Water pollution treatment method and system based on water quality analysis

Abstract

The invention relates to the technical field of water pollution treatment, in particular to a water pollution treatment method and system based on water quality analysis. The method comprises the following steps: acquiring operation data of wastewater treatment equipment, and extracting water quality characteristics according to the operation data of the wastewater treatment equipment so as to obtain water quality data; performing infrared spectrum scanning according to the water quality data so as to obtain infrared spectrum water quality data; carrying out wave number feature extraction and absorption peak intensity feature extraction according to the infrared spectrum water quality data so as to obtain wave number data and absorption peak intensity data; analyzing the organic pollutants according to the wave number data and the absorption peak intensity data so as to obtain organic pollutant data; performing inductively coupled plasma analysis according to the water quality data so as to obtain plasma data; and performing heavy metal mass spectrometry according to the plasma data to obtain heavy metal data. The water quality pollution treatment efficiency is optimized based on the water pollution treatment technology.

Description

Technical Field

[0001] This invention relates to the field of water pollution control technology, and in particular to a water pollution control method and system based on water quality analysis. Background Technology

[0002] Traditional water pollution control methods, such as physicochemical and biological treatments, have achieved some success. However, due to their limitations, especially in treating complex organic pollutants, heavy metal pollution, and trace harmful substances in water bodies, they often fail to achieve ideal treatment results, and are costly and time-consuming. Traditional water quality analysis methods typically rely on chemical reagents, colorimetry, or simple sensors to detect pollutants. These methods often provide only coarse water quality information and are difficult to accurately monitor changes in pollutant concentrations. Especially when dealing with complex water samples, traditional methods have low sensitivity to low-concentration pollutants, making them prone to detection errors and resulting in inaccurate water quality monitoring data, thus affecting the evaluation of treatment effectiveness. Summary of the Invention

[0003] Therefore, it is necessary for the present invention to provide a water pollution treatment method based on water quality analysis to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, a water pollution treatment method based on water quality analysis includes the following steps:

[0005] Step S1: Obtain the operating data of the wastewater treatment equipment, and extract water quality characteristics based on the operating data to obtain water quality data; perform infrared spectral scanning based on the water quality data to obtain infrared spectral water quality data.

[0006] Step S2: Extract wavenumber features and absorption peak intensity features from infrared spectroscopy water quality data to obtain wavenumber data and absorption peak intensity data; analyze organic pollutants based on wavenumber data and absorption peak intensity data to obtain organic pollutant data.

[0007] Step S3: Perform inductively coupled plasma analysis based on water quality data to obtain plasma data; perform heavy metal mass spectrometry analysis based on plasma data to obtain heavy metal data;

[0008] Step S4: Draw a water pollution concentration distribution map based on organic pollutant data and heavy metal data to obtain a water pollution concentration distribution map; identify pollution hotspot distribution areas based on the water pollution concentration distribution map to obtain pollution hotspot distribution area data.

[0009] Step S5: Conduct water pollution treatment based on the data of pollution hotspot distribution areas to obtain water pollution treatment data, and upload it to the industrial wastewater management platform to execute industrial wastewater pollution treatment tasks.

[0010] This invention enables precise monitoring of organic pollutants and heavy metals in water bodies by acquiring operational data from wastewater treatment equipment and extracting water quality characteristics. It automates water quality data acquisition, reducing manual operation and improving data collection efficiency and accuracy. Infrared spectroscopy is used for in-depth analysis of water quality data, extracting wavenumber and absorption peak intensity characteristics to accurately identify organic pollutants in the water. It can finely distinguish various organic pollutants, especially exhibiting higher sensitivity for low-concentration organic pollutants that are difficult to detect using traditional methods, thus providing reliable data support for precision treatment. Infrared spectroscopy analysis offers advantages such as rapid response and real-time monitoring, effectively improving the timeliness of pollutant detection and reducing delays caused by the long detection cycles of traditional methods. Inductively coupled plasma (ICP) technology is used for heavy metal elemental analysis, combined with mass spectrometry for heavy metal mass spectrometry analysis, enabling multi-element, high-sensitivity detection of various heavy metals in water bodies. ICP-MS technology can not only detect trace amounts of heavy metal pollutants in water but also provide comprehensive pollutant analysis data, ensuring accurate identification and quantitative analysis of pollution sources. By combining data on organic pollutants and heavy metals and employing methods for mapping water pollution concentrations, pollution hotspots can be accurately identified. This enables real-time monitoring of pollution sources and provides a scientific basis for designing subsequent pollution control solutions. Identifying pollution hotspots helps concentrate resources and optimize control strategies during the pollution control process, improving overall effectiveness. Utilizing pollution hotspot data for water pollution control and uploading it to an industrial wastewater management platform enables automation and real-time monitoring of the water quality control process. This ensures timely adjustments to pollution control plans and efficient execution and management of pollution control tasks.

[0011] Optionally, step S1 specifically includes:

[0012] Step S11: Obtain the operating data of the wastewater treatment equipment, and extract water quality characteristics based on the operating data of the wastewater treatment equipment to obtain water quality data;

[0013] Step S12: Acquire pollutant spectral data and moisture interference band data;

[0014] Step S13: Simulate pollutant absorption peaks based on pollutant spectral data and water quality data to obtain pollutant absorption peak data;

[0015] Step S14: Perform piecewise linear fitting on the moisture interference band data to obtain the moisture interference baseline data;

[0016] Step S15: Remove moisture interference peaks from the baseline data of moisture interference and correct the data of pollutant absorption peaks.

[0017] Step S16: Generate infrared spectral water quality data based on pollutant absorption peak correction data.

[0018] This invention, by acquiring operational data from wastewater treatment equipment and extracting water quality characteristics, provides accurate basic water quality data for subsequent pollutant detection. By combining real-time monitoring data, it can reflect changes in water quality in real time, promptly grasp the dynamics of water quality, and avoid the delayed response of traditional methods to water quality conditions. Acquiring pollutant spectral data and moisture interference band data further provides crucial data support for accurate pollutant analysis. Acquiring spectral data helps to accurately analyze the characteristic absorption peaks of different pollutants, especially when the water quality is complex or contains many interfering substances, avoiding the low sensitivity and detection error problems of traditional methods. By simulating the absorption peak data of pollutants, the spectral characteristics of pollutants can be accurately extracted, improving the reliability of pollutant analysis and making subsequent analysis more accurate and efficient. It reduces the dependence on samples in traditional methods and avoids analytical errors caused by improper sample processing. Piecewise linear fitting of the moisture interference band data can effectively remove the influence of moisture on the spectral signal. Moisture interference is a common problem in water quality analysis; through fitting techniques, moisture interference can be accurately estimated and removed, ensuring the purity of pollutant analysis data. After removing moisture interference, the pollutant absorption peak data were corrected, making the spectral analysis data more accurate. Correction of pollutant absorption peaks significantly improves the accuracy of analytical results, especially in complex water samples, ensuring that pollutant concentration measurements are not affected by moisture changes or other substances. By generating infrared spectral water quality data based on the corrected absorption peak data, the overall water quality status can be comprehensively and quickly reflected. This ensures comprehensive water quality monitoring and real-time pollutant analysis, providing scientific and accurate data support for subsequent water pollution control. This invention greatly improves the accuracy and real-time performance of water quality analysis by accurately simulating pollutant absorption peaks, removing moisture interference, and optimizing spectral data correction.

[0019] Optionally, step S14 specifically includes:

[0020] Step S141: Divide the water interference band data into band intervals to obtain water interference band interval data;

[0021] Step S142: Perform piecewise least squares fitting based on the moisture interference band interval data to obtain piecewise least squares fitted data.

[0022] Step S143: Set the optimal slope based on the piecewise least squares fitting data to obtain the optimal slope data;

[0023] Step S144: Set the optimal intercept based on the piecewise least squares fitted data to obtain the optimal intercept data;

[0024] Step S145: Construct a moisture disturbance baseline based on the optimal slope data and the optimal intercept data to obtain moisture disturbance baseline data.

[0025] This invention effectively identifies and locates specific bands affecting the spectral signal by dividing the water interference band into intervals, ensuring accurate identification of water interference and avoiding the shortcomings of traditional methods that fail to fully identify water interference bands. Piecewise least squares fitting is used to process the interference bands, allowing for flexible adjustment of the fitting method within different intervals to minimize the impact of interference bands on pollutant analysis results. By setting the optimal slope and intercept, the fitting process can be further optimized, making the corrected data more closely match the actual situation and reducing errors caused by overfitting or underfitting. This optimization step improves the accuracy of data correction, ensuring that the spectral signal after removing water interference truly reflects the characteristic information of pollutants in the water. Constructing a water interference baseline using the optimal slope and intercept data lays the foundation for accurate extraction of pollutant absorption peaks. The accurate construction of the water interference baseline not only improves the reliability of the analysis results but also makes water quality analysis more efficient and accurate in complex samples. By optimizing the identification and removal methods of water interference, the reliability of water quality analysis in practical applications is improved, errors caused by interference are reduced, and the accuracy and efficiency of subsequent pollutant analysis and water quality treatment are ensured.

[0026] Optionally, step S2 specifically includes:

[0027] Step S21: Extract wavenumber features and absorption peak intensity features from infrared spectral water quality data to obtain wavenumber data and absorption peak intensity data;

[0028] Step S22: Identify organic pollution sources in the wavenumber data to obtain organic pollution source data;

[0029] Step S23: Analyze the absorption peak intensity data to obtain organic pollution concentration data;

[0030] Step S24: Perform organic pollutant characteristic fusion based on organic pollution source data and organic pollution concentration data to obtain organic pollutant data.

[0031] This invention significantly improves the detection and quantitative analysis capabilities of organic pollutants in water through detailed analysis of infrared spectral water quality data. Wavenumber feature extraction and absorption peak intensity feature extraction can extract key wavenumber information and absorption peak intensities from complex infrared spectral data. The extracted wavenumber and absorption peak intensity data can more accurately reflect the characteristics of pollutants in water samples, avoiding interference with complex water samples. By identifying organic pollution sources from wavenumber data, different organic pollution sources can be effectively distinguished from complex water quality, identifying potential organic pollutants in water samples. This enables the system to accurately identify specific pollution sources, avoiding errors in pollution source identification in traditional methods and improving the sensitivity and accuracy of identification. This invention can identify more diverse organic pollution sources and can adapt to more complex water samples. By analyzing the organic pollution concentration from absorption peak intensity data, the concentration of organic pollutants can be directly derived from existing spectral information, providing direct and actionable data support for pollution control. The concentration of organic pollutants in water bodies can be accurately calculated, thus providing a more scientific basis for environmental monitoring and control measures. By fusing organic pollution source data with organic pollution concentration data, water quality analysis becomes more comprehensive and accurate. Feature fusion effectively integrates information from multiple data sources, providing a more comprehensive pollutant characteristic profile and ensuring more precise identification and concentration analysis of organic pollutants.

