Water quality pollution multi-dimensional analysis system

Abstract

The invention relates to the technical field of water pollution detection, in particular to a water quality pollution multi-dimensional analysis system which comprises the following steps: synchronously measuring an ultraviolet-visible absorption spectrum of a water sample to be detected and scattered light intensity in an orthogonal direction, and deducting spectrum baseline interference caused by suspended particulate matters in the water sample through a correction algorithm; the interferents in the water sample are selectively converted and degraded by applying a programmed potential to the inflowing water sample to be detected; the reverse pulse is periodically applied, so that the online regeneration function of the electrode is realized; analyzing the spectral data by adopting a multivariate correction algorithm, and calculating the concentration of the target pollutant; the calculation residual error of the multivariate correction algorithm is analyzed, new interference and system performance drift of current measurement are diagnosed, and a corresponding system state code is generated; and responding to the system state code, automatically starting and carrying out state checking and multi-point calibration on the instrument, and using a calibration result to update the analysis model. Through multi-dimensional perception and closed-loop operation, accuracy and reliability of water quality monitoring are guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of water pollution detection, and in particular to a multi-dimensional analysis system for water pollution. Background Art

[0002] Online water quality monitoring is an important technical means to achieve environmental protection and refined industrial management. Existing online analyzers typically successfully apply mature analytical technologies such as optics and electrochemistry, which have performed well in laboratories, to automated and continuous online measurements, forming the technical foundation of this field.

[0003] In practical applications, these analyzers are deployed in the field, where environmental conditions vary widely and require long-term, continuous operation. In these situations, the tooling and testing conditions faced by the analyzers are far more complex than those in a laboratory environment, presenting significant technical challenges in maintaining the accuracy and consistency of their analytical results. For example, the composition of on-site water bodies changes dynamically, and suspended matter, colloids, or coexisting chemicals can all affect the measurement signal. Furthermore, the response characteristics of key components such as the instrument's light source and sensor can naturally and gradually change over time. Existing online monitoring technologies struggle to guarantee the accuracy and consistency of their analytical results under realistic, continuous operating conditions.

[0004] Therefore, a multi-dimensional analysis system for water pollution is proposed. Summary of the Invention

[0005] The purpose of the present invention is to provide a multi-dimensional analysis system for water pollution, which ensures accurate and reliable water quality monitoring through multi-dimensional perception and closed-loop operation. It includes synchronously measuring the ultraviolet-visible absorption spectrum of the water sample to be tested and the scattered light intensity in orthogonal directions, and using a correction algorithm to deduct the spectral baseline interference caused by suspended particulate matter in the water sample; applying a programmed potential to the inflowing water sample to be tested to selectively transform and degrade the interference in the water sample; periodically applying reverse pulses to achieve the function of online electrode regeneration; using a multivariate correction algorithm to analyze the spectral data and calculate the concentration of the target pollutant; analyzing the calculation residuals of the multivariate correction algorithm, diagnosing new interference and system performance drift in the current measurement, and generating a corresponding system status code; in response to the system status code, automatically starting and performing status verification and multi-point calibration on the instrument, and using the calibration results to update the analysis model.

[0006] To achieve the above object, the present invention provides the following technical solutions: A multi-dimensional water pollution analysis system, comprising: Optical detection and correction module: Synchronously measures the UV-visible absorption spectrum of the water sample to be tested and the scattered light intensity in orthogonal directions; uses the scattered light intensity data and a correction algorithm to deduct the spectral baseline interference caused by suspended particulate matter in the water sample in real time; Electrochemical treatment module: By applying a programmed potential optimized for known interferents to the incoming water sample, it selectively transforms and / or degrades the interferents in the water sample, reducing the background complexity of the spectral analysis; periodically applying reverse pulses to achieve online electrode regeneration; Data processing and status diagnosis module: A built-in analysis model uses a multivariate correction algorithm to analyze spectral data and calculate the concentration of target pollutants. By analyzing the calculation residuals of the analysis model, it diagnoses new interferences in the current measurement and system performance drift, and generates corresponding system status codes. Automatic maintenance and calibration module: responds to the system status code, performs status verification and multi-point calibration on the instrument, and uses the calibration results to update the analysis model.

[0007] Preferably, the optical detection and correction module performs spectrum compensation operations, including: a feature extraction unit obtains a complete scattering spectrum covering the ultraviolet to near-infrared bands, and processes it into a high-dimensional feature vector; a neural network operation unit based on a pre-trained model receives the high-dimensional feature vector, and predicts and outputs a scattering interference spectrum baseline corresponding to the current water sample matrix characteristics through a nonlinear mapping model; a compensation operation unit subtracts the scattering interference spectrum baseline predicted by the neural network operation unit from the original absorption spectrum measured by the optical detection and correction module, thereby achieving dynamic compensation of the spectrum.

