A system for monitoring heavy metal content in electroplating wastewater

Abstract

The present invention discloses a system for monitoring heavy metal content in electroplating wastewater, which specifically relates to the field of electroplating wastewater monitoring, and is used to solve the problem of the influence of bubble interference on the monitoring accuracy of heavy metal content. It is aimed at the dynamic characteristics of bubble jump interference, and through identification and threshold screening, the distribution and recovery characteristics of the interference signal are extracted to ensure the accuracy and adaptability of the correction threshold. On this basis, multi-level adaptive filtering is applied to perform point-by-point noise removal and filtering on the monitoring data to achieve effective isolation of the jump interference and obtain a continuous and true heavy metal concentration curve; the pure data after noise removal is combined with historical data for traceability analysis, and the jump compensation coefficient is generated and transmitted back to the identification link to form an adaptive closed loop, and the subsequent interference correction effect is optimized; thus, it has high anti-interference and long-term correction stability in a complex fluid environment, ensuring the real-time accuracy and consistency of heavy metal monitoring, and effectively solving the problem of the hidden influence of bubble interference on the online monitoring accuracy.

Description

Technical Field

[0001] The present invention relates to the field of electroplating wastewater monitoring, and more specifically, to a system for monitoring heavy metal content in electroplating wastewater. Background Art

[0002] In the electroplating process, a large amount of metal coating is formed on the substrate through electrochemical reactions. This process not only involves the reduction and deposition of metal ions, but is also accompanied by a variety of complex chemical reactions and side reactions. In the use of electroplating solution, in order to control the reaction rate and improve the quality of electroplating, it is necessary to add a variety of auxiliary chemicals such as complexing agents and brighteners. The presence of these substances and the passage of current during the electrolysis process often produce a large number of bubbles in electroplating wastewater. These bubbles form a two-phase fluid in the flow of wastewater, and there are a large number of unstable tiny bubbles, especially in the bends of the pipeline, near the valves and the monitoring sensor area. These bubbles have a certain influence on the overall flow characteristics and chemical composition distribution of the liquid during the formation and disintegration process.

[0003] In the online monitoring of electroplating wastewater, heavy metal content monitoring is one of the most critical technologies. Since the concentration of metal ions in wastewater directly affects whether the emission standards are met, accurate online monitoring is essential. However, due to its complex composition, electroplating wastewater is often accompanied by the generation and collapse of a large number of tiny bubbles. These bubbles will attach to the sensor surface or quickly pass through the monitoring area, thereby causing a transient "jump" effect in the detection signal, which is manifested as a sudden increase or decrease in the measured value. This "jump" effect has no direct correlation with the change in the actual heavy metal ion concentration and is an interference caused by the behavior of bubbles in the fluid. The impact of this phenomenon on the monitoring data is difficult to completely eliminate using traditional signal filtering methods, especially when bubbles are frequently generated or irregularly attached to the sensor surface, resulting in irregular spike fluctuations in the monitoring signal. This situation is often mistaken for sensor failure or data noise in actual processes, but in fact its root cause lies in the dynamic disturbance of the monitoring environment caused by the generation and collapse of bubbles. Solving this problem is of great significance for improving the accuracy of online heavy metal monitoring, reducing false alarms, and ensuring the compliance of wastewater treatment.

[0004] In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, the embodiment of the present invention provides a system for monitoring the heavy metal content in electroplating wastewater. By establishing a set of systematic bubble interference identification, correction and closed-loop compensation mechanisms, high-precision monitoring of the heavy metal content in electroplating wastewater is achieved, and the purity and stability of the monitoring data under complex working conditions are significantly improved. The system first identifies and screens the dynamic characteristics of bubble jump interference, extracts the distribution and recovery characteristics of the interference signal from the multidimensional features, and ensures the adaptability and accuracy of the correction threshold; on this basis, the monitoring data is denoised and precisely filtered point by point through a multi-level adaptive filtering structure, effectively isolates the jump interference and retains the key signal, and obtains a continuous and true heavy metal concentration curve. The pure data after denoising is combined with historical data for traceability analysis, generates a jump compensation coefficient and returns it to the identification mechanism to form an adaptive closed loop, thereby dynamically optimizing the subsequent interference correction effect. The overall design enables the system to have a high degree of anti-interference ability and long-term correction stability for bubble interference in a complex fluid environment, ensures the real-time accuracy and long-term consistency of heavy metal monitoring, and solves the problem of the hidden influence of bubble interference on the accuracy of online monitoring, so as to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A heavy metal content monitoring system for electroplating wastewater, comprising: a turbulence deconstruction module, a disturbance control criterion module, a source clarification and noise removal module and a net tracing balance module;

