of excitation light and emission light;

[0012] Extract characteristic surface information of water samples;

[0013] Select the Euclidean distance or Pearson correlation coefficient algorithm according to the feature surface information to calculate the similarity between the feature surfaces of different water system branches;

[0014] According to the magnitude and distribution of the similarity, analyze the transmission path and influence range of the pollution source in the water system.

[0015] Furthermore, the analysis of the transmission path and influence range of the pollution source in the water system according to the magnitude and distribution of the similarity specifically includes the following analysis process:

[0016] The water system is divided into main roads and branch roads. The main road includes at least one main road, and the branch road includes at least one branch road. Along the water flow direction, it flows from the main road to the branch road. The main road and branch main roads are distinguished according to the water flow volume at the confluence of the river network, and the main branch road and branch branch road are distinguished according to the water flow volume flowing out of the confluence. Select the main road with the largest water flow volume as the main road, and select the branch road with the largest water flow volume as the main branch road;

[0017] Based on the similarity analysis between the branch main road and the main branch road, compare the magnitude and distribution of the similarity between the main road and the main branch road, obtain the influence ratios of multiple main roads on the main branch road under different spectral characteristics, and select the main road with the largest influence ratio of the pollution source type corresponding to the spectral characteristic to trace back upstream;

[0018] During the process of tracing back upstream, continue the similarity analysis between the branch main road and the main branch road, and output the transmission path of the pollution source in the water system.

[0019] Furthermore, in the similarity analysis, when the similarity between the branch main road and the main road corresponding to different characteristic peaks is selected, select the branch main road corresponding to the difference from the main road and associate the corresponding pollution source type, and continuously separate the mixture of different pollution source corresponding characteristic peaks affecting the main branch road, obtain the influence ratios of multiple main roads on different pollution source corresponding characteristic peaks, and each time select the main road with the largest influence ratio and the second largest main road for tracing back upstream.

[0020] Furthermore, sample and perform characteristic spectral analysis on the water body samples at the upstream and downstream monitoring points according to the timestamp nodes;

[0021] Calculate the similarity between different timestamp nodes, and analyze the transmission and change trend of the pollution source over time.

[0022] Furthermore, the calculation of the similarity between different timestamp nodes and the analysis of the transmission and change trend of the pollution source over time specifically include:

[0023] Obtain the corresponding transfer paths of pollution sources between arterial roads and branch roads, the corresponding sampling point positions, analyze the timestamp correlation information for reaching the next sampling point position under the influence of water flow velocity, and verify the traceability data results of multiple nodes under the corresponding transfer paths according to the timestamp correlation information. If the data of one node does not match the traceability data results under the transfer path, then correct the path to other arterial roads that conform to the traceability data results at the corresponding timestamp.

[0024] Further, the sampling and characteristic spectral analysis of water body samples at upstream and downstream monitoring points based on timestamp nodes also includes the verification of the sampling timestamps of water body samples for the determined transfer paths:

[0025] Match the sampling timestamps of water body samples under the traced transfer paths to determine whether they conform to the pollutant transfer speed under the influence of water body velocity;

[0026] When the transfer time of the pollutant corresponding transfer speed from the arterial road to the branch road conforms to the sampling timestamp of the water body sample under the output transfer path and is within the influence range of the system error, the verification is successful;

[0027] If the verification fails, select the sampling timestamp of the water body sample that conforms to the influence of water body velocity and re - conduct the transfer path analysis at the positions of the arterial road and branch road where the corresponding node is located.

[0028] Further, collect and analyze chemical characteristic data based on the sampling point of the water body sample where the pollutant is located at the end - point branch of the transfer path. According to the chemical characteristic analysis results and water body characteristics, output the pollutant proportion in the next branch corresponding to the water body characteristics, and at the same time evaluate the time period for the next branch to reach the pollutant accumulation index according to the water body characteristics.

[0029] Further, according to the water body characteristics corresponding to the pollution source type that affect the diffusion of the pollution source, including: water flow velocity, flow direction, water depth, water body shape, water body temperature and density, output the numerical trend of the pollution source accumulating to the threshold index over time under the influence of water body characteristics.