[0008] Preferably, the electrochemical treatment module has a built-in evaluation system, which includes an electrochemical impedance spectroscopy analysis unit, a transient response capture unit and a comprehensive diagnostic controller; An electrochemical impedance spectroscopy analysis unit applies a preset DC bias to the electrode between two routine measurement tasks, superimposes a multi-frequency sinusoidal AC voltage on the DC bias, synchronously collects the AC current response, and calculates the electrochemical impedance spectrum. By analyzing changes in characteristic parameters related to charge transfer resistance or double-layer capacitance in the impedance spectrum, the attenuation of electrode activity and effective surface area caused by insulating material coverage is quantitatively evaluated to obtain quantitative indicators; The transient response capture unit synchronously records the complete current response curve caused by the regeneration pulse when the module executes the periodic electrode online regeneration program, and extracts the peak current, rise time and total charge from the response curve to obtain characteristic parameters representing the surface chemical kinetic state of the electrode; The integrated diagnostic controller receives quantitative indicators from the electrochemical impedance spectroscopy analysis unit and characteristic parameters from the transient response capture unit, compares the multi-dimensional diagnostic input with the preset feature space that defines different electrode states, judges the overall health status of the electrode and decides whether an enhanced cleaning procedure is needed.

[0009] Preferably, the built-in analysis model of the data processing and status diagnosis module includes: a global quantitative analysis unit, which integrates correction algorithms for target pollutants and all known major interferents, directly receives spectral data and synchronously outputs preliminary concentration estimates of each component; a residual analysis unit, which monitors the calculation residuals generated by the global quantitative analysis unit in real time, and diagnoses whether there are unidentified new interferences and system performance drift by analyzing whether they deviate from the normal random noise baseline; a nonlinear unit, which receives the preliminary concentration estimate output by the global quantitative analysis unit, compensates for the nonlinear effects caused by chemical interactions between components, and calculates and outputs the final pollutant concentration.

[0010] Preferably, the data processing and status diagnosis module is used to calculate the presence of new interference in the residual using a residual spectrum matching and identification process, including: a residual spectrum acquisition unit, used to extract the calculated residual spectrum generated by the global quantitative analysis unit; a spectrum matching unit, which performs feature comparison on the residual spectrum with a preset standard spectrum database containing a variety of potential unknown interferents and calculates the spectral similarity; an interference identification unit, which confirms that there is an unmodeled interference corresponding to an entry in the database in the current water sample when the spectral similarity exceeds a preset confidence threshold, and outputs identity information as a diagnosis result.

[0011] Preferably, the evaluation mechanism adopted by the generation logic of the system status code has a bimodal characteristic: any deviation event that exceeds a preset narrow threshold during the system's stability verification period will be immediately identified as a high-level alarm event and directly trigger the corresponding diagnostic procedure; for deviation events that occur after the stability verification period, the system starts a time series accumulator for weighted scoring to continuously track and evaluate the long-term performance drift of the system.

[0012] Preferably, after the automatic maintenance and calibration module determines that the calibration has failed, the system immediately enters a safety lock mode. In the safety lock mode, the system suspends routine measurements and data output, and automatically executes a hardware fault diagnosis subroutine using sequential inspection logic; the subroutine checks the hardware units one by one in a preset order, and the inspection order is: check the light intensity of the light source, if the light intensity of the light source is normal, check the cleanliness of the circulation pool, if the cleanliness of the circulation pool is normal, check the fluid pressure; the subroutine will terminate after detecting the first unqualified hardware unit, and generate a guiding maintenance suggestion containing specific fault unit information.

[0013] Preferably, it also includes a data recording mechanism, which formats routine measurement results, hardware status snapshots and key operation events into log entries in a unified manner, and the data block structure of the log entries, the generation of encrypted hash pointers, and the operation input is spliced ​​data, and the spliced ​​data includes: the encrypted hash pointer of the previous data block and the serialized data stream of all information in the current data block; the information in the data block is uniformly formatted and can be recorded in a single data chain in timestamp order, and the routine measurement results, hardware status snapshots and key operation events including system maintenance, calibration and status alarm information are uniformly recorded in a single data chain, and the overall non-tamperability and traceability of all types of historical data are guaranteed through a single hash chain.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. By combining active purification of chemical interferences in water samples with precise correction of physical background interference, the influence of the complex matrix of real water bodies on measurement results is effectively suppressed. The analysis system can still obtain highly reliable and accurate measurement data under conditions of turbidity and coexistence of multiple components, thereby improving the universality and reliability of online monitoring technology in practical applications.

[0015] 2. By giving the system intelligent self-diagnosis and closed-loop maintenance capabilities, effective monitoring and proactive management of the long-term operating status of the instrument are achieved. The system autonomously identifies potential deviations and automatically performs maintenance processes such as calibration to restore its performance, enhancing the consistency of long-term monitoring data and reducing the difficulty and cost of operation and maintenance.