[0008] Turbulence deconstruction module: obtains electroplating wastewater monitoring signals, identifies bubble jump signal characteristics through turbulence signal feature extraction algorithm, generates bubble jump curve parameters; passes bubble jump curve parameters to disturbance control criterion module;

[0009] Disturbance control criterion module: According to the bubble jump curve parameters, the trend analysis model is used to preliminarily calculate the adaptive threshold parameters of bubble interference, and the optimal threshold is selected by combining the comprehensive decision value of the jump distribution characteristic index and the signal recovery stability index to realize the dynamic correction of the jump signal; the threshold parameters obtained by the screening are passed to the Chengyuan noise removal module;

[0010] Chengyuan De-noising Module: Based on the adaptive threshold parameters, the multi-level filtering structure is used to compare the real-time monitoring data point by point, remove the data segments affected by the sudden bubble jump, and reconstruct the heavy metal concentration curve; the de-noised data is passed to the Jingsu Balance Module;

[0011] Net tracing balance module: Integrate and trace the noise-removed monitoring data with historical data, generate a jump compensation coefficient and feed it back to the identification module to optimize the correction accuracy; the generated jump compensation coefficient is fed back to the turbulence deconstruction module to optimize the recognition accuracy of bubble jump characteristics in the next turbulence deconstruction, forming a closed-loop adaptive correction process.

[0012] In a preferred embodiment, the operation process of the turbulence deconstruction module includes the following:

[0013] S1.1, after obtaining the electroplating wastewater monitoring signal, firstly conduct real-time dynamic feature analysis on the original data stream of the sensor; adopt the local variation analysis method of the signal, calculate the local fluctuation amplitude and transient mutation rate of the signal through a dynamic sliding window; local fluctuation amplitude: calculate the variance and mean of the signal in the sliding window to reflect the fluctuation intensity of the signal in unit time; transient mutation rate: calculate the relative mutation rate between the current signal point and the previous signal point;

[0014] S1.2, a turbulence factor is constructed using the turbulence signal model to quantify the degree of turbulence in the signal; turbulence factor: calculated by the structural entropy and local peak distribution of the signal, describing the complexity of the signal under turbulent conditions;

[0015] S1.3, combining the local fluctuation amplitude, transient mutation rate and turbulence factor, the bubble jump curve parameter set is constructed through nonlinear combination;

[0016] S1.4, after obtaining the bubble jump curve parameters, use the classification-based machine learning model to perform real-time pattern recognition on the bubble jump signal and heavy metal concentration signal; the input of the machine learning model is the bubble jump curve parameters, and the model classifies the input features in real time; the output is the recognition result, that is, whether the marked signal segment belongs to a bubble jump.

[0017] In a preferred embodiment, the operation process of the disturbance control criterion module includes the following contents:

[0018] S2.1, first receive the bubble jump curve parameters output by the turbulence deconstruction module The time series trend analysis model is used to calculate the initial threshold of the bubble jump curve parameters; the time series trend analysis model is based on the slope and curvature changes of the trend line in the sliding window to preliminarily estimate the adaptive threshold of bubble interference. .

[0019] In a preferred embodiment, the operation process of the disturbance control criterion module further includes the following contents:

[0020] S2.2, after the preliminary threshold is calculated, the sudden jump distribution characteristic index and the signal recovery stability index are calculated to generate the sudden jump disturbance index, and the preliminary threshold is evaluated and screened;

[0021] The sudden jump distribution characteristic index is mainly used to evaluate the concentration and dispersion of bubble sudden jump signals in the time dimension; the calculation formula of the sudden jump distribution characteristic index is: ;in, is the mean value of the bubble jump curve parameter, is the center position of the window, is an exponential modulation parameter used to adjust the decay speed;

[0022] The signal recovery stability index is used to evaluate the speed and amplitude of the signal returning to a stable state after a bubble jump. The calculation formula of the signal recovery stability index is: ;in, is an index adjustment parameter used to enhance the sensitivity of the signal recovery stability index to the signal recovery characteristics.