[0030] Further, while outputting the numerical trend of the pollutant accumulating to the threshold index over time, monitor whether there are other branches that merge midway in the downstream water body branch, and at the same time adjust the numerical trend according to the pollutant spectral characteristic data and chemical characteristic data of the downstream water body branch under the interference of water body characteristics;

[0031] An analysis method of a water quality fingerprint database based on feature recognition provided by the present invention uses water body characteristics combined with spectral characteristics, chemical characteristics and biological characteristics to efficiently trace the water body pollutants, output accurate pollutant transfer paths, evaluate the corresponding pollutant types at the same time, and give the corresponding pollutant diffusion trends, providing powerful data for subsequent pollutant treatment. Description of the Drawings

[0032] The accompanying drawings described herein are used to provide a further understanding of the embodiments of the present invention, form a part of the present invention, and do not limit the embodiments of the present invention. In the drawings:

[0033] Figure 1 It is a flowchart of an analysis method for a water quality fingerprint database based on feature recognition provided for an exemplary embodiment of the present invention.

[0034] Figure 2 It is a flowchart of an analysis method for the pollutant tracing transfer path of a water quality fingerprint database based on feature recognition provided for an exemplary embodiment of the present invention.

[0035] Figure 3 It is a flowchart of an analysis method for the pollutant diffusion trend of a water quality fingerprint database based on feature recognition provided for an exemplary embodiment of the present invention. Detailed implementation manners

[0036] Here, the exemplary embodiments will be described in detail, and the examples are shown in the accompanying drawings. When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementation manners described in the following exemplary embodiments do not represent all implementation manners consistent with the present invention. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0037] First, the nouns involved in the present invention are explained:

[0038] The water quality characteristic fingerprint database is a system for storing and managing the characteristic spectral data of water body samples. Its core is to obtain the "water quality fingerprint" of water body 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.

[0039] The water quality characteristic fingerprint database involves the comparative analysis function of public databases and private databases, and also includes the establishment process and update mechanism, which can provide relevant information for the specificity and tracing of the water quality characteristic fingerprint database;

[0040] In the prior art, the construction process of the water quality characteristic fingerprint database is as follows: collect water body samples from different pollution sources (such as industrial wastewater, domestic sewage, etc.), obtain their three-dimensional characteristic spectra through a characteristic spectrometer, extract the key characteristics of the characteristic spectra, such as the position and intensity of characteristic peaks, input the collected and extracted characteristic fingerprint data into the database, and conduct classification management. Then, by comparing the characteristic fingerprints of unknown water body samples with the known pollution source fingerprints in the database, quickly determine the suspected pollution source, and regularly update the database to ensure the accuracy and timeliness of the data, especially when the characteristic fingerprints of the pollution source change significantly.

[0041] The specific application scenario of the present invention is based on the analysis of the water quality characteristic fingerprint database.

[0042] In the present invention, the source of pollutants can be quickly identified through spectral characteristics, the nature of pollutants can be further verified through chemical characteristics, and biological characteristics can be used to evaluate the ecological health status. Combining the three can form database information for comprehensively evaluating water quality. Since the precise quantitative information provided by chemical characteristics takes a long time, the influence degree of pollutants is distinguished through assisted analysis of biological characteristics, and then the spectral characteristics are used to identify and trace the types of pollutants.

[0043] An analysis method of a water quality fingerprint database based on feature recognition provided by the present invention aims to solve the above technical problems of the prior art.

[0044] The following will specifically describe the technical solution of the present invention and how the technical solution of the present invention solves the above technical problems with specific embodiments. These several 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 with reference to the accompanying drawings.

