River channel water quality detection and analysis method and device for river environment protection

Abstract

The invention relates to the technical field of water quality detection, in particular to a riverway water quality detection analysis method and device for river environment protection, and the method comprises the following steps: pre-treating a collected riverway water sample, equally dividing the pre-treated riverway water sample into two parts, and measuring the turbidity of any one of the riverway water samples, performing water quality absorbance detection on the other part of the river channel water sample; forming a water sample absorbance sequence by the absorbance of all wavelengths of the river channel water sample, and correspondingly, obtaining a pure water absorbance sequence of an equal-volume pure water sample; obtaining a calibration water sample sequence by utilizing the difference between the water sample absorbance sequence and the pure water absorbance sequence; determining an absorbance correction value of each preset water quality influence element; and obtaining the content of each preset water quality influencing element in the river water sample by utilizing the absorbance correction value and combining with the Lambert-Beer law. Therefore, the accuracy of river water quality detection is improved.

Description

Technical Field

[0001] This application relates to the field of water quality testing technology, specifically to a method and apparatus for river water quality testing and analysis for river environmental protection. Background Technology

[0002] River water quality monitoring is a crucial line of defense for maintaining ecological security and public health. Excessive levels of heavy metals and accumulation of organic pollutants caused by industrial wastewater and agricultural non-point source pollution threaten river life, severely damaging biodiversity and, in addition to disrupting aquatic ecosystems, threatening human health through the food chain.

[0003] Currently, river water quality testing is often conducted using spectrometers. However, impurities are usually present in river water, interfering with the quality of spectral data and causing deviations in the measured spectral data, resulting in low accuracy in river water quality testing. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide a method and apparatus for river water quality detection and analysis for river environmental protection. The specific technical solution adopted is as follows: In a first aspect, embodiments of this application provide a method for river water quality detection and analysis for river environmental protection, the method comprising the following steps: After preprocessing, the collected river water samples were divided into two parts. The turbidity of one part of the river water sample was measured, and the absorbance of the remaining part of the river water sample was tested. The absorbance of all wavelengths of the river water sample was used to form the absorbance sequence of the water sample. Correspondingly, the absorbance sequence of pure water of the same volume was obtained. By utilizing the difference between the absorbance sequence of the water sample and the absorbance sequence of pure water, a calibration water sample sequence is obtained; based on the changing trend of all data in the calibration water sample sequence and the number of peak points in the calibration water sample sequence, combined with the turbidity, the sparsity parameter of the river water sample is determined. Combining the aforementioned sparsity parameters with the sparse principal component analysis algorithm, we screened the characteristic wavelengths of river water samples; and determined the characteristic wavelengths of each preset water quality influencing element using Lambert-Beer's law through controlled variable experiments. The differences between the characteristic wavelengths of each preset water quality influencing element and the characteristic wavelengths of the river water sample in the corresponding elements of the calibrated water sample sequence were analyzed. Combined with the turbidity, the absorbance correction value of each preset water quality influencing element was determined. Using the absorbance correction value and the Lambert-Beer law, the content of each preset water quality influencing element in the river water sample is obtained.

[0005] In one embodiment, the pretreatment includes adding a digesting agent dropwise to a river water sample and stirring.

[0006] In one embodiment, the calibration water sample sequence consists of the difference between the absorbance of the same wavelength in the water sample absorbance sequence and the absorbance of pure water.

[0007] In one embodiment, determining the sparsity parameter includes: Curve fitting is performed on all data in the calibration water sample sequence to obtain the goodness of fit. The sparsity parameter is positively correlated with the goodness of fit and negatively correlated with the number of peak points and the turbidity.

[0008] In one embodiment, the sparsity parameter is calculated as follows: In the formula, For the sparsity parameters of river water samples, The goodness of fit is... The turbidity of the river water sample. To determine the number of peak points in the water sample sequence, ln() is a logarithmic function with the natural constant as the base, and norm() is a normalization function.

[0009] In one embodiment, determining the characteristic wavelengths of each preset water quality influencing element using Beer-Lambert's law through a controlled variable experiment includes: For each preset water quality influencing element, a preset concentration of each water quality influencing element is added to pure water. The absorbance of the pure water with each preset concentration is measured. Based on the absorbance measurement results of all preset concentrations, the characteristic wavelength of each preset water quality influencing element is determined.