[0032] Optionally, step S22 specifically includes:

[0033] Step S221: Extract the spectral region features of benzene compounds from the wavenumber data to obtain the spectral region data of benzene compounds;

[0034] Step S222: Calculate the absorbance based on the wavelength region data of benzene compounds to obtain the absorbance data of benzene compounds;

[0035] Step S223: Obtain absorbance data of benzene compounds at water quality monitoring points;

[0036] Step S224: Draw a spatial distribution map of absorbance values ​​based on the absorbance data of benzene compounds at water quality monitoring points to obtain spatial distribution map data of absorbance values, and identify the features of high absorbance value areas based on the spatial distribution map data of absorbance values ​​to obtain high absorbance value area data;

[0037] Step S225: Plot the absorbance value over time curve based on the absorbance data of benzene compounds at the water quality monitoring points to obtain the absorbance value over time curve data, and statistically analyze the increase in absorbance value based on the absorbance value over time curve data to obtain the time data of the increase in high absorbance value.

[0038] Step S226: Perform pollution intersection calculation based on the time data of the increase in high absorbance value and the data of the high absorbance value region to obtain organic pollution source data.

[0039] This invention extracts the characteristic spectral regions of benzene compounds from wavenumber data, accurately identifying these regions from complex infrared spectral data. This effectively enhances the system's ability to identify benzene compounds, making pollution source identification more sensitive and accurate. It also effectively reduces background interference and improves data quality, providing more reliable foundational data for subsequent absorbance calculations. Absorbance calculations based on the extracted benzene compound spectral region data allow the system to directly obtain benzene compound absorbance data, providing a quantitative basis for pollutant concentration analysis and pollution source tracing. Absorbance is a crucial characteristic parameter of benzene compounds, and its changes reflect changes in benzene compound concentration in water. This calculation provides a more intuitive and accurate description of the spatiotemporal distribution of pollutant concentrations, avoiding indirect derivation errors common in traditional methods. By acquiring benzene compound absorbance data from water quality monitoring points, the system's ability to accurately monitor pollution levels at different monitoring points is further enhanced. This process enables the system to track water quality changes at each monitoring point in real time, promptly detect abnormal concentration changes of benzene compounds in the water, and ensure rapid response and intervention to water pollution. It significantly improves the frequency and accuracy of monitoring, making pollutant detection more sensitive. By plotting the spatial distribution map of absorbance values ​​based on the absorbance data, the concentration distribution of benzene compounds in the water can be visually displayed. This spatial distribution map effectively reveals the spatial location and concentration gradient of pollution sources, thus helping to discover the distribution patterns of pollutants and identify high-risk pollution areas. Through feature identification of high absorbance areas, hotspots of water pollution can be accurately located, ensuring precise tracking and treatment of pollution sources. By plotting the absorbance change curve over time, the system can monitor the changes in benzene compound concentration over time. This helps identify fluctuations in benzene compound concentration in the water and reveals trends and periodic changes in concentration, helping decision-makers understand the diffusion rate and impact range of pollutants. By statistically analyzing the increase in absorbance values, periods of rapid increase in pollutant concentration can be clearly identified, providing early warning of potential pollution crises. By performing pollution intersection calculations based on temporal data of high absorbance increase and regional data of high absorbance, the specific location of pollution sources and their impact on the surrounding environment can be determined by combining the spatiotemporal characteristics of pollutants. This intersection calculation provides a comprehensive assessment of benzene compound pollution sources, which not only helps to accurately identify pollution sources but also provides a scientific basis for subsequent remediation measures.

[0040] Optionally, step S23 specifically includes:

[0041] Step S231: Extract the absorption peak characteristics of benzene compounds from the absorption peak intensity data to obtain the absorption peak data of benzene compounds;

[0042] Step S232: Identify the peak regions based on the absorption peak data of benzene compounds to obtain the absorption peak region data of benzene compounds;

[0043] Step S233: Perform concentration statistics on the absorbance data of benzene compounds to obtain the concentration data of benzene compounds;

[0044] Step S234: Plot a concentration calibration curve based on the absorbance data and concentration data of benzene compounds to obtain the concentration calibration curve data;

[0045] Step S235: Calculate the organic pollution concentration based on the absorption peak region data of benzene compounds according to the concentration calibration curve data, thereby obtaining the organic pollution concentration data.

[0046] This invention extracts the characteristic absorption peaks of benzene compounds from absorption peak intensity data, effectively screening out characteristic absorption peaks related to benzene compounds. This reduces data errors caused by interference from other components, improving the accuracy of benzene compound identification. It more accurately reflects the absorption characteristics of benzene compounds, ensuring the quality and reliability of the analytical data. Peak region identification based on benzene compound absorption peak data reduces data redundancy and noise impact, ensuring precise division of absorption peak regions. Identifying peak regions effectively filters out irrelevant information, ensuring focused and efficient analysis. Compared to traditional simple absorption peak extraction methods, this step optimizes peak region division, making absorption peak location more precise and further improving the quantitative analysis capability of benzene compounds. In the benzene compound concentration statistics stage, concentration statistics based on benzene compound absorbance data not only provide accurate concentration values ​​but also capture fluctuations in water pollution levels. The beneficial effect of this step is that quantitative statistical analysis of benzene compound concentrations provides more scientific data support for water pollution assessment. By plotting concentration calibration curves, a clear quantitative relationship is established between the absorbance data and concentration data of benzene compounds, providing a standard basis for subsequent concentration calculations. This step significantly improves the quantitative accuracy of water quality analysis, enabling accurate conversion of absorbance data into benzene compound concentration data through standardized calibration curves, ensuring the scientific rigor and consistency of data processing. The application of concentration calibration curves makes quantitative analysis more standardized and accurate, effectively enhancing data reliability. By calculating organic pollution concentrations based on the absorption peak region data of benzene compounds according to the concentration calibration curve data, the degree of benzene compound pollution in water can be accurately reflected, providing direct evidence for pollution source monitoring and remediation. Compared with traditional methods, this process not only improves the accuracy of concentration calculations but also ensures the timeliness and scientific nature of pollution control. Through this precise pollution concentration analysis, pollution sources can be better identified, remediation measures optimized, and more efficient and targeted water quality management achieved.

[0047] Optionally, step S3 specifically includes:

[0048] Step S31: Perform inductively coupled plasma analysis based on water quality data to obtain inductively coupled plasma data;

[0049] Step S32: Ionize the heavy metal elements in the inductively coupled plasma data to obtain heavy metal ion data;

[0050] Step S33: Perform a mass spectrometer simulation based on the heavy metal ion data to obtain mass spectrometry data;

[0051] Step S34: Perform heavy metal mass spectrometry analysis based on the mass spectrometry data to obtain heavy metal data.

[0052] This invention utilizes inductively coupled plasma (ICP) analysis to efficiently excite and ionize heavy metal elements in water, providing high-quality ion data for subsequent mass spectrometry (MS / MS) analysis. The advantage of this step is that ICP technology can effectively handle complex water samples, especially in the detection of low concentrations and trace elements, exhibiting excellent sensitivity and accuracy, avoiding the limitations of traditional methods, and ensuring the reliability of the detection results. By ionizing heavy metal elements from the ICP data, heavy metal ion data can be obtained. The advantage of this step is that the generation process of heavy metal ions, through a highly efficient ionization mechanism, greatly improves the ionization efficiency, ensuring the accuracy of subsequent analysis. Compared to traditional chemical analysis methods, the ionization process of heavy metal ions not only improves detection sensitivity but also effectively reduces the influence of interference factors, ensuring accurate detection of heavy metal elements, especially in complex water conditions, avoiding detection errors caused by interference from other ions. By importing the heavy metal ion data into a mass spectrometer simulation, high-precision mass spectrometry data can be obtained. This process, through high-resolution analysis using mass spectrometry, can accurately identify and quantify different heavy metal elements and their isotopes, improving data accuracy and repeatability. Mass spectrometry can effectively distinguish different types of heavy metal elements, providing more precise elemental content analysis, especially in the detection of trace heavy metals, exhibiting higher sensitivity and accuracy. Compared with traditional detection methods, it avoids error accumulation and result bias. Accurate heavy metal data can be obtained through heavy metal mass spectrometry analysis of the mass spectrometry data. The beneficial effect of this step is that mass spectrometry analysis not only enables high-precision determination of heavy metal concentrations but also provides reliable data support for tracing and remediating water pollution sources through quantitative analysis. Compared with traditional water quality detection methods, this invention, through the processing of mass spectrometry data, can provide more comprehensive and accurate heavy metal pollution analysis, optimizing the pollution source identification and remediation process, and helping to improve the efficiency and effectiveness of water pollution control. Through this process, efficient and accurate monitoring of heavy metal pollutants in water can be achieved, thereby providing a scientific basis for water pollution control.

[0053] Optionally, step S32 specifically includes:

[0054] Step S321: Extract water quality metal element features from inductively coupled plasma data to obtain water quality metal element data;

[0055] Step S322: Perform heavy metal element gasification based on water quality metal element data to obtain heavy metal atom gasification data;

[0056] Step S323: Excite heavy metal atoms at high temperature to obtain excited state data of heavy metal atoms by performing high-temperature excitation on the heavy metal atom vaporization data;

[0057] Step S324: High-temperature ionization of the excited state data of heavy metal atoms to obtain heavy metal ion data.