[0023] In a preferred embodiment, the operation process of the disturbance control criterion module further includes the following contents:

[0024] S2.3, calculation of the sudden jump disturbance index: the sudden jump distribution characteristic index and the signal recovery stability index are integrated by combining polynomials and fractions to generate a sudden jump disturbance index reflecting the sudden jump characteristics of the bubble;

[0025] S2.4, compare the size of the sudden disturbance index with the preset system threshold. If the sudden disturbance index is greater than the system threshold, confirm that the current preliminary threshold is suitable as the final adaptive threshold; if the sudden disturbance index is lower than the system threshold, adjust the preliminary threshold according to the difference to obtain a more adaptable interference correction parameter.

[0026] In a preferred embodiment, the operation process of the source noise removal module includes the following:

[0027] S3.1, first compare the monitoring data point by point, and apply the interference threshold parameter to identify the sudden jump effect contained in the signal;

[0028] S3.2, after completing the dynamic weight elimination, the monitoring signal is subjected to multi-stage filtering processing based on the adaptive filtering structure;

[0029] S3.3, after ensuring that the sudden jump interference is eliminated, the high-frequency spike filter signal after multi-stage filtering is reconstructed to fill the signal gap or deviation; based on the signal interpolation and reconstruction method, interpolation and reconstruction are performed on the data segment after the sudden jump interference is eliminated to obtain denoised data.

[0030] In a preferred embodiment, the operation process of the net balancing module includes the following:

[0031] S4.1, integrate the denoised data with the historical monitoring data to construct a long-term series data set; identify potential trend fluctuation patterns by analyzing the trend changes of the reconstructed data and historical data layer by layer, and judge whether the current data remains stable within the long-term statistical trend; if the current monitoring data and the historical data show consistency in trend characteristics, the reconstructed data is considered to have long-term stability; if the data fluctuation deviates from the historical trend, it is marked as a potential abnormal fluctuation and recorded;

[0032] S4.2, based on the trend stability analysis, by constructing a traceability analysis model, evaluate whether there are sudden jump interferences that are not fully identified or corrected in the denoised data; by extracting the key differences between the current data and the historical data, a data traceability map is formed to intuitively display the characteristic fluctuations and impact range of the interference sudden jumps;

[0033] S4.3, based on the results of the traceability analysis, comprehensively considers the degree of deviation between the denoised data and the historical trend data, the long-term fluctuation stability and the continuous influence of the sudden jump interference, and generates the sudden jump compensation coefficient; it reflects the long-term effect of the bubble sudden jump interference during the monitoring process, and aims to correct the potential sudden jump feature recognition bias in the turbulence deconstruction module;

[0034] S4.4 feeds back the generated jump compensation coefficient to the turbulence deconstruction module, and closes the loop to correct the jump feature recognition and dynamic threshold calculation of the turbulence deconstruction module.

[0035] The technical effects and advantages of the heavy metal content monitoring system for electroplating wastewater of the present invention are as follows:

[0036] The present invention realizes high-precision monitoring of heavy metal content in electroplating wastewater by establishing a set of systematic bubble interference identification, correction and closed-loop compensation mechanisms, and significantly improves the purity and stability of monitoring data under complex working conditions. The system first identifies and thresholds the dynamic characteristics of bubble jump interference, extracts the distribution and recovery characteristics of interference signals from multidimensional features, and ensures the adaptability and accuracy of the correction threshold; on this basis, the monitoring data is denoised and precisely filtered point by point through a multi-level adaptive filtering structure, effectively isolating the jump interference and retaining the key signal, and obtaining a continuous and true heavy metal concentration curve. The pure data after denoising is combined with historical data for traceability analysis, and the jump compensation coefficient is generated and transmitted back to the identification mechanism to form an adaptive closed loop, thereby dynamically optimizing the subsequent interference correction effect. The system has a high degree of anti-interference ability and long-term correction stability for bubble interference in a complex fluid environment, ensuring the real-time accuracy and long-term consistency of heavy metal monitoring, and solving the problem of the hidden influence of bubble interference on the accuracy of online monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1The present invention is a schematic structural diagram of a system for monitoring heavy metal content in electroplating wastewater.

[0038] Figure 2 The present invention is a schematic diagram of the steps of a source clarification and noise removal module of a system for monitoring heavy metal content in electroplating wastewater. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0040] Embodiment 1: Figure 1 The invention provides a system for monitoring heavy metal content in electroplating wastewater, comprising a turbulence deconstruction module, a disturbance control criterion module, a source clarification and noise removal module and a net tracing balance module.

[0041] Turbulence deconstruction module: obtains the electroplating wastewater monitoring signal, identifies the bubble jump signal characteristics through the turbulence signal feature extraction algorithm, generates the bubble jump curve parameters; and passes the bubble jump curve parameters to the disturbance control criterion module.