[0010] In one embodiment, determining the absorbance correction value includes: The turbidity is mapped to a preset range, and the characteristic wavelengths of all characteristic wavelengths of the river water sample, excluding the characteristic wavelengths of all preset water quality influencing elements, are recorded as turbidity wavelengths. For any preset water quality influencing element, a preset number of turbidity wavelengths adjacent to the characteristic wavelength of the preset water quality influencing element are obtained and denoted as near-turbidity wavelengths. The reciprocal of the distance between each near-turbidity wavelength and the characteristic wavelength of the preset water quality influencing element is used as a weight, and the data corresponding to all near-turbidity wavelengths in the calibration water sample sequence are weighted and summed. The absorbance correction value of any preset water quality influencing element is inversely proportional to the weighted summation result and the turbidity mapped to the preset range, and is directly proportional to the characteristic wavelength of any preset water quality influencing element in the data corresponding to the calibrated water sample sequence.

[0011] In one embodiment, the absorbance correction value of any preset water quality influencing element is determined as follows: The product of the weighted summation result and the turbidity mapped to the preset interval is calculated. The absorbance correction value of any preset water quality influencing element is the difference between the data corresponding to the characteristic wavelength of any preset water quality influencing element in the calibration water sample sequence and the product.

[0012] In one embodiment, during the process of obtaining the content of each preset water quality influencing element in the river water sample, the optical path length in the Lambert-Beer law is determined by the controlled variable experiment.

[0013] Secondly, embodiments of this application also provide a river water quality detection and analysis device for river environmental protection, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of any of the methods described above.

[0014] This application has at least the following beneficial effects: This application preprocesses collected river water samples and divides them into two equal parts. The turbidity of one sample is measured, while the absorbance of the remaining sample is detected. This dual-sample parallel processing strategy improves the efficiency of water sample detection. The absorbance of all wavelengths from the river water samples is used to create a water sample absorbance sequence, and correspondingly, a pure water absorbance sequence for an equal volume of pure water is obtained. The difference between the water sample absorbance sequence and the pure water absorbance sequence is used to obtain a calibration water sample sequence. The pure water absorbance sequence eliminates interference from suspended particulate scattering, improving the detection sensitivity for low-concentration pollutants in the river water. Based on the changing trends of all data in the calibration water sample sequence and the number of peak points in the sequence, combined with the turbidity, the sparsity parameter of the river water sample is determined. This sparsity parameter is then combined with sparse principal component analysis to calculate... This paper describes a method for screening characteristic wavelengths in river water samples. A turbidity-compensated sparse principal component analysis algorithm improves the accuracy of the screened characteristic wavelengths, effectively solving the cross-interference problem caused by overlapping heavy metal absorption peaks in traditional spectral analysis. The characteristic wavelengths of each preset water quality influencing element are determined using Lambert-Beer's law through controlled variable experiments. The differences between the characteristic wavelengths of each preset water quality influencing element and the corresponding elements in the calibrated water sample sequence are analyzed. Combined with the turbidity, absorbance correction values ​​for each preset water quality influencing element are determined. Using the absorbance correction values ​​and Lambert-Beer's law, the content of each preset water quality influencing element in the river water sample is obtained. This application significantly improves the accuracy and efficiency of water quality detection through the synergistic optimization of multi-dimensional spectral correction and intelligent feature extraction, enabling rapid and accurate water quality detection in complex water bodies. Attached Figure Description

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

[0016] Figure 1 A flowchart illustrating the steps of a river water quality detection and analysis method for river environmental protection, provided in one embodiment of this application; Figure 2 Flowchart for determining the content of elements affecting water quality. Detailed Implementation

[0017] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a river water quality detection and analysis method and apparatus for river environmental protection proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0019] The following description, in conjunction with the accompanying drawings, details the specific scheme of a river water quality detection and analysis method and apparatus for river environmental protection provided in this application.

[0020] Please see Figure 1 The diagram illustrates a flowchart of a method for river water quality detection and analysis for river environmental protection, provided in one embodiment of this application. The method includes the following steps: S1. After preprocessing the collected river water samples, they are divided into two parts. The turbidity of one part of the river water sample is measured, and the absorbance of the remaining part of the river water sample is detected.

[0021] To achieve water quality monitoring and analysis in rivers, this embodiment deploys water quality monitoring stations at equal intervals along the river's flow direction, based on any river requiring water quality testing. In this embodiment, the distance between two adjacent water quality monitoring stations is 100m. Implementers can set the distance between water quality monitoring stations according to actual conditions; this embodiment does not impose any restrictions. Water samples are extracted from each monitoring station and referred to as river water samples for water quality testing and analysis. This embodiment uses an example of a river water sample extracted from any one monitoring station to illustrate the process, reducing environmental interference and improving the automation and accuracy of water quality testing.