[0058] This invention utilizes inductively coupled plasma (ICP) data to extract the characteristics of metal elements in water samples, effectively identifying various metal elements, especially trace and ultra-trace elements, from complex water samples. It accurately extracts characteristic data of metal elements from water samples, laying a solid foundation for subsequent heavy metal analysis while avoiding errors and omissions inherent in traditional methods. This is particularly beneficial under complex water quality conditions, ensuring data integrity and reliability. By vaporizing the water metal element data, heavy metal atom vaporization data can be obtained. The advantage of this step is that the vaporization process ensures that the metal elements exist in gaseous atomic form, which not only improves the detection efficiency but also reduces interference from solid and liquid impurities in the water sample. Through vaporization, heavy metal elements can more easily enter the plasma region, providing favorable conditions for subsequent excitation and ionization processes, thereby improving analytical accuracy, especially important for the detection of low-concentration heavy metals. The heavy metal atom vaporization data is then subjected to high-temperature excitation of metal atoms to generate excited-state data of heavy metal atoms. The beneficial effect of this process is that high-temperature excitation can effectively excite metal atoms to high-energy states, providing a sufficient excitation source for subsequent ionization and mass spectrometry analysis. This excitation process improves the ion formation efficiency, allowing heavy metal elements in the water sample to fully respond during mass spectrometry analysis, thus enhancing the sensitivity and reliability of the analysis. Heavy metal ion data are obtained by high-temperature ionization of the excited-state data of heavy metal atoms. The beneficial effect of this process is that the ionization process can convert metal atoms into charged ions, enabling them to generate measurable signals in the mass spectrometer and ensuring that the ionization degree of each heavy metal element is sufficient and uniform. High-temperature ionization technology provides a stable ion source for mass spectrometry analysis and can effectively improve the detection sensitivity, especially demonstrating excellent capabilities in the detection of low concentrations of heavy metal elements. Ultimately, this series of steps can provide accurate and reliable quantitative analysis results for heavy metal pollutants in water quality, greatly improving the accuracy and efficiency of water quality monitoring.

[0059] Optionally, step S34 specifically includes:

[0060] Step S341: Draw a mass spectrum based on the mass spectrometry data to obtain the mass spectrum data;

[0061] Step S342: Identify the lead element mass-to-charge ratio region in the mass spectrum data to obtain the lead element mass-to-charge ratio region data;

[0062] Step S343: Extract the peak features of lead element based on the mass-to-charge ratio regional data of lead element to obtain the peak data of lead element;

[0063] Step S344: Calculate the peak area of ​​the lead element peak data to obtain the peak area data of the lead element.

[0064] Step S345: Calculate the peak height of lead element peak data to obtain the peak height data of lead element.

[0065] Step S346: Calculate the lead concentration based on the lead peak area data and lead peak height data to obtain the lead concentration data;

[0066] Step S347: Perform heavy metal feature fusion based on lead element peak data and lead element concentration data to obtain heavy metal data.

[0067] This invention utilizes mass spectrometry data to create mass spectra, providing a clear view of the mass-to-charge ratio (M / C ratio) of various ions in a water sample and offering a comprehensive elemental spectrum. This step clearly displays the characteristic peaks of various heavy metals in the water sample, providing comprehensive and systematic mass spectrometry data support for subsequent analysis. Identifying the lead M / C ratio region within the mass spectra further narrows the analytical scope, focusing on the lead M / C ratio region. This process avoids interference from other elements by accurately identifying the characteristic M / C ratio of lead, improving the accuracy of lead analysis, effectively extracting its characteristic region, enhancing the resolution of the mass spectra, and improving the detection capability for low concentrations of lead. Characteristic extraction of lead peaks is then performed based on the lead M / C ratio region data. This step, by extracting specific lead M / C peaks, more accurately obtains the analytical signal of lead, ensuring accurate identification of lead in complex water samples. This step accurately extracts lead peak data, making lead concentration measurements more reliable and providing a solid data foundation for subsequent quantitative analysis. The peak area and height of lead peak data are calculated to extract key data related to lead concentration by quantifying peak shape characteristics. This calculation process is beneficial because peak area and height are indispensable parameters in quantitative analysis, directly proportional to element concentration. Accurate calculation ensures precise measurement of lead concentration, improving the reliability of analytical results, especially in complex water samples where it effectively reduces errors. Lead concentration is calculated based on peak area and height data. This step establishes a mathematical model between peak data and known standard concentrations to determine the actual lead concentration in the water sample. This step not only improves the accuracy of concentration measurement but also provides clear quantitative evidence for water quality management, ensuring the operability and practical application value of water quality monitoring results. By fusing heavy metal characteristics of lead peak and concentration data, multiple factors can be comprehensively considered, further refining the analysis results of heavy metal pollutants. The beneficial effect of this step is that the fusion processing of heavy metal data makes the analysis results more comprehensive and reliable, and provides a more efficient method for the synergistic analysis of multiple heavy metals in complex water samples, thereby improving the accuracy and response speed of water pollution control.

[0068] Optionally, this specification also provides a water pollution control system based on water quality analysis for performing the water pollution control method based on water quality analysis as described above. The water pollution control system based on water quality analysis includes:

[0069] Infrared spectroscopy scanning module: used to acquire operating data of wastewater treatment equipment, extract water quality characteristics based on the operating data of wastewater treatment equipment, and thus obtain water quality data; and to perform infrared spectroscopy scanning based on the water quality data to obtain infrared spectral water quality data;

[0070] Organic pollutant analysis module: used to extract wavenumber features and absorption peak intensity features from infrared spectral water quality data to obtain wavenumber data and absorption peak intensity data; and to analyze organic pollutants based on the wavenumber data and absorption peak intensity data to obtain organic pollutant data.

[0071] Heavy metal mass spectrometry analysis module: used to perform inductively coupled plasma analysis based on water quality data to obtain plasma data; and to perform heavy metal mass spectrometry analysis based on plasma data to obtain heavy metal data.

[0072] Pollution hotspot distribution area identification module: used to draw water pollution concentration distribution maps based on organic pollutant data and heavy metal data, thereby obtaining water pollution concentration distribution maps; and to identify pollution hotspot distribution areas based on water pollution concentration distribution maps, thereby obtaining pollution hotspot distribution area data.

[0073] Water pollution control module: Used to control water pollution based on data on the distribution of pollution hotspots, thereby obtaining water pollution control data and uploading it to the industrial wastewater management platform to execute industrial wastewater pollution control tasks.

[0074] The present invention discloses a water pollution control system based on water quality analysis. This system can implement any water pollution control method based on water quality analysis according to the present invention. It serves as a medium for coordinating the operation and signal transmission between various modules to complete the water pollution control method based on water quality analysis. The modules within the system cooperate with each other to optimize the efficiency of water pollution control. Attached Figure Description

[0075] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0076] Figure 1 This is a schematic diagram of the steps of the water pollution treatment method based on water quality analysis of the present invention;

[0077] Figure 2 This is a detailed flowchart of step S1 in the present invention;

[0078] Figure 3 This is a detailed flowchart of step S14 in the present invention;

[0079] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0080] The technical method of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this invention.

[0081] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0082] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0083] To achieve the above objectives, please refer to Figures 1 to 3 This invention provides a water pollution treatment method based on water quality analysis, the method comprising the following steps:

[0084] Step S1: Obtain the operating data of the wastewater treatment equipment, and extract water quality characteristics based on the operating data to obtain water quality data; perform infrared spectral scanning based on the water quality data to obtain infrared spectral water quality data.

[0085] In this embodiment, the operational data of the wastewater treatment equipment is first collected through a real-time monitoring system. This system includes sensors installed in the wastewater treatment equipment, such as temperature sensors, flow meters, pH sensors, and conductivity sensors. The sensors upload the real-time wastewater parameters to an industrial automation management platform. The data processing module preprocesses the raw data, removing noise and outliers, and extracts water quality characteristic data using algorithms. The feature extraction process includes analyzing key indicators in the water sample, such as dissolved oxygen, ammonia nitrogen, total phosphorus, and chemical oxygen demand (COD), and calculating their concentration values. Next, based on the extracted water quality data, infrared spectroscopy is performed using a Fourier transform infrared spectrometer (FTIR) to scan the sample and acquire infrared spectral data. This data contains absorption information of different chemical substances in the infrared light band, which is converted into absorbance data corresponding to the wavelength by a spectrometer. At this point, the infrared spectral data of the water sample can provide detailed spectral information for subsequent pollutant analysis.

[0086] Step S2: Extract wavenumber features and absorption peak intensity features from infrared spectroscopy water quality data to obtain wavenumber data and absorption peak intensity data; analyze organic pollutants based on wavenumber data and absorption peak intensity data to obtain organic pollutant data.

[0087] In this embodiment, after acquiring infrared spectral water quality data, wavenumber feature extraction methods are used to analyze the data. Specific wavenumber ranges (e.g., 3200-3400 cm⁻¹, typically related to the stretching vibrations of functional groups such as OH and NH in organic pollutants) are extracted from the infrared spectrum to identify relevant absorption peaks. Each characteristic absorption peak is identified using an algorithm, and its peak intensity is calculated. These peak intensities show specific correlations with the concentrations of different organic pollutants in the water. Based on this, organic pollutant analysis is performed on the wavenumber data and absorption peak intensity data. Specifically, using a multiple linear regression model or other statistical analysis methods, based on the spectral data of known standard samples, the characteristic wavenumber and peak intensity of each pollutant are determined, establishing a quantitative relationship between pollutant concentration and absorbance. By comparing with standard data, the concentration values ​​of organic pollutants in the water sample are obtained.

[0088] Step S3: Perform inductively coupled plasma analysis based on water quality data to obtain plasma data; perform heavy metal mass spectrometry analysis based on plasma data to obtain heavy metal data;

[0089] In this embodiment, water quality data is input into an inductively coupled plasma (ICP) analysis system. ICP is a highly efficient technique for analyzing metal elements in water. It uses high-temperature plasma to excite elements in the sample to a high-energy state, causing them to form ions. This process requires adjusting the plasma power, gas flow rate, and gas composition to ensure optimal ionization efficiency. The collected plasma data is imported into a simulation using a mass spectrometer (MS). The MS separates and quantifies these ions, analyzing them based on their mass-to-charge ratio (m / z). At this point, heavy metal elements in the water sample, such as lead, mercury, and copper, are accurately identified and converted into corresponding heavy metal data. To ensure data accuracy, multiple internal standard elements are used for correction during the analysis to compensate for any errors.

[0090] Step S4: Draw a water pollution concentration distribution map based on organic pollutant data and heavy metal data to obtain a water pollution concentration distribution map; identify pollution hotspot distribution areas based on the water pollution concentration distribution map to obtain pollution hotspot distribution area data.