[0016] 3. By incorporating all measurement data, system status and operation events into a data chain with inherent security mechanisms and cannot be tampered with, a solid guarantee is provided for the full-cycle traceability of the monitoring process, ensuring the originality and integrity of historical records, so that each piece of data has a reliable "credential of trust". BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic diagram of the structure of a multi-dimensional water pollution analysis system provided by an embodiment of the present invention; Figure 2 A schematic diagram of the electrochemical treatment module flow chart provided in an embodiment of the present invention; Figure 3 This is a flow chart of the data processing and status diagnosis module provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0019] See also Figures 1 to 3 The present invention provides a multi-dimensional water pollution analysis system, and the technical solution is as follows: A multi-dimensional water pollution analysis system, the structure is as follows Figure 1 As shown: Optical detection and correction module: Synchronously measures the UV-visible absorption spectrum of the water sample to be tested and the scattered light intensity in orthogonal directions; uses the scattered light intensity data and a correction algorithm to deduct the spectral baseline interference caused by suspended particulate matter in the water sample in real time; Electrochemical treatment module: By applying a programmed potential optimized for known interferents to the incoming water sample, it selectively transforms and / or degrades the interferents in the water sample, reducing the background complexity of the spectral analysis; periodically applying reverse pulses to achieve online electrode regeneration; The specific method of periodically applying reverse pulses is as follows: after every 100 measurements, the system automatically performs a regeneration procedure, applying a constant reduction potential of -0.6 volts (relative to the Ag / AgCl reference electrode) to the working electrode (glassy carbon electrode) for 120 seconds. This potential and duration are sufficient to reduce and desorb lightly adsorbed oxidizing species on the electrode surface while avoiding damage to the electrode substrate, thereby restoring electrode activity.

[0020] To achieve adaptive pretreatment for specific interferents, the electrochemical treatment module also integrates an expert knowledge base. This base stores the characteristic spectra of several known potential interferents and associates them with corresponding, optimized electrochemical pretreatment program parameters (potential, duration). When the system identifies an interferent that matches a characteristic spectrum stored in this knowledge base through residual spectral analysis, it automatically calls the associated pretreatment program parameters and applies them to the subsequent water sample purification process. This achieves closed-loop automatic control without manual intervention, significantly improving the system's operational reliability and adaptability in complex environments.

[0021] Built-in analysis model, which uses a multivariate correction algorithm to analyze spectral data and calculate the concentration of target pollutants; by analyzing the calculation residuals of the analysis model, it can diagnose new interferences and system performance drift in the current measurement and generate corresponding system status codes; Automatic maintenance and calibration module: responds to the system status code, performs status verification and multi-point calibration on the instrument, and uses the calibration results to update the analysis model.

[0022] The automatic maintenance and calibration module is a key execution unit for realizing the closed-loop intelligent operation of the system. Under the unified scheduling of the system central controller, it responds to the specific status code generated by the data processing and status diagnosis module and autonomously performs the corresponding maintenance or calibration operations. In this embodiment, when the status code 1 indicating that maintenance is recommended is received, the module automatically starts the status verification process. In this process, the module executes a series of programmed instructions, including: selecting zero-point liquid from multiple connected liquid sources, accurately and quantitatively delivering it to the optical flow cell, and ensuring that the flow path is completely replaced and stabilized, triggering the optical module to perform baseline measurement. When the status code 2 indicating performance drift is received, the module starts a more complex multi-point calibration process. This process sequentially executes the selection, delivery and measurement of the zero-point liquid and one or more standard liquids in a preset order, and submits the complete calibration data set to the analysis model for updating.

[0023] The updated analysis model incorporates a pre-update verification mechanism to ensure the stability and reliability of the analysis model. When automatic calibration is complete, the system does not directly update the model with the new data. Instead, it first uses the old model to predict the newly measured calibration standard solution. The system then compares the predicted concentration with the actual concentration of the standard solution. If the relative deviation exceeds 10%, the calibration is deemed invalid, the update is terminated, and an alarm is issued. The pre-update verification mechanism effectively prevents the model from being corrupted by erroneous data due to on-site issues such as standard solution failure and flow path contamination. Only after the deviation is qualified and the new calibration data is confirmed to be accurate and reliable, will the system use this set of verified data to generate and activate a new analysis model, ensuring the stability and reliability of the analysis results during long-term automatic operation of the instrument.

[0024] In this embodiment, the system is designed to measure chemical oxygen demand (COD). The zero-point solution is deionized water, and the standard solutions are at least two potassium hydrogen phthalate standard solutions of varying concentrations. For example, 50 mg / L and 200 mg / L KHP solutions are used as standard solutions for multi-point calibration to cover a typical wastewater COD concentration range.

[0025] Furthermore, the optical detection and correction module performs spectrum compensation calculations, including: The feature extraction unit obtains the complete scattering spectrum covering the ultraviolet to near-infrared band and processes it into a high-dimensional feature vector; A neural network operation unit based on a pre-trained model receives the high-dimensional feature vector, predicts and outputs a scattering interference spectrum baseline corresponding to the current water sample matrix characteristics through a nonlinear mapping model; The compensation operation unit subtracts the scattering interference spectrum baseline predicted by the neural network operation unit from the original absorption spectrum measured by the optical detection and correction module to achieve dynamic compensation of the spectrum.

[0026] To achieve the aforementioned complete scattering spectrum measurement, spanning the ultraviolet to near-infrared range, the optical detection and correction module uses a highly stable pulsed xenon lamp as the excitation light source. This light source emits a composite beam orthogonally incident on the water sample in the optical flow cell. A separate spectrometer collects the beam at a 90-degree angle, simultaneously acquiring complete scattering spectrum data from 200 to 800 nm. This data is then processed and used as input for the neural network.