[0022] Specifically, in this embodiment, a water quality sample is extracted from the river using a water pump. The extracted water quality sample is 50 ml. The implementer can set the extraction volume of the water quality sample. The extracted water quality sample is stored in a sampling container. 20 ml of the water quality sample is extracted from the sampling container and taken into the digestion chamber. In the digestion chamber, the water quality sample is heated to a fixed temperature, which is related to the optimal operating temperature of the spectrometer. Typically, the operating temperature of the spectrometer is between 25°C and 40°C. In this embodiment, the water quality sample is heated to 30°C.

[0023] Pretreatment of the heated river water samples in the digestion chamber aims to reduce interference from inorganic elements and organic compounds that may affect the absorption spectrum during spectral detection. Specifically, the pretreatment involves adding a digesting agent dropwise to the river water sample in the digestion chamber. In this embodiment, a 1:1 mixture of FeSO4 and H2O2 is used as the digesting agent. The agent is added dropwise at a rate of 0.1 ml / s, with magnetic stirring performed simultaneously for 20 minutes.

[0024] The pretreated river water samples were divided into two portions, denoted as river water sample X and river water sample Y. Turbidity was measured on either sample, for example, river water sample X. The specific procedure can be found in the standard "Determination of Turbidity in Water Quality - Turbidity Meter Method (HJ 1075-2019)" to obtain the turbidity information of river water sample X. Simultaneously, the absorbance of river water sample Y was measured using a spectrometer.

[0025] S2, the absorbance of all wavelengths of the river water sample is used to form the absorbance sequence of the water sample, and correspondingly, the absorbance sequence of pure water of the same volume is obtained.

[0026] In this embodiment, the absorbance of all wavelengths of the river water sample Y is arranged in ascending order to form a water sample absorbance sequence.

[0027] Water quality testing using spectrometers primarily relies on Beer-Lambert's law, which states that the absorbance of a river water sample at a specific wavelength is positively correlated with the concentration of the substance corresponding to that wavelength and the thickness of the solution. In actual testing, the thickness of the water sample solution is consistent, thus approximating a positive correlation between absorbance and the concentration of the corresponding substance.

[0028] In the process of using spectrometers to detect river water quality samples, the high wavelength resolution results in a high degree of redundancy in the absorbance sequences. This makes the analysis of various elements in the river water samples susceptible to noise interference, leading to poor accuracy in water quality detection. Furthermore, the presence of impurities and other non-dissolved components in the water during the detection process causes inconsistencies in turbidity levels at different times, which also interferes with water quality analysis. Therefore, it is necessary to analyze the absorbance sequences of water samples during the water quality detection process to improve the accuracy of river water quality detection.

[0029] Since H2O constitutes the majority of the water quality during river water quality testing, to reduce equipment deviations during spectrometer operation at different times, while obtaining the absorbance sequence of the river water samples through spectral analysis, an equivalent volume of pure water was pretreated, and its absorbance sequence was also collected. The aim is to reduce the impact of spectrometer operational deviations on river water quality testing.

[0030] S3. By utilizing the difference between the absorbance sequence of the water sample and the absorbance sequence of pure water, a calibration water sample sequence is obtained. Based on the changing trend of all data in the calibration water sample sequence and the number of peak points in the calibration water sample sequence, combined with the turbidity, the sparsity parameter of the river water sample is determined.

[0031] To reduce the impact of equipment operation deviations on water quality testing, the difference between the absorbance of the same wavelength in the water sample absorbance sequence and the pure water absorbance sequence is calculated. The difference is then arranged in order of wavelength to form a calibration water sample sequence.

[0032] Water quality testing primarily focuses on organic and inorganic elements that affect water quality. This embodiment analyzes water quality-influencing elements including chemical oxygen demand (COD), total organic carbon (TOC), ammonia nitrogen (NH3-N), and nitrate (NO3). - Nitrite NO2 -Total phosphorus (TP), lead (Pb), cadmium (Cd), mercury (Hg), and arsenic (As) are all considered. Based on Beer-Lambert's law, each element typically corresponds to a single characteristic wavelength. However, in spectral detection, thousands of wavelengths are often present, leading to wavelength redundancy. Furthermore, impurities in the river water will affect characteristic wavelengths related to turbidity, potentially interfering with the values ​​of these characteristic wavelengths. Therefore, it is necessary to screen all characteristic wavelengths of the current river water sample to reduce interference from redundant information.