[0091] In this embodiment, a spatial interpolation algorithm (such as Kriging or inverse distance weighting) is used to draw a spatial distribution map of pollutants based on data from each water quality monitoring point. This map visually displays the concentration distribution of organic pollutants and heavy metals in the water sample, clearly showing the pollution level at each monitoring point. Next, based on the water pollution concentration distribution map, the algorithm identifies pollution hotspots. Hotspots refer to areas where pollutant concentrations exceed a certain threshold (e.g., COD concentration exceeding 30 mg / L). In practical applications, a suitable threshold can be set (e.g., concentration exceeding the standard water quality limit), and by calculating the spatial distribution of pollutant concentrations, severely polluted areas can be identified.

[0092] Step S5: Conduct water pollution treatment based on the data of pollution hotspot distribution areas to obtain water pollution treatment data, and upload it to the industrial wastewater management platform to execute industrial wastewater pollution treatment tasks.

[0093] In this embodiment, data on the distribution of pollution hotspots is input into the water pollution control system, and corresponding treatment measures are selected based on the pollution type. A real-time data feedback mechanism is employed, automatically adjusting the operating status of the treatment equipment in conjunction with real-time changes in pollutant concentrations. Treatment measures include physical adsorption, chemical precipitation, and biological treatment. For example, for heavy metal pollution, the system automatically initiates adaptive chemical precipitation, adding an appropriate amount of precipitant (such as sulfides) to cause heavy metals to settle; while for organic pollutants, activated carbon adsorption or ozone oxidation methods are used. Data during the treatment process is fed back to the industrial wastewater management platform in real time via sensors and uploaded via wireless communication networks. The system dynamically adjusts the treatment plan based on the treatment effect until the water quality meets the standards. Finally, the completed data and results are uploaded to the platform in real time, facilitating real-time tracking and decision optimization by supervisory personnel.

[0094] Optionally, step S1 specifically includes:

[0095] Step S11: Obtain the operating data of the wastewater treatment equipment, and extract water quality characteristics based on the operating data of the wastewater treatment equipment to obtain water quality data;

[0096] In this embodiment, the operational data of the wastewater treatment equipment is collected through sensors installed at different stages of wastewater treatment, such as the inlet, outlet, and key treatment units. These sensors include flow meters, pH sensors, dissolved oxygen sensors, chemical oxygen demand (COD) meters, and ammonia nitrogen sensors, which monitor and record various indicators of the wastewater in real time. This real-time data is transmitted to the central processing system via a PLC (Programmable Logic Controller) or Data Acquisition System (DAQ). After preprocessing, the data is used to extract water quality characteristics using algorithms. The feature extraction process primarily focuses on the concentrations of indicators such as COD, BOD, ammonia nitrogen, total phosphorus, and dissolved oxygen, and the overall water quality is determined through simple weighted calculations or regression analysis. Based on the water quality data, Fourier Transform Infrared Spectroscopy (FTIR) is used to scan the wastewater's infrared spectrum. The instrument acquires the infrared absorption spectrum data of the sample at a certain scanning speed and accuracy (e.g., a resolution of 1 cm^-1). This spectral data reflects the chemical characteristics of different substances in the water. The data obtained from the FTIR scan provides a basis for further analysis of pollutants.

[0097] Step S12: Acquire pollutant spectral data and moisture interference band data;

[0098] In this embodiment, pollutant spectral data and moisture interference band data are obtained by infrared spectral scanning of wastewater samples. Moisture in the water sample can interfere with the spectral data, especially in the 3200 cm⁻¹ to 3500 cm⁻¹ band, which is usually associated with the OH bond vibration of water and therefore needs to be removed from the spectral data. First, spectral data containing moisture interference is acquired using a specially designed sensor or spectroscopic device. This data contains spectral information of moisture and other pollutants. For pollutant spectral data, the characteristic bands of dissolved organic matter and heavy metal elements in the water sample are recorded simultaneously during the spectral scan, such as the 2000 cm⁻¹ to 2200 cm⁻¹ range, which are typically used to detect the absorption characteristics of certain heavy metals or organic matter. This data will be used in subsequent steps in conjunction with the moisture interference data to remove or correct the influence of moisture on the pollutant spectrum.

[0099] Step S13: Simulate pollutant absorption peaks based on pollutant spectral data and water quality data to obtain pollutant absorption peak data;

[0100] In this embodiment, pollutant absorption peak simulation is performed based on the spectral data of the pollutants and water quality data. By analyzing the characteristic bands of different pollutants in the spectral data, the absorption peaks of the pollutants can be extracted. During the simulation, mathematical models such as Gaussian functions or Lorentz functions are used to fit the pollutant absorption peak data. In this process, the typical wavenumber range of the pollutants is first set (e.g., 2000 cm⁻¹ to 2200 cm⁻¹ for analyzing organic matter, and around 3000 cm⁻¹ for water-related absorption). Then, based on existing water quality data and laboratory standards, suitable bands and parameters are selected for simulation calculations. By comparing the spectral data with that of pollutants of known concentrations, the absorption peak data of the pollutants are obtained. The model will further predict the concentration range of pollutants in the water based on the intensity and shape of the standard absorption peaks, thereby providing data support for pollutant concentration analysis.

[0101] Step S14: Perform piecewise linear fitting on the moisture interference band data to obtain the moisture interference baseline data;

[0102] In this embodiment, the moisture interference band data is processed using a piecewise linear fitting method. The moisture-related bands (e.g., 3200-3500 cm⁻¹) in the acquired infrared spectral data are divided into bands, and then processed using a piecewise linear fitting algorithm. This algorithm approximates the moisture interference band by determining a linear fitting function for each sub-interval. Linear fitting parameters for each band are set, specifically including the slope and intercept. This process employs a least-squares fitting algorithm to ensure that the fitting result has the minimum error within each sub-interval. Through this step, the baseline features of moisture interference can be extracted, removing the influence of moisture interference on the pollutant absorption peak data. The obtained moisture interference baseline data provides a basis for subsequent data interference removal and pollutant absorption peak correction.

[0103] Step S15: Remove moisture interference peaks from the baseline data of moisture interference and correct the data of pollutant absorption peaks.

[0104] In this embodiment, moisture interference baseline data and pollutant absorption peak data are processed to remove moisture interference peaks, resulting in corrected data for the pollutant absorption peaks. By comparing the wavenumber positions of the moisture interference baseline and the pollutant absorption peaks, the influence region of the moisture interference band is determined, and data correction is then performed within specific absorption peak regions. Based on linear regression or adaptive filtering algorithms, the influence of moisture interference peaks on pollutant absorption peaks is removed. Specific steps include using the moisture interference baseline data as a reference, performing peak value subtraction within the corresponding absorption peak region to remove the moisture-related influence from the pollutant absorption peaks. This yields more accurate pollutant absorption peak data, providing clear foundational data for further quantitative pollutant analysis.

[0105] Step S16: Generate infrared spectral water quality data based on pollutant absorption peak correction data.

[0106] In this embodiment, after correcting for pollutant absorption peaks, infrared spectral water quality data is generated using the corrected data. The pollutant absorption peaks generated from the spectral data, combined with their characteristic wavenumbers, can effectively reflect the types and concentration levels of pollutants in the water. A quantitative relationship model between absorbance and pollutant concentration is constructed using the absorption peaks and corresponding concentration data of known standard pollutants. This model calculates the concentration levels of each pollutant in the water by further analyzing the absorbance data. Key parameters used in the generation of spectral water quality data include the typical absorption band of each pollutant and the relationship curve between absorbance and concentration. This data is transmitted to the wastewater management platform for subsequent pollutant analysis and monitoring systems, ensuring real-time monitoring of water quality changes and optimizing wastewater treatment processes.

[0107] Optionally, step S14 specifically includes:

[0108] Step S141: Divide the water interference band data into band intervals to obtain water interference band interval data;

[0109] In this embodiment, moisture interference band data is acquired using an infrared spectrometer. The spectrometer's scanning range typically includes the band interval from 3200 cm⁻¹ to 3500 cm⁻¹, which primarily contains the absorption characteristics of the OH vibration of water. To ensure accurate moisture interference identification, the data in this band is first preliminarily analyzed and organized. The data segmentation process follows the experimental design, using fixed wavenumber intervals for segmentation. For example, the 3200 cm⁻¹ to 3400 cm⁻¹ interval is divided into the first segment, and the 3400 cm⁻¹ to 3500 cm⁻¹ interval into the second segment. Each band undergoes data smoothing processing based on actual conditions to eliminate noise in the spectral data. Based on this, the start and end points of each band interval are determined, and each band is processed independently to obtain the moisture interference band data for each interval. These interval data provide the foundation for subsequent fitting and baseline construction.

[0110] Step S142: Perform piecewise least squares fitting based on the moisture interference band interval data to obtain piecewise least squares fitted data.

[0111] In this embodiment, after obtaining the water interference band interval data, piecewise least squares fitting is used for further data processing. Each divided band interval is linearly fitted according to the principle of least squares, the purpose of which is to find an optimal fitting line that minimizes the deviation between the fitted line and the band data. To achieve this goal, a fitting accuracy standard must first be set, typically the minimum sum of squared errors. This method generates fitting results for each interval, and the error between the fitted line and the actual data for each interval is adjusted within an allowable range. During processing, the threshold for linear fitting can be set through error analysis; for example, a successful fit is considered when the error is less than 0.01. The obtained piecewise least squares fitted data provides the basis for subsequent calculations of the optimal slope and intercept.

[0112] Step S143: Set the optimal slope based on the piecewise least squares fitting data to obtain the optimal slope data;

[0113] In this embodiment, the optimal slope is set based on the piecewise least squares fitting data. The slope is calculated based on the fitted straight line for each data segment, and the slope value represents the rate of change of the moisture interference band. During calculation, the slope of each fitted line segment needs to be calculated first. The slope value is usually obtained from the fitting formula: Slope = Δy / Δx, where Δy is the change in the y-coordinate on the fitted line, and Δx is the change in the x-coordinate. To obtain the optimal slope, the slopes of each fitted segment are usually compared, and the average slope of all fitted segments is selected as the final slope, ensuring that the optimal slope effectively represents the absorption characteristics of moisture in the moisture interference band. The final optimal slope value provides parameter support for further constructing the moisture interference baseline.