[0027] In order to enable those skilled in the art to implement the neural network operation unit, the specific configuration of this embodiment is as follows: a BP neural network containing two hidden layers is used. The number of input layer nodes is the number of wavelength points collected by the scattering spectrum (covering the range of 200-800nm, with an interval of 2nm, a total of 301 nodes), the first hidden layer contains 20 neurons, the second hidden layer contains 10 neurons, and the number of output layer nodes is the same as the number of wavelength points of the ultraviolet-visible absorption spectrum to be corrected. The activation function of all neurons uses the ReLU function. The pre-training process of the model is as follows: collect at least 500 groups of water samples with different turbidity and particulate matter types, and simultaneously measure their scattering spectra (as input data of the model) and the absorption spectrum baseline interference caused by the water samples to pure water (as label data of the model) to form a training data set. The mean square error is used as the loss function, and the network weights are optimized and trained by the gradient descent method.

[0028] By accurately identifying and effectively subtracting the complex background interference caused by suspended particulate matter of varying types and concentrations, the spectral analysis's anti-interference capabilities and measurement accuracy are enhanced. This ensures that measurement results remain stable even under field conditions where water turbidity dynamically changes, making the final calculated pollutant concentration closer to the actual value, thus laying a solid foundation for the entire system to provide reliable data in complex environments.

[0029] Furthermore, the electrochemical pretreatment module has a built-in evaluation system, which includes an electrochemical impedance spectroscopy analysis unit, a transient response capture unit and a comprehensive diagnostic controller. The specific process is as follows: Figure 2 shown.

[0030] An electrochemical impedance spectroscopy analysis unit, which applies a preset DC bias to the electrode between two regular measurement tasks, and superimposes a multi-frequency sinusoidal AC voltage on top of the DC bias, synchronously collects the AC current response, and calculates the electrochemical impedance spectrum. Specifically, the preset DC bias is 0V DC bias relative to the open circuit potential of the electrode, the perturbation amplitude of the sinusoidal AC voltage is 10mV, and the scanning frequency range is from 100kHz to 0.1Hz. By analyzing the changes of the characteristic parameters related to the charge transfer resistance or double-layer capacitance in the impedance spectrum, the attenuation of the electrode activity and effective surface area caused by the coverage of insulating substances is quantitatively evaluated, and a quantitative index is obtained; A transient response capturing unit, which synchronously records the complete current response curve caused by the regeneration pulse when the module performs the periodic electrode online regeneration program, and extracts the peak current, rise time and total charge from the response curve, to obtain characteristic parameters representing the surface chemical kinetic state of the electrode; A comprehensive diagnostic controller, which receives the quantitative index from the electrochemical impedance spectroscopy analysis unit and the characteristic parameters from the transient response capturing unit, compares the multi-dimensional diagnostic input with a preset characteristic space defining different electrode states, judges the comprehensive health status of the electrode and decides whether to perform an enhanced cleaning program.

[0031] The characteristic space defining different electrode states is realized by a set of explicit logical rules. The comprehensive diagnostic controller compares the real-time acquired parameters with the reference values recorded when the device is initially calibrated: State 1 (healthy): the increase of the charge transfer resistance and the attenuation of the total charge of the regeneration pulse are both at a preset lower level, and the change of the preset lower level is less than 20%.

[0032] State 2 (mildly contaminated): the increase of the charge transfer resistance or the attenuation of the total charge of the regeneration pulse exceeds the healthy level and enters a preset medium range, which is 20% to 50%. The controller will generate a state code suggesting maintenance.

[0033] State 3 (severely contaminated): the increase of the charge transfer resistance and the attenuation of the total charge of the regeneration pulse both enter a preset higher level, which is more than 50%. The controller determines that an enhanced cleaning program needs to be performed immediately.

[0034] In this example, the electrochemical treatment module primarily targets nitrite, a common interfering substance in wastewater. The programmed potential is a constant potential method, specifically applying a constant oxidation potential of +0.85 volts (relative to an Ag / AgCl reference electrode) to the working electrode (a glassy carbon electrode) for 60 seconds. This potential is sufficient to selectively oxidize nitrite to nitrate, which has minimal interference with UV spectroscopy measurements, while avoiding widespread oxidation of other major organic compounds in the water.

[0035] By combining two complementary diagnostic techniques, electrochemical impedance spectroscopy and transient response analysis, this invention provides the electrochemical pretreatment module with accurate self-health assessment capabilities. The system can comprehensively quantify the activity decay and surface state of the electrode, thereby predicting any significant performance degradation before it occurs and initiating enhanced maintenance only when necessary. This shift from a passive, periodic to an active, on-demand maintenance model not only ensures the long-term stability of the pretreatment effect and effectively extends the life of the electrode, but also significantly reduces operating and maintenance costs and the risk of unexpected downtime, providing a solid guarantee for the reliable operation of the entire system.