[0033] Typically, the selection of characteristic wavelengths for spectral data relies on feature selection parameters. For example, in the Sparse Principal Component Selection (SPCAFS) algorithm, a fixed sparsity parameter is usually set to determine the number of characteristic wavelengths to be selected. However, in water quality testing, different water samples often have different environmental conditions and turbidity levels, resulting in variations in the number and location of characteristic wavelengths in each selection process. Therefore, fixed selection parameters can easily lead to deviations in the selection of characteristic wavelengths.

[0034] Furthermore, the higher the turbidity of the river water sample, the lower the degree of scattering and absorption of light by suspended particles in the river. In normal river water, the turbidity value is generally between 1 and 100 NTU. The higher the value, the higher the content of suspended matter in the river water, and the greater the impact on the water quality spectrum data.

[0035] In an ideal scenario, the spectral data of a water quality sample shows characteristic peaks only at the wavelengths corresponding to the elements affecting water quality, with smooth transitions between these peaks, a small overall number of characteristic peaks, and few outliers. However, when affected by suspended particles in river water, interference occurs between wavelengths in the water quality spectral data, resulting in a significantly larger number of peaks in the calibrated water sample sequence than the number of elements affecting water quality, and a greater overall number of outliers.

[0036] Based on the above analysis, this embodiment calculates the sparsity parameters of the current river water quality samples, specifically as follows: Curve fitting is performed on all data in the calibration water sample sequence using the least squares method to obtain the goodness of fit. A peak point detection algorithm is then used to obtain the peak points in the calibration water sample sequence. The least squares method is a known existing technique, and the implementer can choose other feasible existing curve fitting algorithms. The specific calculation method for the sparsity parameter is as follows: In the formula, For the sparsity parameters of river water samples, The goodness of fit is... Let X be the turbidity of the river water sample. To determine the number of peak points in the water sample sequence, ln() is a logarithmic function with the natural constant as the base, and norm() is a normalization function.

[0037] It should be understood that the more severe the influence of suspended particles in river water, the more significant the influence between different wavelengths during spectral analysis of river water samples. This results in a larger number of outliers in the spectral data, leading to a smaller sparsity parameter value. To better extract feature information from the spectral data, more feature wavelengths need to be selected. Conversely, when the river water quality is good and less affected by suspended particles, the measured spectral data is of higher quality, and fewer feature wavelengths can be used to extract the feature information.

[0038] S4. Combining the sparsity parameters and the sparse principal component analysis algorithm, the characteristic wavelengths of the river water samples are screened; the characteristic wavelengths of each preset water quality influencing element are determined by using Lambert-Beer's law through controlled variable experiments.

[0039] Based on the sparsity parameters of the obtained river water quality samples The calibrated water sample sequence is used as input to the Sparse Principal Component Selection (SPCAFS) algorithm, and the output is the selected characteristic wavelengths of the current river water quality sample. For example, 100 characteristic wavelengths are selected from the original 1900 wavelengths. It should be noted that the number of characteristic wavelengths is determined by the sparsity parameter of the SPCAFS algorithm. Generally, the number of characteristic wavelengths should include and be greater than the number of wavelengths of water quality influencing elements. The SPCAFS algorithm is a well-known existing technology, and its specific process will not be elaborated upon.

[0040] This embodiment uses the Lambert-Beer law to determine the characteristic wavelengths of each water quality influencing element mentioned in step S3 through controlled variable experiments. Specifically, to obtain the characteristic wavelength of total phosphorus (TP), TP solutions with concentrations of 10, 20, 30, 40, 50, 60, 70, 80, 90, and 100 mg / L were prepared in pure water and subjected to spectral detection. The spectral data were obtained, and the characteristic peaks corresponding to TP were obtained based on their positions. The wavelengths corresponding to the characteristic peaks were taken as the characteristic wavelengths of total phosphorus (TP).

[0041] It should be understood that the formula for Lambert-Beer's Law is... In the formula, A is the absorbance, k is the proportionality coefficient, c is the concentration of the measured liquid, and l is the optical path length. The optical path length is a specific parameter used in spectrometer measurements and is generally constant; it can be obtained through the spectrometer's display interface. Based on the known absorbance at different concentrations, the proportionality coefficients of each water quality influencing element can be obtained through fitting. The Lambert-Beer law is a well-known technique, and its specific process will not be elaborated upon.