[0114] Step S144: Set the optimal intercept based on the piecewise least squares fitted data to obtain the optimal intercept data;

[0115] In this embodiment, after piecewise least squares fitting, the optimal intercept is set. The intercept value is usually the value of the intersection of the fitted line and the y-axis, representing the baseline value when there is no moisture interference. The intercept is calculated using the formula for the fitted line. During processing, the intercept value is calculated according to the fitting formula for each data segment based on the fitting results. In the processing of multiple segmented data, the optimal intercept is determined based on the fitting of each segment, usually selecting the average intercept of each fitting result as the optimal intercept. To optimize the accuracy of the intercept, it is necessary to ensure the uniformity of the data point distribution and avoid extreme values ​​that could significantly affect the intercept. The final optimal intercept value will become the reference value for the moisture interference baseline.

[0116] Step S145: Construct a moisture disturbance baseline based on the optimal slope data and the optimal intercept data to obtain moisture disturbance baseline data.

[0117] In this embodiment, a moisture interference baseline is constructed using optimal slope and optimal intercept data. During this process, the optimal slope and optimal intercept are used to fit the moisture interference baseline data. Baseline construction involves synthesizing the data from each segment according to the optimal slope and intercept formulas. Specifically, the fitted straight line for each band interval is combined with the optimal slope and intercept to perform linear compensation on each segment of data. The compensated data is the moisture baseline after removing moisture interference. This process effectively removes the interference of moisture absorption characteristics on pollutant absorption peaks. After the moisture interference baseline is constructed, the obtained data can serve as a reference for subsequent pollutant concentration analysis, ensuring the accuracy and reliability of pollutant absorption peak data.

[0118] Optionally, step S2 specifically includes:

[0119] Step S21: Extract wavenumber features and absorption peak intensity features from infrared spectral water quality data to obtain wavenumber data and absorption peak intensity data;

[0120] In this embodiment, wastewater samples are scanned using Fourier Transform Infrared Spectroscopy (FTIR) to obtain spectral data of the water quality. The wavenumber range of the scan is typically from 4000 cm⁻¹ to 400 cm⁻¹, covering the absorption characteristics of various substances present in the water. Wavenumber feature extraction focuses on significant bands in the water quality spectral data, some of which correspond to absorption peaks of specific pollutants, such as the OH vibration absorption peak near 3000 cm⁻¹. During extraction, noise is removed by filtering the selected band intervals, and the wavenumber data is smoothed, for example, using a Savitzky-Golay filter, to improve data accuracy. Absorption peak intensity feature extraction involves detecting absorption peaks in the spectrum to determine their positions and intensities. Absorption peak intensity represents changes in pollutant concentration. In this process, absorption peak identification distinguishes peaks with significant intensity by setting a threshold, typically set to 50% of the maximum peak value, to ensure accurate identification. The final wavenumber data and absorption peak intensity data provide a foundation for subsequent pollutant analysis.

[0121] Step S22: Identify organic pollution sources in the wavenumber data to obtain organic pollution source data;

[0122] In this embodiment, wavenumber data is used to identify organic pollution sources. The process of identifying organic pollution sources relies on spectral analysis of specific wavenumber bands. For example, benzene compounds and chlorides have characteristic absorption peaks in infrared spectra, and benzene rings typically have significant absorption peaks in the 1600 cm⁻¹ and 1500 cm⁻¹ wavenumber regions. Based on these characteristics, wavenumber data can be used to identify organic pollution sources. First, the absorption peak regions of potential pollutants are determined through wavenumber data analysis. Then, eigenvalues ​​are extracted for each wavenumber band, and the absorption peak patterns within the band are analyzed. If a feature matching the absorption peak of a known organic pollutant appears in the wavenumber data, the pollutant can be considered to be present in the water body. Specific identification criteria include setting a tolerance range for wavenumber matching, typically ±2 cm⁻¹. Furthermore, multiple comparison methods, such as the correlation coefficient method, can be used to compare the spectral characteristics of known pollutants to further improve identification accuracy. Through these methods, organic pollution source data can ultimately be obtained, indicating the types and concentration levels of organic pollutants in the water body.

[0123] Step S23: Analyze the absorption peak intensity data to obtain organic pollution concentration data;

[0124] In this embodiment, organic pollutant concentration analysis is performed on the absorption peak intensity data. Using the absorption peak intensity data obtained in the previous step, absorption peaks associated with known organic pollutants are selected for concentration analysis. Concentration analysis relies on establishing a quantitative relationship between absorbance and concentration, which can be done using a calibration curve method. A series of standard solutions with known concentrations needs to be selected, and their corresponding absorbance values ​​are measured. The relationship between absorbance and concentration is obtained through linear regression, and a regression equation is calculated. This regression equation is used to convert the absorbance of the water sample into a concentration value. In actual analysis, absorbance is measured using a spectrophotometer, and the concentration is extrapolated by measuring the absorbance of the sample at a specific wavelength. To improve the accuracy of the analysis, repeated measurements can be performed, and the reliability of the results can be evaluated using statistical methods (such as standard deviation). By combining the absorbance data and the regression equation, the concentration data of organic pollutants in the water sample are obtained.

[0125] Step S24: Perform organic pollutant characteristic fusion based on organic pollution source data and organic pollution concentration data to obtain organic pollutant data.

[0126] In this embodiment, organic pollutant feature fusion is performed based on organic pollution source data and organic pollutant concentration data. The feature fusion process aims to integrate wavenumber data and absorption peak intensity data to provide more comprehensive information about organic pollutants. First, the organic pollution source data identified in step S22 is matched with the organic pollutant concentration data obtained in step S23. By comparing concentration data within the same wavenumber region with the pollution source data, a comprehensive feature for each pollutant is constructed. To ensure the accuracy of the fusion, a weighted average method is used to weight the concentrations of different pollutants to better reflect the actual impact of each pollutant. A weighting coefficient is set to assign weights to the influence of different pollutants based on their characteristics. For example, some organic pollutants (such as benzene compounds) have a greater environmental impact and are given higher weights. The feature-fused data will cover the types of pollution sources, concentrations, and the degree of impact on water quality, resulting in a comprehensive organic pollutant dataset. This dataset can be used for further pollutant source tracing analysis and water pollution control decisions.

[0127] Optionally, step S22 specifically includes:

[0128] Step S221: Extract the spectral region features of benzene compounds from the wavenumber data to obtain the spectral region data of benzene compounds;

[0129] In this embodiment, specific wavelength regions characteristic of benzene compounds are extracted from the infrared spectral data containing their characteristic absorption bands. Benzene compounds typically exhibit a distinct C=C vibrational absorption peak around 1500 cm⁻¹ and also show strong absorption characteristics around 1600 cm⁻¹. By extracting features from these wavelength regions, absorbance values ​​within these specific bands are determined. The selection of wavelength bands is strictly based on the infrared spectral characteristics of benzene compounds and verified using preset standards. These standards are set to ensure that the absorption intensity to noise ratio of benzene compounds within the specified wavelength range is greater than a certain threshold; for example, the peak absorbance intensity should be greater than 0.02 to ensure the accuracy of the wavelength regions. Furthermore, data smoothing is performed to remove noise from the spectrum, further improving the accuracy of feature extraction.

[0130] Step S222: Calculate the absorbance based on the wavelength region data of benzene compounds to obtain the absorbance data of benzene compounds;

[0131] In this embodiment, absorbance is calculated based on the spectral data of benzene compounds. After extracting the characteristic spectral bands of benzene compounds, the absorbance of the corresponding spectral bands is measured using a spectrophotometer. The absorbance (A) is calculated using Beer's Law, with the formula A = ε * c * l, where ε is the absorptivity, c is the concentration, and l is the optical path length. During the experiment, a standard solution of benzene compounds of known concentration is used for calibration to obtain the absorptivity ε of the corresponding spectral band. Subsequently, based on the measured spectral data, the absorbance is calculated within a specific spectral region. During absorbance calculation, background correction is first performed to remove baseline drift caused by the instrument or solvent, and the wavenumber range for absorbance calculation is set, for example, 1500 cm⁻¹ to 1600 cm⁻¹ as the target spectral band for benzene compounds. The obtained absorbance data reflects the concentration changes of benzene compounds in the water, providing data support for subsequent concentration analysis.

[0132] Step S223: Obtain absorbance data of benzene compounds at water quality monitoring points;

[0133] In this embodiment, absorbance data of benzene compounds at water quality monitoring points are acquired. Water quality monitoring points are deployed, and a spectrometer is used to periodically monitor the water quality at specified time intervals. The selection of monitoring points should cover areas affected by pollution sources, such as water bodies near industrial discharge outlets. The absorbance values ​​measured by the spectrometer are recorded according to changes in different locations and times. The measurement results are saved in the form of wavelength on the x-axis and absorbance on the y-axis, ensuring that the absorbance data of benzene compounds at each monitoring point can be correlated with the corresponding spatial coordinates. During the monitoring process, the water quality conditions at each monitoring point are recorded, and preliminary analysis is performed on the data, such as checking whether the measured values ​​meet quality control standards and whether the absorbance values ​​are within the preset normal range (e.g., 0.01-1.0). This data will provide a basis for subsequent spatial distribution analysis and pollution hotspot identification.

[0134] Step S224: Draw a spatial distribution map of absorbance values ​​based on the absorbance data of benzene compounds at water quality monitoring points to obtain spatial distribution map data of absorbance values, and identify the features of high absorbance value areas based on the spatial distribution map data of absorbance values ​​to obtain high absorbance value area data;

[0135] In this embodiment, a spatial distribution map of absorbance values ​​for benzene compounds is drawn based on absorbance data from water quality monitoring points. By collecting absorbance data of benzene compounds at water quality monitoring points, Geographic Information System (GIS) software is used to combine this data with corresponding geographic coordinates. Based on the absorbance values ​​at different locations, interpolation algorithms, such as Kriging interpolation or inverse distance weighted (IDW), are employed to draw a spatial distribution map of absorbance values ​​for the entire area. This map shows the distribution of benzene compound concentrations in different areas, with higher absorbance values ​​typically appearing near pollution sources. Based on the distribution map, high absorbance value areas are further identified. During identification, a threshold for absorbance values ​​is set, typically set to areas with absorbance greater than 0.2, which are marked as high absorbance value areas, indicating areas with more severe benzene pollution. Spatial analysis tools are used to identify these areas and perform spatial statistics to obtain high absorbance value area data, further supporting pollution source tracing.