[0036] Furthermore, the analysis model built into the data processing and status diagnosis module includes: a global quantitative analysis unit, which integrates correction algorithms for target pollutants and all known major interferents, directly receives spectral data and synchronously outputs preliminary concentration estimates of each component; a residual analysis unit, which monitors the calculated residual spectrum generated by the global quantitative analysis unit (i.e., the difference between the original measured spectrum and the model reconstructed spectrum) in real time, and diagnoses whether there are unidentified new interferences and system performance drift by analyzing whether the error exceeds a preset threshold (the threshold is determined based on the normal noise level of the instrument in a stable state); once the presence of new interference is diagnosed, the system automatically compares the calculated residual spectrum with the standard spectral database to identify the identity of the interferent; a nonlinear unit, which receives the preliminary concentration estimate output by the global quantitative analysis unit, compensates for the nonlinear effects caused by chemical interactions between components, and calculates and outputs the final pollutant concentration. The specific process is as follows: Figure 3 shown.

[0037] In this embodiment, the multivariate correction algorithm used by the global quantitative analysis unit is partial least squares. Before establishing the partial least squares model, the spectral data is first preprocessed by Savitzky-Golay smoothing and first-order derivative. The optimal number of principal factors for the model is determined by leave-one-out cross-validation, which in this embodiment is determined to be seven.

[0038] The so-called model-reconstructed spectrum refers to the use of the established partial least squares model to project the original spectrum into the latent variable space composed of principal factors, and then inversely calculate the theoretical spectrum based on its score and the model's loading information. The reconstructed spectrum represents the part of the original spectrum that can be explained and described by the current model.

[0039] The function of the nonlinear unit is to compensate for the nonlinear spectral response caused by the chemical interaction between high-concentration coexisting substances and the target pollutant. Specifically, first, the system uses the global quantitative analysis unit to perform a rapid calculation on the original spectrum to obtain preliminary concentration estimates of the target pollutant and the main interferent. Secondly, the nonlinear unit receives the preliminary concentration estimate to compensate for the spectral nonlinear response caused by the chemical interaction between the high-concentration coexisting substances and the target pollutant (such as complexation reaction, acid-base equilibrium shift, etc.). This nonlinear response is essentially a manifestation of the deviation from the linear superposition relationship (Lambert-Beer law) between the total absorbance of the mixture and the concentration of each component. To achieve accurate compensation, the unit uses the preliminary concentration estimate as a key parameter and inputs it into a pre-constructed nonlinear quantitative model together with the original spectral data. In this embodiment, the model is a support vector regression model, and its specific construction and configuration are as follows: Model Configuration: A support vector regression model based on a Gaussian radial basis function kernel was used. Model parameters were optimized using K-fold cross-validation and grid search. The optimal search range for the penalty coefficient C was [0.1, 100], and the optimal search range for the kernel parameter gamma was [0.001, 10].

[0040] Training Sample Set: To train the model, complex calibration samples that reflect chemical interactions are required. For example, when targeting chemical oxygen demand (COD), 100 mixed standard solutions containing the target pollutant and at least one major coexisting interferent were prepared. The sample set's concentration profile was orthogonal to ensure coverage of high, medium, and low concentration levels, effectively learning the nonlinear interactions between components.

[0041] Model Input and Output: The model input is a composite vector consisting of the raw spectral data and the preliminary concentration estimates of each component output by the global quantitative analysis unit. The model output is the final pollutant concentration, which has been compensated for nonlinear effects.

[0042] By employing a multi-stage processing strategy that combines quantitative calculation, residual diagnosis, and nonlinear correction, this method significantly improves the accuracy of analytical results and the intelligence of the system. This method not only achieves more precise concentration values ​​through specialized nonlinear compensation, but more importantly, it utilizes calculated residuals to monitor model status in real time, enabling the detection of unknown interference or system drift. This provides a core basis for instrument status warnings and self-diagnosis, effectively ensuring the reliability of long-term monitoring data.

[0043] Furthermore, the data processing and status diagnosis module is used to confirm the presence of new interference in the calculation residual by using a residual spectrum matching and identification process, including: a residual spectrum acquisition unit, configured to extract the calculated residual spectrum generated by the global quantitative analysis unit; A spectrum matching unit compares the residual spectrum with a preset standard spectrum database containing multiple potential unknown interferents to calculate the spectrum similarity; The interference identification unit confirms that there is an unmodeled interference corresponding to an entry in the database in the current water sample when the spectral similarity exceeds a preset confidence threshold, and outputs identity information as a diagnosis result.

[0044] In this embodiment, the standard spectral database contains more than 20 potential organic pollutants that are common in the intended application field of the present invention (e.g., sewage treatment), such as phenol, aniline, common anionic surfactants, etc. All spectra in the library are collected at a spectral resolution of 1 nm, and are uniformly baseline corrected and normalized. The method used by the spectral matching unit to calculate spectral similarity is the cosine similarity algorithm. The preset confidence threshold in the interference identification unit is set to 0.95 based on experience. When the calculated cosine similarity value exceeds 0.95, the system confirms that there is an unmodeled interference in the current water sample that matches the corresponding entry in the database.

[0045] By matching and identifying the calculated residual spectrum with the standard spectral library, the system's diagnostic capability is upgraded from "anomaly detection" to "identity recognition". This allows it to identify the emergence of new interference and clarify its chemical identity, providing direct evidence for the rapid tracing of pollution incidents and diagnosis of abnormal operating conditions. This also points the way for the subsequent optimization of the analysis model, enabling the system to continuously adapt to new monitoring environments and effectively ensuring the long-term accuracy of the data.