[0042] It should be noted that when obtaining the characteristic wavelengths of each water quality influencing element, the concentration of the water quality influencing element added to pure water can be set by the implementer according to the actual situation, and this embodiment does not impose any restrictions here.

[0043] S5, analyze the differences between the characteristic wavelengths of each preset water quality influencing element and the characteristic wavelengths of the river water sample in the corresponding elements of the calibrated water sample sequence, and determine the absorbance correction value of each preset water quality influencing element in combination with the turbidity.

[0044] Under ideal water quality conditions, the spectral data of river water samples only contain the characteristic wavelengths of water quality influencing elements. However, in actual water quality testing, the characteristic wavelengths include not only those of water quality influencing elements but also those affected by water turbidity. Due to the scattering of light by suspended particles in the water, other scattered light wavelengths are added to the characteristic wavelengths of water quality influencing elements, causing the measured absorbance of these elements to be greater than the actual absorbance, thus affecting the judgment of actual water quality influencing conditions.

[0045] In this embodiment, the characteristic wavelengths of all characteristic wavelengths of the river water quality sample, excluding the characteristic wavelengths of all water quality influencing elements, are denoted as turbidity wavelengths. For any water quality influencing element, a preset number of turbidity wavelengths adjacent to the characteristic wavelength of the water quality influencing element are obtained and denoted as near-turbidity wavelengths. In this embodiment, the preset number is 10. Implementers can set it according to the actual situation. This embodiment does not impose any restrictions here.

[0046] Generally, for the characteristic wavelengths of various water quality influencing elements in river water samples, the closer they are to the near-turbidity wavelength, the greater the impact on the absorbance of that element. Therefore, this embodiment corrects absorbance by considering the overall turbidity of the water body and the distribution of near-turbidity wavelengths within a local range of the characteristic wavelengths of each water quality influencing element. Specifically: First, the turbidity of river water sample X is proportionally reduced and mapped to a value range of 0 to m, where m ranges from [0.2, 0.5]. A larger value of m indicates a greater impact of turbidity on water quality. In this embodiment, m = 0.2. The implementer can set the value of m within its range. The mapped value of the turbidity of river water sample X is recorded as the turbidity influence coefficient. Second, taking the j-th water quality influencing element as an example, the reciprocal of the distance between each near-turbidity wavelength of the j-th water quality influencing element and its characteristic wavelength is used as a weight. The data corresponding to all near-turbidity wavelengths of the j-th water quality influencing element in the calibration water sample sequence are then weighted and summed. The absorbance correction value for each water quality influencing element in this embodiment is calculated as follows: In the formula, This is the absorbance correction value for the j-th water quality influencing element at its characteristic wavelength. This represents the corresponding data of the characteristic wavelength of the j-th water quality influencing element in the calibration water sample sequence. The turbidity influence coefficient is mentioned above. This is the weighted sum of the j-th water quality influencing element.

[0047] It should be understood that if the content of water quality influencing elements in river water is higher, it indicates that the water quality influencing element is more harmful to the water quality. At this time, the near-turbidity wavelength in the local range of the characteristic wavelength corresponding to the water quality influencing element may cause scattering effect on the characteristic wavelength of the water quality influencing element. Therefore, it is necessary to reduce the influence of near-turbidity wavelength to obtain a more accurate characteristic wavelength of the water quality influencing element.

[0048] It should be noted that the content of every water quality influencing element in river water is not necessarily high. This may result in a negative value for the actual calculated absorbance correction value. In this case, it indicates that the content of the water quality influencing element is small and less affected by turbidity. The absorbance correction value of the water quality influencing element in this case is the corresponding data in the calibration water sample sequence.

[0049] S6. Using the absorbance correction value and the Lambert-Beer law, the content of each preset water quality influencing element in the river water sample is obtained.

[0050] Based on the above steps, the absorbance correction value corresponding to each water quality influencing element analyzed in this embodiment can be obtained. Based on the absorbance correction value of each water quality influencing element and the proportion coefficient of each water quality influencing element obtained in step S4, the content of each water quality influencing element in the river water sample Y can be obtained using the known optical path length and the Lambert-Beer law, thus completing the river water quality detection and analysis. The flowchart for determining the content of water quality influencing elements is as follows. Figure 2 As shown.

[0051] Based on the same inventive concept as the above method, this application embodiment also provides a river water quality detection and analysis device for river environmental protection, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-described methods for river water quality detection and analysis for river environmental protection.