[0045] Embodiment 1:

[0046] Water pollution sampling and analysis are carried out on a river and a lake. Multiple water body sample sampling points are set, and the sampling frequency of the sampling points is set according to the runoff velocity. Multiple dense sampling areas are set for the sampling points with fast runoff velocity, three-dimensional characteristic spectral data are obtained, and the corresponding water quality fingerprint database is generated. The water body characteristics in the sampling area are investigated synchronously, and the following pollutant source tracing and transfer path analysis and diffusion trend analysis are carried out:

[0047] Cross-compare and sort the characteristic surface information under the full library data of characteristic peaks at different upstream and downstream, obtain the pollutant characteristics at different intensities, and identify the source type of the pollution source according to the pollutant differences;

[0048] Sort and call the chemical characteristic analysis results of the characteristic surface information of the water body samples reflecting different nodes, output the chemical characteristic sorting including pH value, chemical oxygen demand, biochemical oxygen demand and total organic carbon, select the position of the first sequence sampling point of the chemical characteristic sorting corresponding to the spectral characteristic of the identified pollution source type as the pollution source, and analyze whether the biological characteristics at the pollution source conform to the identification results;

[0049] Among them, the characteristic surface information can be understood as a multi-dimensional characteristic expression constructed from spectral data in the analysis of water body samples. Specifically, the characteristic surface information can be regarded as a surface formed by the three-dimensional fluorescence spectral data of water body samples at different excitation wavelengths and emission wavelengths. This surface reflects the spectral response characteristics of water body samples under different wavelength combinations.

[0050] During the analysis process, the characteristic surface information can help the present invention more comprehensively understand the spectral characteristics of water body samples. By comparing the characteristic surface information of different nodes, the present invention can identify the possible pollutant characteristics in water body samples. This comparative analysis can reveal the pollution situation of water body samples at different locations, thereby helping the present invention identify the pollution sources.

[0051] The characteristic surface information can also be combined with the chemical and biological characteristics of water body samples for more in-depth analysis. For example, by comparing the characteristic surface information with chemical characteristics such as pH value, chemical oxygen demand, biochemical oxygen demand, and total organic carbon, the present invention can more accurately identify the type and location of pollution sources. At the same time, by combining biological characteristic analysis, the present invention can further verify the accuracy of the identification results.

[0052] In summary, the characteristic surface information plays an important role in the analysis of water body samples. It provides a multi-dimensional and comprehensive analysis means for the present invention, which helps the present invention more comprehensively and accurately understand the pollution situation of water bodies and identify pollution sources.

[0053] As Figure 1 shown,

[0054] Step 1: Obtain the characteristic intensity distribution of water body samples at multiple upstream and downstream nodes within the excitation light and emission light wavelength ranges;

[0055] Step 2: Extract the characteristic surface information of water body samples;

[0056] 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;

[0057] Step 4: Analyze the transmission path and influence range of pollution sources in the water system according to the magnitude and distribution of the similarity.

[0058] In step 3, appropriate algorithms (such as Euclidean distance, Pearson correlation coefficient, etc.) are used to calculate the similarity between the characteristic surfaces of different water system branches. The calculation of similarity can be based on the original data of the entire surface or on the extracted key characteristic parameters. According to the magnitude and distribution of the similarity, analyze the transmission path and influence range of the pollution source in the water system. 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 occur during the intermediate confluence.

[0059] Among them, the preprocessing includes:

[0060] Characteristic peak extraction: Extract information such as the position and intensity of characteristic peaks from the characteristic spectrum. Characteristic peaks can represent specific pollutants or pollution sources.

[0061] Characteristic peak comparison: Compare the characteristic peaks of different water system branches and analyze the similarity and difference 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.

[0062] Traceability analysis: Based on the comparison results of characteristic peaks, judge the transmission path and influence range of the pollution source. The change in the intensity of characteristic peaks can reflect the dilution and diffusion of the pollution source in the water system.

[0063] The process of analyzing the transmission path of the traceable pollutant is as follows:

[0064] As Figure 2 shown,

[0065] Step a: Based on the similarity analysis between the branch trunk road and the main branch road, compare the magnitude and distribution of the similarity between the main trunk road and the main branch road;

[0066] Step b: Obtain the influence ratios of multiple trunk roads on the main branch road under different spectral characteristics, and select the trunk road with the largest influence ratio where the pollution source type corresponding to the spectral characteristics is located to trace upstream;

[0067] Step c: During the process of tracing upstream, continuously loop step b to output the transmission path of the pollution source in the water system.