[0046] Furthermore, the evaluation mechanism adopted by the generation logic of the system status code has a bimodal characteristic. The system sets an initial stable verification period after calibration. Any deviation event that exceeds a preset narrow threshold during this period will be immediately identified as a high-level alarm event and directly trigger the corresponding diagnostic procedure; for deviation events that occur after the stable verification period, the system starts a time series accumulator for weighted scoring to continuously track and evaluate the long-term performance drift of the system.

[0047] The system status code is implemented as a layered and correlated structured code to provide accurate root-cause diagnosis. The system status code consists of a main code summarizing the overall status and subcodes pointing to specific fault sources. For example, when the system detects both performance drift and electrode contamination, a correlated diagnosis can be performed and a status code generated: {Main code: Maintenance recommended, Subcode: [Performance drift - increased residual, electrode contamination - increased impedance]}, accompanied by guiding maintenance recommendations such as prioritizing electrode cleaning. This structured coding elevates isolated phenomenon alarms to traceable root-cause diagnoses, making maintenance more targeted and efficient.

[0048] In this embodiment, the stability verification period is set to 24 hours after successful instrument calibration or 100 consecutive successful measurements (whichever comes first). The preset narrow threshold is set such that the root mean square (RMS) residual of each measurement calculated during the stability verification period deviates by no more than 5% from the average of the RMS of the 10 previous measurements during the current verification period. The weighted score is implemented using an exponentially weighted moving average (EWMA) algorithm, with a smoothing coefficient empirically set to 0.1 to track the long-term trend of the residual.

[0049] By adopting a dual-modal evaluation mechanism, the intelligence and efficiency of system status diagnosis are improved. The narrow threshold verification period after calibration ensures that critical initial instabilities can be captured immediately to prevent problems in system operation. In long-term operation, weighted-scoring time series tracking can effectively distinguish between random noise and true performance drift. While avoiding frequent false alarms, it can accurately identify slow system degradation, making alarms and maintenance more targeted and effectively ensuring the long-term stable operation of the instrument.

[0050] Furthermore, upon determining that calibration has failed, the automatic maintenance and calibration module immediately enters a safety lockout mode. In this mode, the system suspends conventional measurements and data output and automatically executes a hardware fault diagnosis subroutine employing sequential inspection logic. This subroutine checks each hardware unit item by item in a predetermined sequence, with the inspection sequence and judgment criteria being as follows: Check the light source intensity: The system records the light source reference intensity after the last successful calibration. The self-test program first measures the current light intensity. If the current light intensity is lower than 80% of the reference light intensity, it is determined that the light source intensity is unqualified.

[0051] Check the cleanliness of the circulation pool: If the light intensity of the light source is normal, the system automatically pumps in the zero-point liquid (such as pure water) and measures its absorbance in the non-absorption area (such as the 500-600nm band). If the average value of the absorbance is greater than 0.01AU, the cleanliness of the circulation pool is judged to be unqualified.

[0052] Check fluid pressure: If the first two items are normal, the system checks the flow path pressure through the built-in pressure sensor. If the pressure reading is lower than 0.5 bar or higher than the normal working range of 2.0 bar, the fluid pressure is judged to be unqualified.

[0053] The subroutine will terminate after detecting the first unqualified hardware unit and generate a guiding maintenance suggestion including specific fault unit information (such as unqualified light intensity of the light source).

[0054] By initiating this safety lock and sequential diagnostic program after a calibration failure, an intelligent emergency response capability is provided. The safety lock mode can immediately terminate erroneous measurements and prevent the spread of unreliable data. The efficient sequential self-test logic can automatically and quickly locate the root hardware failure point and generate specific maintenance recommendations, shortening the time for on-site troubleshooting, reducing the difficulty of maintenance, and improving the maintenance efficiency and overall availability of the instrument.

[0055] Furthermore, the system also includes a data logging mechanism that uniformly formats routine measurement results, hardware status snapshots, and key operational events into log entries with a data block structure. Each data block contains a cryptographic hash pointer generated through a specific operation. The cryptographic hash pointer is generated using the SHA-256 (Secure Hash Algorithm 256-bit) hash algorithm. The input to the operation is concatenated data, which includes: the cryptographic hash pointer of the previous data block and a serialized data stream of all information in the current data block. In this embodiment, the information in the data blocks is uniformly serialized using the JSON format to form a standard text data stream. This mechanism can uniformly record routine measurement results, hardware status snapshots, and key operational events including system maintenance, calibration, status alarms, and other information in a single data chain in timestamp order. This single hash chain ensures the overall immutability and traceability of all types of historical data.

[0056] By uniformly recording all measurement results, system status, and operational events in a data chain connected by cryptographic hash pointers, this invention establishes a mechanism to ensure the integrity and traceability of historical data. The technical characteristic of this structure is that any modification to historical data will cause subsequent hash verification to fail, thus making the data verifiable and tamper-proof. This method of uniformly recording various types of information and ensuring its originality provides a technical foundation for regulatory and legal applications requiring high-level data authentication.