[0136] Step S225: Plot the absorbance value over time curve based on the absorbance data of benzene compounds at the water quality monitoring points to obtain the absorbance value over time curve data, and statistically analyze the increase in absorbance value based on the absorbance value over time curve data to obtain the time data of the increase in high absorbance value.

[0137] In this embodiment, absorbance value over time curves are plotted based on the absorbance data of benzene compounds from water quality monitoring points. Using the aforementioned benzene compound absorbance data, the absorbance values ​​of each water quality monitoring point are recorded within a specified time period (e.g., daily, hourly). These data are then used to plot the absorbance value over time curves for each monitoring point. These curves demonstrate the trend of benzene compound concentration changes over time. During this process, time series analysis identifies periods of significant increase in absorbance, indicating a sudden release of pollutants. Next, the increase in absorbance is statistically analyzed based on the absorbance value over time curves to determine the increase in absorbance within a specific time period. Typically, periods with an increase exceeding a certain threshold (e.g., 0.1) are selected for statistical analysis. The obtained high absorbance value increase time data helps identify the time-sensitive characteristics of pollution sources.

[0138] Step S226: Perform pollution intersection calculation based on the time data of the increase in high absorbance value and the data of the high absorbance value region to obtain organic pollution source data.

[0139] In this embodiment, high absorbance regions are spatially and temporally matched with temporal data on the increase in high absorbance. Specifically, the geographical coordinates of high absorbance regions are combined with the temporal data on the increase in absorbance to analyze whether there is a concentrated pollution trend in these regions over time. Then, an intersection operation is performed, and overlay analysis is used to identify which areas exhibit significant pollution characteristics both temporally and spatially. For example, if a region shows a large increase in absorbance at multiple time points, this region can be identified as a potential source of pollution. Ultimately, the organic pollution source data obtained through the intersection operation will provide a basis for subsequent water quality treatment and pollution source tracing.

[0140] Optionally, step S23 specifically includes:

[0141] Step S231: Extract the absorption peak characteristics of benzene compounds from the absorption peak intensity data to obtain the absorption peak data of benzene compounds;

[0142] In this embodiment, an infrared spectrometer was used to acquire absorbance data from the water sample, with particular attention paid to the absorption peak regions in the characteristic bands of benzene compounds. Benzene compounds typically exhibit a series of typical absorption peaks in the infrared spectrum, located between 1500 cm⁻¹ and 1600 cm⁻¹. Based on experimental data, the wavelength corresponding to the maximum absorbance point was determined by calculating the absorbance at each wavenumber point within this band. Signal processing methods were employed, using Gaussian or Lorentzian fitting models to curve-fit the absorption peaks, extracting features such as peak position, width, and height for each peak. Specifically, an absorbance threshold, such as 0.05, was set; peaks below this threshold were excluded to ensure that the extracted peaks were significant absorption peaks. This process extracts the absorption peak data of benzene compounds, providing accurate peak information for subsequent analysis.

[0143] Step S232: Identify the peak regions based on the absorption peak data of benzene compounds to obtain the absorption peak region data of benzene compounds;

[0144] In this embodiment, peak regions are identified based on the absorption peak data of benzene compounds. After obtaining the absorption peak data of benzene compounds, peak regions are identified by defining characteristic intervals of the absorption peaks. Mathematical algorithms, such as peak detection algorithms or the second derivative method, are used to determine the start and end points of the absorption peaks. These regions are typically set as the range where the absorbance value is greater than a certain standard threshold (e.g., 0.05). To further accurately identify peak regions, local maximum detection technology is used to ensure that only the point with the highest absorbance is selected as the center of the peak. Subsequently, polynomial interpolation or three-point smoothing methods are used to optimize the peak boundaries and further confirm the peak regions. This process also requires setting a width limit for the absorption peaks, such as a width not exceeding 50 cm^-1. The benzene compound absorption peak region data identified in this way provides a clear wavelength range for subsequent concentration analysis.

[0145] Step S233: Perform concentration statistics on the absorbance data of benzene compounds to obtain the concentration data of benzene compounds;

[0146] In this embodiment, concentration statistics are performed on the absorbance data of benzene compounds. After obtaining the absorbance data of benzene compounds, the concentration is calculated using Beer's Law (A = εcl), where A is the absorbance, ε is the absorptivity, c is the concentration of the benzene compound, and l is the optical path length. First, the absorbance of a standard solution of known concentration in the laboratory is measured at a specific wavelength to obtain the absorptivity ε at that wavelength. Using the standard solution of known concentration and the measured absorbance value, the concentration of benzene compounds in the water sample is calculated by reversing the Beer's Law formula. During this process, the absorbance data of the water sample must be corrected to eliminate the influence of factors such as optical path length and instrument errors. Ultimately, the benzene compound concentration data obtained in this way will be helpful for subsequent pollutant analysis and concentration calibration.

[0147] Step S234: Plot a concentration calibration curve based on the absorbance data and concentration data of benzene compounds to obtain the concentration calibration curve data;

[0148] In this embodiment, a concentration calibration curve is plotted based on the absorbance and concentration data of benzene compounds. Absorbance data of standard solutions of benzene compounds at different concentrations are collected. Within the concentration range of the standard solutions, multiple concentration points (e.g., 1 ppm, 5 ppm, 10 ppm, etc.) are selected, and the absorbance value at each concentration is recorded. Then, these concentration data and corresponding absorbance values ​​are plotted as a scatter plot, and a linear regression method is used to fit the concentration-absorbance relationship. The curve generated by this fitting process is typically a straight line, representing the linear relationship between concentration and absorbance. To ensure data accuracy, a regression coefficient (R^2) greater than 0.99 is used as a standard to confirm the reliability of the linear relationship. Based on this concentration calibration curve, the absorbance value in the water sample can be accurately correlated with the concentration of benzene compounds, yielding accurate concentration values.

[0149] Step S235: Calculate the organic pollution concentration based on the absorption peak region data of benzene compounds according to the concentration calibration curve data, thereby obtaining the organic pollution concentration data.

[0150] In this embodiment, the absorbance data of benzene compounds is input into the concentration calibration curve, and a fitting formula is used to convert it into the concentration value of benzene compounds. During this process, if the absorbance data falls within the linear range of the concentration calibration curve, the concentration is directly calculated using the calibration curve. If the data exceeds the linear range of the calibration curve, an appropriate interpolation method or a reselection of the concentration range of the calibration solution is required. During the calculation, interfering substances in the water sample (such as moisture and suspended solids) also need to be considered, and corresponding interference corrections are performed to ensure the accuracy of the calculated benzene compound concentration. Ultimately, the organic pollution concentration data obtained through this method accurately reflects the content of benzene pollutants in the water quality, providing a basis for subsequent environmental monitoring and pollution control.

[0151] Optionally, step S3 specifically includes:

[0152] Step S31: Perform inductively coupled plasma analysis based on water quality data to obtain inductively coupled plasma data;

[0153] In this embodiment, water quality data of a water sample is acquired and then fed into an inductively coupled plasma optical spectrometer (ICP-OES). During this process, the water sample is ionized into ions and atoms by a plasma source. The high temperature (typically 6000-8000K) generated by the inductively coupled plasma inside the ICP-OES instrument excites the metal elements in the water into free atoms and ions. The water sample is then introduced into the inductively coupled plasma via a gas flow and interacts with electromagnetic waves. The instrument detects the emission spectra of these metal elements at specific wavelengths, and by measuring the intensity of the emission spectra, the concentration of each element can be determined. During this process, appropriate measurement wavelengths are set to analyze the heavy metal elements contained in the water sample, such as lead (Pb) and copper (Cu). To ensure measurement accuracy, a reasonable excitation power (e.g., 1200W) and an appropriate radio frequency (e.g., 27.12MHz) must be set. Using these parameters, inductively coupled plasma data are obtained.

[0154] Step S32: Ionize the heavy metal elements in the inductively coupled plasma data to obtain heavy metal ion data;

[0155] In this embodiment, inductively coupled plasma (ICP) data is post-processed to analyze metal elements in the water sample. Metal elements in the water sample are ionized in the plasma, transforming into positively charged ions (e.g., lead ions Pb+, copper ions Cu+). Utilizing the high temperature of the plasma (typically greater than 6000 K), the outer electrons of the metal elements gain sufficient energy to escape the atomic nucleus and form ions. This process is primarily achieved by analyzing the degree of ionization in the ICP. The degree of ionization is typically influenced by factors such as gas temperature, pressure, and excitation power. To ensure analytical accuracy, appropriate operating conditions must be set according to the ionization potential of different metal elements. For example, in the ionization of lead ions, a standard excitation frequency and power are used to ensure complete ionization of lead. The heavy metal ion data obtained through this step provides the necessary material basis for subsequent mass spectrometry analysis.

[0156] Step S33: Perform a mass spectrometer simulation based on the heavy metal ion data to obtain mass spectrometry data;

[0157] In this embodiment, heavy metal ion data is imported into a mass spectrometer for subsequent analysis. Data simulation and analysis are performed using a mass spectrometer (e.g., ICP-MS). First, heavy metal ions are guided to the ion source of the mass spectrometer through electronic excitation, generating ions with a specific mass-to-charge ratio (m / z). During this process, the mass spectrometer separates the ions using electric and magnetic fields, and analyzes them based on their mass-to-charge ratio. During the simulation, appropriate ionization gas flow rate and ion source voltage (e.g., 3.0 kV) must be set to ensure efficient entry of ions into the mass spectrometry analysis region. During the import simulation, according to the instrument settings (e.g., ion source temperature of 2000 °C, gas flow rate of 0.8 L / min), heavy metal ions are introduced into the quadrupole or magnetic mass analyzer of the mass spectrometer for further processing. The accuracy of the mass spectrometry data depends on the stability of this import step; therefore, strict control of temperature, gas flow, and voltage standardization is necessary.

[0158] Step S34: Perform heavy metal mass spectrometry analysis based on the mass spectrometry data to obtain heavy metal data.