[0068] The process of analyzing the diffusion trend of pollutants is as follows:

[0069] Step A: Obtain the corresponding transmission path of the pollution source between the trunk road and the branch road and the corresponding sampling point positions;

[0070] Step B: Based on the water sample sampling point where the pollutant is located at the end branch of the transmission path, collect and analyze the chemical characteristic data.

[0071] Step C: Based on the chemical characteristic analysis results and the water body characteristics, output the proportion of pollutants in the next branch corresponding to the water body characteristics.

[0072] Step D: Based on the water body characteristics, evaluate the time period for the next branch to reach the pollutant accumulation index. While outputting the numerical trend of the pollutant accumulation to the threshold index over time, monitor whether there are other branches that merge midway in the downstream water body branch.

[0073] Step E: Adjust the numerical trend based on the pollutant spectral characteristic data and chemical characteristic data of the downstream water body branch under the interference of the water body characteristics.

[0074] In 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 merely illustrative. For example, the division of the modules is only a logical function division. In actual implementation, there can be other division methods. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0075] The modules described as separate components may or may not be physically separated. The components shown as modules may or may not be physical modules, that is, they can be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0076] In addition, in each embodiment of the present invention, the functional modules can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module. The above integrated modules can be implemented in the form of hardware or in the form of a combination of hardware and software functional modules.

[0077] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods or systems. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects.

[0078] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover a non-exclusive inclusion, so that a process, method, commodity or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, commodity or device. Without further limitation, the element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, commodity or device including the element.

[0079] The above are only embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, various modifications and variations can be made to the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the scope of the claims of the present invention.

[0080] After considering the present invention disclosed in the specification and the embodiments, those skilled in the art will readily conceive of other embodiments of the present invention. The present invention is intended to cover any variations, uses, or adaptations of the present invention, which follow the general principles of the present invention and include the common general knowledge or conventional technical means in the technical field not disclosed in the present invention. The specification and the embodiments are only regarded as exemplary, and the true scope and spirit of the present invention are pointed out by the claims above.

[0081] It should be understood that the present invention is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present invention is only limited by the appended claims.

Claims

1. An analysis method for a water quality fingerprint database based on feature recognition, characterized in that, Including: Collecting the three-dimensional characteristic spectral data of water samples at multiple upstream and downstream nodes, and obtaining the key characteristics of the characteristic spectra, Key characteristics including the characteristic peak position, intensity, and regional intensity integration; Collecting the chemical and biological characteristics of water samples at the corresponding node positions; Cross-comparing and sorting the characteristic surface information of different upstream and downstream under the full library data of characteristic peaks, obtaining the pollutant characteristics at different intensities, and identifying the source type according to the pollutant differences, including: obtaining the characteristic intensity distribution of water samples at multiple upstream and downstream nodes within the excitation light and emission light wavelength ranges, extracting the characteristic surface information of water samples, selecting 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, and analyzing the transmission path and influence range of the pollution source in the water system according to the magnitude and distribution of the similarity. Specifically, the analysis process is as follows: The water system is divided into main roads and branch roads, and there is at least one main road and at least one branch road. Along the water flow direction, it flows from the main road to the branch road. The main road and branch main road are distinguished according to the water flow volume at the confluence of the river network, and the main branch road and branch branch road are distinguished according to the water flow volume flowing out of the confluence. The main road with the largest water flow volume is selected as the main road, and the branch road with the largest water flow volume is selected as the main branch road. According to the similarity analysis between the branch main road and the main branch road, compare the magnitude and distribution of the similarity between the main road and the main branch road, obtain the influence ratio of multiple main roads on the main branch road under different spectral characteristics, select the main road with the largest influence ratio of the pollution source type corresponding to the corresponding spectral characteristic to trace upstream, and during the upstream tracing process, continuously perform the similarity analysis between the branch main road and the main branch road, and output the transmission path of the pollution source in the water system; Sorting and calling the chemical characteristic analysis results of the characteristic surface information of water samples reflecting different nodes, outputting the chemical characteristic sorting including pH value, chemical oxygen demand, biochemical oxygen demand, and total organic carbon, selecting the sampling point position of the first sequence of the chemical characteristics corresponding to the spectral characteristics identifying the pollution source type as the pollution source, and analyzing whether the biological characteristics at the pollution source meet the identification results.