[0057] By constructing a closed-loop operation system integrating multi-dimensional perception, intelligent diagnosis, automatic maintenance and safety record, the application systematically solves the problem that the accuracy and consistency of online monitoring technology are difficult to guarantee in complex field environment. The system, in combination with chemical pretreatment and advanced spectral correction technology at the measurement front end, effectively deals with the dynamic interference of water matrix, ensuring the accuracy of single measurement; in long-term operation, it can actively diagnose the drift of its own performance and the state of key components, and automatically perform maintenance and calibration, ensuring the high consistency of data, all processes and results being recorded in the tamper-proof data chain, establishing a complete data traceability system, and providing users with full-cycle and high-credibility data services.

[0058] Embodiment two:

[0059] The embodiments of the application aim to illustrate the application of the system in a specific industrial wastewater monitoring scenario. Taking the online monitoring of total phenol concentration in B factory effluent as an example, the composition of the B factory effluent is relatively complex, containing high concentrations of reducing interferents (such as sulfides) and other organic matter in addition to the target pollutant phenolic substances, and the pH value of the water sample fluctuates greatly, posing a challenge to accurate monitoring.

[0060] To accurately eliminate the influence of the common strong reducing interferent sulfide in wastewater on subsequent ultraviolet spectral measurement, the program potential of the electrochemical treatment module is specially optimized. In this embodiment, after the water sample to be measured flows into the module, the system applies a constant oxidation potential of +0.50 volts (relative to the Ag / AgCl reference electrode) on the working electrode and lasts for 90 seconds. The selection of this potential value is carefully considered. It is sufficient to selectively and quickly oxidize sulfides into sulfate ions that do not interfere with spectral measurement, while due to the relatively mild potential, it can maximize the avoidance of electrochemical degradation of the target pollutant phenolic substances, thereby achieving accurate removal of the main inorganic interferents and ensuring the accuracy of subsequent spectral analysis.

[0061] The analysis model in the data processing and state diagnosis module is also deeply customized. The partial least squares model used by the global quantitative analysis unit is specially trained using mixed standard solutions containing different gradient concentrations of total phenol and the main coexisting organic interferents (such as pyridine, quinoline, thiocyanate, etc.) known in the factory, to ensure the applicability of the model to this specific water matrix. In addition, considering that the ultraviolet absorption spectrum of phenolic substances is significantly affected by pH value, the nonlinear unit integrates real-time readings of an external pH sensor. After receiving the preliminary total phenol concentration estimate, the unit will dynamically compensate and correct the preliminary concentration according to the synchronously acquired pH value, based on the pre-established pH-correction curve established through multi-point experimental calibration.

[0062] The curve is established as follows: In the laboratory, a total phenol standard solution is used to perform measurements at multiple key pH points (at pH 7.5, 8.0, 8.5, 9.0, and 9.5). The concentration correction factor required for each pH point is calculated, and these pH-correction factor pairs are stored in the system memory, forming a calibration curve. During actual field measurements, the system searches this stored calibration curve based on the real-time pH readings. If the measured pH value falls exactly between two calibration points, the system calculates the precise correction factor corresponding to the current pH value through an interpolation algorithm, applies it to the initially calculated concentration value, and ultimately outputs the accurate pH-compensated concentration. Table 1 shows an example of pH-correction factor Table 1 for this embodiment.

[0063]

[0064] The system's self-diagnosis and maintenance features are also designed with application-specific features in mind. In the residual spectrum matching process used to identify new interferences, the spectral library used for comparison has been replaced with a library of characteristic pollutants from the coking industry. This library contains pre-populated spectra of over 30 chemical species that may be discharged into water bodies during production process anomalies, including various types of polycyclic aromatic hydrocarbons (PAHs) (such as naphthalene and anthracene) and benzene series. Furthermore, the standard solutions used in the automatic maintenance and calibration module are specified as phenol standard solutions at concentrations of 10 mg / L and 100 mg / L, with secondary purified deionized water used as the zero-point solution. When the system determines that calibration is necessary based on a status code, these solutions are automatically used to verify and update the analysis model, ensuring the instrument's long-term stability and reliability in complex industrial environments.

[0065] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A multi-dimensional water pollution analysis system, characterized in that: include: Optical detection and correction module: Synchronously measures the UV-visible absorption spectrum of the water sample to be tested and the scattered light intensity in the orthogonal direction; Using the scattered light intensity data, a correction algorithm is used to deduct the spectral baseline interference caused by suspended particles in the water sample in real time; Electrochemical treatment module: By applying a programmed potential optimized for known interferents to the incoming water sample, it selectively transforms and / or degrades the interferents in the water sample, reducing the background complexity of the spectral analysis; periodically applying reverse pulses to achieve online electrode regeneration; Data processing and status diagnosis module: A built-in analysis model uses a multivariate correction algorithm to analyze spectral data and calculate the concentration of target pollutants. By analyzing the calculation residuals of the analysis model, it diagnoses new interferences in the current measurement and system performance drift, and generates corresponding system status codes. Automatic maintenance and calibration module: responds to the system status code, performs status verification and multi-point calibration on the instrument, and uses the calibration results to update the analysis model.