[0159] In this embodiment, mass spectrometry data is used for detailed analysis of heavy metal ions. Heavy metal ions are separated using a mass spectrometer, and their respective mass-to-charge ratios (m / z) are recorded. For example, the mass-to-charge ratio of lead ions (Pb+) is 207.2, and that of copper ions (Cu+) is 63.5. During the mass spectrometry analysis, precise mass analysis is used to determine the specific peak position of each metal element, and quantitative analysis is performed. Standard solutions are used to calibrate the mass spectrometer to ensure that the concentration of the metal element corresponding to each peak can be accurately calculated. In this process, background noise elimination and interference peak processing need to be considered in heavy metal mass spectrometry analysis; therefore, refined scanning techniques (such as progressively increasing the resolution to 0.1 Da) are employed to ensure data accuracy. Ultimately, the heavy metal mass spectrometry data will reflect the concentration distribution of each heavy metal element in the water sample, providing a basis for subsequent water quality assessment and pollution source analysis.

[0160] Optionally, step S32 specifically includes:

[0161] Step S321: Extract water quality metal element features from inductively coupled plasma data to obtain water quality metal element data;

[0162] In this embodiment, inductively coupled plasma optical emission spectrometry (ICP-OES) is used to acquire emission spectral data of atoms and ions in the water sample. Water quality data typically contains information on multiple metal elements. By analyzing the characteristic wavelengths of different metal elements (e.g., lead's characteristic wavelength is 220.353 nm, and copper's is 324.754 nm), the metal elements in the water sample are characterized. Fourier transform or wavelet transform is used to denoise and filter the spectral data to improve the accuracy of metal element identification. The extracted characteristic data includes the peak intensity, peak width, and corresponding wavelength position of the element concentration. By comparing with the characteristic spectra of known standard metal elements, the concentration data of different metal elements in the water sample are extracted, such as determining the concentration of heavy metals like lead, cadmium, and copper. This data processing step requires high spectral resolution (e.g., 0.1 nm) to ensure effective differentiation of the spectral lines of different metal elements.

[0163] Step S322: Perform heavy metal element gasification based on water quality metal element data to obtain heavy metal atom gasification data;

[0164] In this embodiment, water quality metal element data is fed into a high-temperature gasification system. Plasma gasification technology is typically used, where metal elements are converted to an atomic state in an inductively coupled plasma source using a gasification temperature (usually set around 8000K). During this process, the metal elements are heated by the high temperature of the plasma, exciting electrons that collide with gas molecules, causing the metal elements to evaporate and form gas. Specifically, heavy metal elements such as lead (Pb) and copper (Cu) are gasified in this high-temperature environment, transforming into free metal atoms, which are then detected by a mass spectrometer. The gasification efficiency in this step is affected by the gas flow rate, plasma temperature, and the volatility of the metal elements. Appropriate gas flow rates (e.g., 1 L / min) and radio frequency power (e.g., 1200 W) need to be set to ensure complete gasification of the metal elements.

[0165] Step S323: Excite heavy metal atoms at high temperature to obtain excited state data of heavy metal atoms by performing high-temperature excitation on the heavy metal atom vaporization data;

[0166] In this embodiment, the heavy metal atom vaporization data are subjected to high-temperature excitation of the metal atoms. After vaporization, the heavy metal atoms enter an inductively coupled plasma (ICP) region, where the temperature is maintained between 6000K and 8000K, sufficient to excite the vaporized metal atoms to an excited state. The stability of the ICP is ensured by setting the radio frequency power to 1200W. This process utilizes the high temperature of the plasma to excite the electrons of the metal atoms to higher energy levels, placing the metal atoms in an excited state. These excited-state atoms then return to their ground state and emit radiation of characteristic wavelengths, which are collected by a spectrometer. Analysis of these spectral signals extracts the energy characteristics of the excited-state atoms, thereby identifying and quantifying each metal element. For each metal element, analysis is performed using a specific excitation wavelength; for example, lead (Pb) has a characteristic spectrum of 280.6 nm in the excited state. This step obtains the excited-state data of heavy metal atoms, providing a foundation for subsequent ionization analysis.

[0167] Step S324: High-temperature ionization of the excited state data of heavy metal atoms to obtain heavy metal ion data.

[0168] In this embodiment, excited-state metal atoms are converted into metal ions by further heating within the plasma region, utilizing high-temperature ionization. During the ionization phase of this process, ionization efficiency can be improved by adjusting the plasma temperature (typically set to 6500K to 8000K) and radio frequency power (e.g., 1150W). After absorbing sufficient energy in this phase, the metal atoms detach their electrons from the nucleus, forming positively charged ions. For example, lead (Pb) is converted into Pb+ ions, and copper (Cu) is converted into Cu+ ions. The efficiency of the ionization process is also affected by the plasma atmosphere, flow rate, and the state of the excitation source. Through the ion source of the mass spectrometer, the heavy metal ions enter the mass spectrometer analyzer, where they are separated and detected according to their mass-to-charge ratio (m / z). The result of this step is the acquisition of heavy metal ion data, further providing accurate quantitative data for mass spectrometry analysis.

[0169] Optionally, step S34 specifically includes:

[0170] Step S341: Draw a mass spectrum based on the mass spectrometry data to obtain the mass spectrum data;

[0171] In this embodiment, the raw mass spectrometry data acquired from the mass spectrometer is imported into specialized data processing software, such as Origin or Matlab. Mass spectrometry data is typically recorded in the form of mass-to-charge ratio (m / z) and signal intensity. To generate a mass spectrum, the data needs to be normalized according to the mass-to-charge ratio range (e.g., 50 to 300 m / z) to ensure the signal intensity is within the normalized range of 0 to 1. When plotting, the X-axis represents the mass-to-charge ratio, and the Y-axis represents the signal intensity. Data processing must be accurate to two decimal places, using an appropriate resolution (e.g., 1 Da) to ensure clear signal visibility. When plotting the mass spectrum, the presence and concentration of each element or compound can be determined based on the ion peaks in the graph. In practice, the plotting process must eliminate background noise to ensure accurate mass spectrum data.

[0172] Step S342: Identify the lead element mass-to-charge ratio region in the mass spectrum data to obtain the lead element mass-to-charge ratio region data;

[0173] In this embodiment, the characteristic peaks of lead (Pb) are located by analyzing the mass spectrum. Based on the known mass-to-charge ratios of lead isotopes in the literature, a mass-to-charge ratio range between 205 m / z and 208 m / z is selected. Typically, the main isotopes of lead are Pb+ (m / z = 207) and Pb²+ (m / z = 103.5). Within this region, an automatic peak identification algorithm (such as the Peak Detection algorithm based on gradient or smoothing) is used to identify the characteristic peak regions of lead by setting a minimum peak height threshold (e.g., 20% of the highest peak intensity). If multiple overlapping peaks are present, the boundaries of the lead peaks are further refined using a peak resolution algorithm (such as Gaussian fitting). The goal of this step is to accurately extract the mass-to-charge ratio region data corresponding to lead for subsequent analysis and calculation.

[0174] Step S343: Extract the peak features of lead element based on the mass-to-charge ratio regional data of lead element to obtain the peak data of lead element;

[0175] In this embodiment, lead peak features are extracted based on the lead mass-to-charge ratio region data. After identifying the lead mass-to-charge ratio region, features of the peaks in that region are extracted. Peak detection algorithms (such as Fourier transform or wavelet transform) are used to process the signal in that region to obtain the peak information of lead. Specific steps include: 1) locating the maximum value of the peak and its corresponding mass-to-charge ratio; 2) calculating the half-width at half-maximum (FWHM) of the peak to evaluate its shape; 3) extracting the symmetry and distribution of the peak using a Gaussian curve fitting algorithm to confirm the presence of interference signals. The peak height, width, and symmetry can be used to further determine the concentration of lead in the sample. Through the above process, accurate peak data of lead is obtained, providing necessary parameters for the next step of peak area and peak height calculation.

[0176] Step S344: Calculate the peak area of ​​the lead element peak data to obtain the peak area data of the lead element.

[0177] In this embodiment, after extracting the peak characteristics of lead, its peak area needs to be calculated for further concentration analysis. When calculating the peak area, a numerical integration method (such as the trapezoidal rule or Simpson's rule) is used to integrate the region under the lead peak. The integration range for this region starts from the baseline and ends at the peak's maximum point. For a single lead peak, the influence of background noise should be considered during the calculation; the background signal needs to be removed before calculating the peak area, typically by setting a background noise threshold for filtering. The calculation result is corrected using the peak area and the response factor of lead to obtain the relative concentration of lead. The integration accuracy in this step needs to reach three decimal places to ensure the accuracy of the calculation results.

[0178] Step S345: Calculate the peak height of lead element peak data to obtain the peak height data of lead element.

[0179] In this embodiment, peak height refers to the maximum signal intensity from the baseline to the peak apex. Lead element peaks are identified based on peak characteristic data. Determining the baseline signal intensity is crucial during the calculation process; typically, a baseline fitting model (such as linear or polynomial fitting) is used to eliminate background signal interference. Then, the peak height is obtained by measuring the difference between the maximum peak intensity and the baseline. During operation, a peak identification algorithm automatically calculates the maximum value of each peak. If multiple overlapping peaks exist, deconvolution processing (such as waveform deconvolution) is performed to ensure accurate acquisition of lead element peak height data. Peak height is directly proportional to the concentration of lead element, making it an important parameter in concentration analysis.

[0180] Step S346: Calculate the lead concentration based on the lead peak area data and lead peak height data to obtain the lead concentration data;

[0181] In this embodiment, the calculation of lead concentration requires a known calibration curve and standard sample concentrations. A series of lead standard solutions with known concentrations are pre-set experimentally, and their corresponding peak areas and heights are obtained using a mass spectrometer. A linear regression relationship between peak area and concentration is established to derive the concentration calculation formula. For the lead element in the sample, the actual measured peak area or height data is substituted into the calculation formula to obtain the lead concentration value. The fitting of the calibration curve must ensure high accuracy, typically requiring R0. 2 A value of 0.999 or higher is required to ensure the accuracy of concentration calculations. Furthermore, regular instrument calibration is necessary to ensure measurement accuracy.

[0182] Step S347: Perform heavy metal feature fusion based on lead element peak data and lead element concentration data to obtain heavy metal data.

[0183] In this embodiment, a comprehensive analysis of heavy metal characteristics is performed by combining lead peak data with its concentration data. The mass spectrometry data of lead is compared and fused with peak data of other heavy metals (such as mercury and cadmium), and a concentration relationship model between lead and other metals is established using a multiple linear regression method. This process, by introducing the influencing factors of other metals and combining lead mass spectrometry and concentration data, further improves the accuracy of heavy metal pollution analysis. During the fusion process, it is necessary to ensure the consistency of response factors and calibration curves for different elements to avoid cross-interference. Ultimately, the heavy metal data obtained through feature fusion not only provides accurate lead concentrations but also helps determine the pollution status of other heavy metals in water.