2. The analysis method of a water quality fingerprint database based on feature recognition according to claim 1, characterized in that, When in the similarity analysis, the similarity between the branch main road and the main road corresponding to different characteristic peaks, select the branch main road corresponding to the difference from the main road and associate the corresponding pollution source type, continuously separate the mixture of different pollution source corresponding characteristic peaks affecting the main branch road, obtain the influence ratio of multiple main roads on the characteristic peaks corresponding to different pollution sources, and each time select the main road with the largest influence ratio and the second largest main road to trace upstream.

3. The analysis method of a water quality fingerprint database based on feature recognition according to claim 2, characterized in that, It also includes: Sampling and performing characteristic spectral analysis on water samples at upstream and downstream monitoring points according to the timestamp nodes; Calculating the similarity between different timestamp nodes, and analyzing the transmission and change trend of the pollution source over time.

4. The analysis method of a water quality fingerprint database based on feature recognition according to claim 3, wherein The calculation of the similarity between different timestamp nodes and the analysis of the transmission and change trend of the pollution source over time specifically include: Obtain the corresponding transfer paths of pollution sources between arterial roads and branch roads, the positions of corresponding sampling points, analyze the timestamp correlation information for reaching the next sampling point position under the influence of water flow velocity, and verify the traceability data results of multiple nodes under the corresponding transfer paths according to the timestamp correlation information. If the data of one node does not match the traceability data results under the transfer path, then correct the path to other arterial roads that conform to the traceability data results at the corresponding timestamp.

5. The analysis method of a water quality fingerprint database based on feature recognition according to claim 3, characterized in that Sampling and characteristic spectral analysis of water body samples at upstream and downstream monitoring points according to the timestamp nodes also include the verification of the sampling timestamps of water body samples for the determined transfer paths: Match the sampling timestamps of water body samples under the traced transfer paths to determine whether they conform to the pollutant transfer speed under the influence of water flow velocity; When the transfer time of the pollutant corresponding transfer speed from the arterial road to the branch road conforms to the sampling timestamp of the water body sample under the output transfer path and is within the influence range of the system error, the verification is successful; If the verification fails, select the sampling timestamp of the water body sample that conforms to the influence of water flow velocity and re - conduct the transfer path analysis at the positions of the arterial road and branch road where the corresponding node is located.

6. The analysis method of a water quality fingerprint database based on feature recognition according to claim 4, characterized in that Collect and analyze chemical characteristic data based on the sampling point of the water body sample where the pollutant is located at the end - point branch of the transfer path. According to the chemical characteristic analysis results and water body characteristics, output the proportion of pollutants in the next branch corresponding to the water body characteristics. At the same time, evaluate the time period for the next branch to reach the pollutant accumulation index based on the water body characteristics.

7. The analysis method of a water quality fingerprint database based on feature recognition according to claim 6, characterized in that, According to the water body characteristics corresponding to the pollution source type that affect the diffusion of the pollution source, including: water flow velocity, flow direction, water depth, water body shape, water temperature and density, output the numerical trend of the pollution source accumulating to the threshold index over time under the influence of water body characteristics.

8. The analysis method of a water quality fingerprint database based on feature recognition according to claim 7, characterized in that, While outputting the numerical trend of the pollutant accumulating to the threshold index over time, monitor whether there are other branches that merge midway in the downstream water body branch, and at the same time adjust the numerical trend according to the pollutant spectral characteristic data and chemical characteristic data of the downstream water body branch under the interference of water body characteristics.

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