2. A multi-dimensional water pollution analysis system according to claim 1, characterized in that: The optical detection and correction module performs spectrum compensation calculations, including: a feature extraction unit obtains a complete scattering spectrum covering the ultraviolet to near-infrared band ， The optical detection and correction module processes the absorption spectrum into a high-dimensional feature vector; a neural network operation unit based on a pre-trained model receives the high-dimensional feature vector, predicts and outputs a scattering interference spectrum baseline corresponding to the current water sample matrix characteristics through a nonlinear mapping model; a compensation operation unit subtracts the scattering interference spectrum baseline predicted by the neural network operation unit from the original absorption spectrum measured by the optical detection and correction module to achieve dynamic compensation of the spectrum.

3. A multi-dimensional water pollution analysis system according to claim 1, characterized in that: The electrochemical treatment module has a built-in evaluation system, which includes an electrochemical impedance spectroscopy analysis unit, a transient response capture unit and a comprehensive diagnostic controller; An electrochemical impedance spectroscopy analysis unit applies a preset DC bias to the electrode between two routine measurement tasks, superimposes a multi-frequency sinusoidal AC voltage on the DC bias, synchronously collects the AC current response, and calculates the electrochemical impedance spectrum. By analyzing changes in characteristic parameters related to charge transfer resistance or double-layer capacitance in the impedance spectrum, the attenuation of electrode activity and effective surface area caused by insulating material coverage is quantitatively evaluated to obtain quantitative indicators; The transient response capture unit synchronously records the complete current response curve caused by the regeneration pulse when the module executes the periodic electrode online regeneration program, and extracts the peak current, rise time and total charge from the response curve to obtain characteristic parameters representing the surface chemical kinetic state of the electrode; The integrated diagnostic controller receives quantitative indicators from the electrochemical impedance spectroscopy analysis unit and characteristic parameters from the transient response capture unit, compares the multi-dimensional diagnostic input with the preset feature space that defines different electrode states, judges the overall health status of the electrode and decides whether an enhanced cleaning procedure is needed.

4. A multi-dimensional water pollution analysis system according to claim 1, characterized in that: The analysis model built into the data processing and status diagnosis module includes: a global quantitative analysis unit, which integrates correction algorithms for target pollutants and all known major interferents, directly receives spectral data and synchronously outputs preliminary concentration estimates for each component; a residual analysis unit, which monitors the calculation residuals generated by the global quantitative analysis unit in real time, and diagnoses the presence of unidentified new interferences and system performance drift by analyzing whether they deviate from the normal random noise baseline; and a nonlinear unit, which receives the preliminary concentration estimates output by the global quantitative analysis unit, compensates for the nonlinear effects caused by chemical interactions between components, and calculates and outputs the final pollutant concentration.

5. A multi-dimensional water pollution analysis system according to claim 4, characterized in that: The data processing and status diagnosis module is used to calculate the presence of new interference in the residual using a residual spectrum matching and identification process, including: a residual spectrum acquisition unit, used to extract the calculated residual spectrum generated by the global quantitative analysis unit; a spectrum matching unit, which performs feature comparison on the residual spectrum with a preset standard spectrum database containing multiple potential unknown interferents and calculates the spectral similarity; and an interference identification unit, which confirms the presence of unmodeled interference corresponding to an entry in the database in the current water sample when the spectral similarity exceeds a preset confidence threshold, and outputs identity information as a diagnosis result.

6. The multi-dimensional water pollution analysis system according to claim 1, characterized in that: The evaluation mechanism adopted by the system status code generation logic has a bimodal characteristic: any deviation event that exceeds a preset narrow threshold during the system's stability verification period will be immediately identified as a high-level alarm event and directly trigger the corresponding diagnostic procedure; for deviation events that occur after the stability verification period, the system starts a time series accumulator for weighted scoring to continuously track and evaluate the long-term performance drift of the system.

7. The multi-dimensional water pollution analysis system according to claim 1, characterized in that: After the automatic maintenance and calibration module determines that the calibration has failed, the system immediately enters a safety lock mode. In the safety lock mode, the system suspends routine measurements and data output, and automatically executes a hardware fault diagnosis subroutine using sequential inspection logic; the subroutine checks the hardware units one by one in a preset order, and the inspection order is: check the light intensity of the light source, if the light intensity of the light source is normal, check the cleanliness of the circulation pool, if the cleanliness of the circulation pool is normal, check the fluid pressure; the subroutine will terminate after detecting the first unqualified hardware unit, and generate a guiding maintenance suggestion containing specific fault unit information.

8. The multi-dimensional water pollution analysis system according to claim 1, characterized in that: It also includes a data recording mechanism, which uniformly formats routine measurement results, hardware status snapshots and key operation events into log entries, the data block structure of the log entries, the generation of encrypted hash pointers, and the operation input as spliced ​​data, which includes: the encrypted hash pointer of the previous data block and the serialized data stream of all information in the current data block; the information in the data block is uniformly formatted and can be recorded in a single data chain in timestamp order, and the routine measurement results, hardware status snapshots and key operation events including system maintenance, calibration and status alarm information are uniformly recorded in a single data chain, and the overall immutability and traceability of all types of historical data are guaranteed through a single hash chain.

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