[0184] Optionally, this specification also provides a water pollution control system based on water quality analysis for performing the water pollution control method based on water quality analysis as described above. The water pollution control system based on water quality analysis includes:

[0185] Infrared spectroscopy scanning module: used to acquire operating data of wastewater treatment equipment, extract water quality characteristics based on the operating data of wastewater treatment equipment, and thus obtain water quality data; and to perform infrared spectroscopy scanning based on the water quality data to obtain infrared spectral water quality data;

[0186] Organic pollutant analysis module: used to extract wavenumber features and absorption peak intensity features from infrared spectral water quality data to obtain wavenumber data and absorption peak intensity data; and to analyze organic pollutants based on the wavenumber data and absorption peak intensity data to obtain organic pollutant data.

[0187] Heavy metal mass spectrometry analysis module: used to perform inductively coupled plasma analysis based on water quality data to obtain plasma data; and to perform heavy metal mass spectrometry analysis based on plasma data to obtain heavy metal data.

[0188] Pollution hotspot distribution area identification module: used to draw water pollution concentration distribution maps based on organic pollutant data and heavy metal data, thereby obtaining water pollution concentration distribution maps; and to identify pollution hotspot distribution areas based on water pollution concentration distribution maps, thereby obtaining pollution hotspot distribution area data.

[0189] Water pollution control module: Used to control water pollution based on data on the distribution of pollution hotspots, thereby obtaining water pollution control data and uploading it to the industrial wastewater management platform to execute industrial wastewater pollution control tasks.

[0190] The present invention discloses a water pollution control system based on water quality analysis. This system can implement any water pollution control method based on water quality analysis according to the present invention. It serves as a medium for coordinating the operation and signal transmission between various modules to complete the water pollution control method based on water quality analysis. The modules within the system cooperate with each other to optimize the efficiency of water pollution control.

[0191] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0192] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A water pollution treatment method based on water quality analysis, characterized in that, Includes the following steps: Step S1: Obtain the operating data of the wastewater treatment equipment, and extract water quality characteristics based on the operating data of the wastewater treatment equipment to obtain water quality data; Infrared spectral scanning is performed based on water quality data to obtain infrared spectral water quality data; Step S2: Extract wavenumber features and absorption peak intensity features from infrared spectroscopy water quality data to obtain wavenumber data and absorption peak intensity data; analyze organic pollutants based on wavenumber data and absorption peak intensity data to obtain organic pollutant data. Step S3: Perform inductively coupled plasma analysis based on water quality data to obtain plasma data; perform heavy metal mass spectrometry analysis based on plasma data to obtain heavy metal data; Step S4: Draw a water pollution concentration distribution map based on organic pollutant data and heavy metal data to obtain a water pollution concentration distribution map; identify pollution hotspot distribution areas based on the water pollution concentration distribution map to obtain pollution hotspot distribution area data. Step S5: Conduct water pollution treatment based on the data of pollution hotspot distribution areas to obtain water pollution treatment data, and upload it to the industrial wastewater management platform to execute industrial wastewater pollution treatment tasks.

2. The water pollution treatment method based on water quality analysis according to claim 1, characterized in that, Step S1 is as follows: Step S11: Obtain the operating data of the wastewater treatment equipment, and extract water quality characteristics based on the operating data of the wastewater treatment equipment to obtain water quality data; Step S12: Acquire pollutant spectral data and moisture interference band data; Step S13: Simulate pollutant absorption peaks based on pollutant spectral data and water quality data to obtain pollutant absorption peak data; Step S14: Perform piecewise linear fitting on the moisture interference band data to obtain the moisture interference baseline data; Step S15: Remove moisture interference peaks from the baseline data of moisture interference and correct the data of pollutant absorption peaks. Step S16: Generate infrared spectral water quality data based on pollutant absorption peak correction data.

3. The water pollution treatment method based on water quality analysis according to claim 2, characterized in that, Step S14 is as follows: Step S141: Divide the water interference band data into band intervals to obtain water interference band interval data; Step S142: Perform piecewise least squares fitting based on the moisture interference band interval data to obtain piecewise least squares fitted data. Step S143: Set the optimal slope based on the piecewise least squares fitting data to obtain the optimal slope data; Step S144: Set the optimal intercept based on the piecewise least squares fitted data to obtain the optimal intercept data; Step S145: Construct a moisture disturbance baseline based on the optimal slope data and the optimal intercept data to obtain moisture disturbance baseline data.

4. The water pollution treatment method based on water quality analysis according to claim 1, characterized in that, Step S2 is as follows: Step S21: Extract wavenumber features and absorption peak intensity features from infrared spectral water quality data to obtain wavenumber data and absorption peak intensity data; Step S22: Identify organic pollution sources in the wavenumber data to obtain organic pollution source data; Step S23: Analyze the absorption peak intensity data to obtain organic pollution concentration data; Step S24: Perform organic pollutant characteristic fusion based on organic pollution source data and organic pollution concentration data to obtain organic pollutant data.

5. The water pollution treatment method based on water quality analysis according to claim 4, characterized in that, Step S22 is as follows: Step S221: Extract the spectral region features of benzene compounds from the wavenumber data to obtain the spectral region data of benzene compounds; Step S222: Calculate the absorbance based on the wavelength region data of benzene compounds to obtain the absorbance data of benzene compounds; Step S223: Obtain absorbance data of benzene compounds at water quality monitoring points; Step S224: Draw a spatial distribution map of absorbance values ​​based on the absorbance data of benzene compounds at water quality monitoring points to obtain spatial distribution map data of absorbance values, and identify the features of high absorbance value areas based on the spatial distribution map data of absorbance values ​​to obtain high absorbance value area data; Step S225: Plot the absorbance value over time curve based on the absorbance data of benzene compounds at the water quality monitoring points to obtain the absorbance value over time curve data, and statistically analyze the increase in absorbance value based on the absorbance value over time curve data to obtain the time data of the increase in high absorbance value. Step S226: Perform pollution intersection calculation based on the time data of the increase in high absorbance value and the data of the high absorbance value region to obtain organic pollution source data.

6. The water pollution treatment method based on water quality analysis according to claim 4, characterized in that, Step S23 is as follows: Step S231: Extract the absorption peak characteristics of benzene compounds from the absorption peak intensity data to obtain the absorption peak data of benzene compounds; Step S232: Identify the peak regions based on the absorption peak data of benzene compounds to obtain the absorption peak region data of benzene compounds; Step S233: Perform concentration statistics on the absorbance data of benzene compounds to obtain the concentration data of benzene compounds; Step S234: Plot a concentration calibration curve based on the absorbance data and concentration data of benzene compounds to obtain the concentration calibration curve data; Step S235: Calculate the organic pollution concentration based on the absorption peak region data of benzene compounds according to the concentration calibration curve data, thereby obtaining the organic pollution concentration data.

7. The water pollution treatment method based on water quality analysis according to claim 1, characterized in that, Step S3 is as follows: Step S31: Perform inductively coupled plasma analysis based on water quality data to obtain inductively coupled plasma data; Step S32: Ionize the heavy metal elements in the inductively coupled plasma data to obtain heavy metal ion data; Step S33: Perform a mass spectrometer simulation based on the heavy metal ion data to obtain mass spectrometry data; Step S34: Perform heavy metal mass spectrometry analysis based on the mass spectrometry data to obtain heavy metal data.

8. The water pollution treatment method based on water quality analysis according to claim 1, characterized in that, Step S32 is as follows: Step S321: Extract water quality metal element features from inductively coupled plasma data to obtain water quality metal element data; Step S322: Perform heavy metal element gasification based on water quality metal element data to obtain heavy metal atom gasification data; Step S323: Excite heavy metal atoms at high temperature to obtain excited state data of heavy metal atoms by performing high-temperature excitation on the heavy metal atom vaporization data; Step S324: High-temperature ionization of the excited state data of heavy metal atoms to obtain heavy metal ion data.

9. The water pollution treatment method based on water quality analysis according to claim 8, characterized in that, Step S34 is as follows: Step S341: Draw a mass spectrum based on the mass spectrometry data to obtain the mass spectrum data; Step S342: Identify the lead element mass-to-charge ratio region in the mass spectrum data to obtain the lead element mass-to-charge ratio region data; Step S343: Extract the peak features of lead element based on the mass-to-charge ratio regional data of lead element to obtain the peak data of lead element; Step S344: Calculate the peak area of ​​the lead element peak data to obtain the peak area data of the lead element. Step S345: Calculate the peak height of lead element peak data to obtain the peak height data of lead element. Step S346: Calculate the lead concentration based on the lead peak area data and lead peak height data to obtain the lead concentration data; Step S347: Perform heavy metal feature fusion based on lead element peak data and lead element concentration data to obtain heavy metal data.

10. A water pollution treatment system based on water quality analysis, characterized in that, For performing the water pollution control method based on water quality analysis as described in claim 1, the water pollution control system based on water quality analysis comprises: Infrared spectroscopy scanning module: used to acquire operating data of wastewater treatment equipment, extract water quality characteristics based on the operating data of wastewater treatment equipment, and thus obtain water quality data; and to perform infrared spectroscopy scanning based on the water quality data to obtain infrared spectral water quality data; Organic pollutant analysis module: used to extract wavenumber features and absorption peak intensity features from infrared spectral water quality data to obtain wavenumber data and absorption peak intensity data; and to analyze organic pollutants based on the wavenumber data and absorption peak intensity data to obtain organic pollutant data. Heavy metal mass spectrometry analysis module: used to perform inductively coupled plasma analysis based on water quality data to obtain plasma data; and to perform heavy metal mass spectrometry analysis based on plasma data to obtain heavy metal data. Pollution hotspot distribution area identification module: used to draw water pollution concentration distribution maps based on organic pollutant data and heavy metal data, thereby obtaining water pollution concentration distribution maps; and to identify pollution hotspot distribution areas based on water pollution concentration distribution maps, thereby obtaining pollution hotspot distribution area data. Water pollution control module: Used to control water pollution based on data on the distribution of pollution hotspots, thereby obtaining water pollution control data and uploading it to the industrial wastewater management platform to execute industrial wastewater pollution control tasks.

Images