[0052] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments of this specification have been described above. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0053] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0054] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A method for detecting and analyzing river water quality for river environmental protection, characterized in that, The method includes the following steps: After preprocessing, the collected river water samples were divided into two parts. The turbidity of one part of the river water sample was measured, and the absorbance of the remaining part of the river water sample was tested. The absorbance of all wavelengths of the river water sample was used to form the absorbance sequence of the water sample. Correspondingly, the absorbance sequence of pure water of the same volume was obtained. By utilizing the difference between the absorbance sequence of the water sample and the absorbance sequence of pure water, a calibration water sample sequence is obtained; based on the changing trend of all data in the calibration water sample sequence and the number of peak points in the calibration water sample sequence, combined with the turbidity, the sparsity parameter of the river water sample is determined. Combining the aforementioned sparsity parameters with the sparse principal component analysis algorithm, we screened the characteristic wavelengths of river water samples; and determined the characteristic wavelengths of each preset water quality influencing element using Lambert-Beer's law through controlled variable experiments. The differences between the characteristic wavelengths of each preset water quality influencing element and the characteristic wavelengths of the river water sample in the corresponding elements of the calibrated water sample sequence were analyzed. Combined with the turbidity, the absorbance correction value of each preset water quality influencing element was determined. Using the absorbance correction value and the Lambert-Beer law, the content of each preset water quality influencing element in the river water sample is obtained.

2. The method for river water quality detection and analysis for river environmental protection as described in claim 1, characterized in that, The pretreatment involves adding a digesting agent dropwise to the river water sample and stirring.

3. The method for river water quality detection and analysis for river environmental protection as described in claim 1, characterized in that, The calibration water sample sequence consists of the difference between the absorbance of the water sample absorbance sequence and the absorbance of pure water at the same wavelength.

4. The method for river water quality detection and analysis for river environmental protection as described in claim 1, characterized in that, The determination of the sparsity parameter includes: Curve fitting is performed on all data in the calibration water sample sequence to obtain the goodness of fit. The sparsity parameter is positively correlated with the goodness of fit and negatively correlated with the number of peak points and the turbidity.

5. The method for river water quality detection and analysis for river environmental protection as described in claim 4, characterized in that, The sparsity parameter is calculated as follows: In the formula, For the sparsity parameters of river water samples, The goodness of fit is... The turbidity of the river water sample. To determine the number of peak points in the water sample sequence, ln() is a logarithmic function with the natural constant as the base, and norm() is a normalization function.

6. The method for river water quality detection and analysis for river environmental protection as described in claim 1, characterized in that, The method of determining the characteristic wavelengths of each preset water quality influencing element using Lambert-Beer's law through controlled variable experiments includes: For each preset water quality influencing element, a preset concentration of each water quality influencing element is added to pure water. The absorbance of the pure water with each preset concentration is measured. Based on the absorbance measurement results of all preset concentrations, the characteristic wavelength of each preset water quality influencing element is determined.

7. The method for river water quality detection and analysis for river environmental protection as described in claim 1, characterized in that, Determining the absorbance correction value includes: The turbidity is mapped to a preset range, and the characteristic wavelengths of all characteristic wavelengths of the river water sample, excluding the characteristic wavelengths of all preset water quality influencing elements, are recorded as turbidity wavelengths. For any preset water quality influencing element, a preset number of turbidity wavelengths adjacent to the characteristic wavelength of the preset water quality influencing element are obtained and denoted as near-turbidity wavelengths. The reciprocal of the distance between each near-turbidity wavelength and the characteristic wavelength of the preset water quality influencing element is used as a weight, and the data corresponding to all near-turbidity wavelengths in the calibration water sample sequence are weighted and summed. The absorbance correction value of any preset water quality influencing element is inversely proportional to the weighted summation result and the turbidity mapped to the preset range, and is directly proportional to the characteristic wavelength of any preset water quality influencing element in the data corresponding to the calibrated water sample sequence.

8. The method for river water quality detection and analysis for river environmental protection as described in claim 7, characterized in that, The absorbance correction value for any preset water quality influencing element is determined as follows: The product of the weighted summation result and the turbidity mapped to the preset interval is calculated. The absorbance correction value of any preset water quality influencing element is the difference between the data corresponding to the characteristic wavelength of any preset water quality influencing element in the calibration water sample sequence and the product.

9. A method for river water quality detection and analysis for river environmental protection as described in claim 1, characterized in that, In the process of obtaining the content of each preset water quality influencing element in the river water sample, the optical path length in the Lambert-Beer law is determined by the controlled variable experiment.

10. A river water quality detection and analysis device for river environmental protection, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1-9.

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