[0042] Disturbance control criterion module: According to the bubble jump curve parameters, the trend analysis model is used to preliminarily calculate the adaptive threshold parameters of bubble interference, and the optimal threshold is screened in combination with the comprehensive decision value of the jump distribution characteristic index and the signal recovery stability index to realize the dynamic correction of the jump signal; the threshold parameters obtained by screening are passed to the Chengyuan denoising module.

[0043] Chengyuan denoising module: Based on the adaptive threshold parameters, a multi-level filtering structure is used to compare the real-time monitoring data point by point, eliminate the data segments affected by bubble jumps, and reconstruct the heavy metal concentration curve; the denoised data is passed to the Jingsu balance module.

[0044] Net tracing balance module: Integrate and trace the noise-removed monitoring data with historical data, generate a jump compensation coefficient and feed it back to the identification module to optimize the correction accuracy; the generated jump compensation coefficient is fed back to the turbulence deconstruction module to optimize the recognition accuracy of bubble jump characteristics in the next turbulence deconstruction, forming a closed-loop adaptive correction process.

[0045] During the monitoring of electroplating wastewater, bubbles are continuously generated and disintegrated in the pipeline due to electrochemical reactions and wastewater flow characteristics, resulting in frequent fluctuations and sudden jump effects in the monitoring signal. In order to ensure the accuracy of heavy metal concentration monitoring, it is necessary to effectively identify and eliminate the bubble interference characteristics in the monitoring signal. The turbulence deconstruction module constructs exclusive parameters for bubble sudden jump signals through turbulence signal feature extraction and dynamic analysis, so as to achieve effective identification and preliminary classification of sudden jump signals.

[0046] The operation process of the turbulence deconstruction module includes the following:

[0047] S1.1, after obtaining the electroplating wastewater monitoring signal, firstly conduct real-time dynamic feature analysis on the original data stream of the sensor. Considering the transient characteristics of the bubble jump signal, the local variation analysis method of the signal is adopted to calculate the local fluctuation amplitude of the signal through a dynamic sliding window. and transient mutation rate .

[0048] Local fluctuation amplitude: It is calculated by the variance and mean of the signal within the sliding window to reflect the fluctuation intensity of the signal per unit time.

[0049] ;in, is the first The signal value at each position, is the signal mean in the window, is the window length.

[0050] Transient mutation rate: Calculates the relative mutation rate between the current signal point and the previous signal point.

[0051] ;in, is the current signal value, is the signal value at the previous moment, To prevent division by zero.

[0052] Through the joint analysis of fluctuation amplitude and mutation rate, the high fluctuation segments in the signal can be preliminarily screened out. These segments are regarded as the initial basis for judging bubble interference signals.

[0053] S1.2, based on the physical characteristics of bubble jump in wastewater pipes, the turbulence factor was constructed using the turbulence signal model , which is used to quantify the degree of turbulence in the signal. The turbulence factor relates the non-stationarity of the signal to the local structural features, so as to further distinguish between bubble jumps and heavy metal concentration fluctuations.

[0054] Turbulence factor: Calculated through the structural entropy and local peak distribution of the signal, describing the complexity of the signal under turbulent conditions.

[0055] ;in, It represents the structural entropy of the signal within a specific window and is defined as the probability distribution entropy of the signal fluctuation. The local peak density within the window is the ratio of the number of peak points within the window to the window length.

[0056] This turbulence factor can effectively describe the chaotic characteristics of bubble jumps through the complexity and peak distribution of the signal, providing parameter support for subsequent classification.

[0057] S1.3, combining the local fluctuation amplitude, transient mutation rate and turbulence factor, a bubble jump curve parameter set is constructed through nonlinear combination to characterize the dynamic characteristics of the bubble jump signal.

[0058] Bubble jump curve parameters : The bubble jump curve parameters ensure that the bubble jump curve parameters can capture the high-frequency, low-amplitude and turbulence characteristics in the signal jump through a nonlinear combination of the local fluctuation amplitude, transient mutation rate and turbulence factor. This combination avoids the simple weighted summation form and obtains a more stable and highly recognizable jump feature through geometric mean and nonlinear ratio adjustment.

[0059] S1.4, after obtaining the bubble jump curve parameters, use a classification-based machine learning model (such as a support vector machine or a decision tree) to perform real-time pattern recognition on the bubble jump signal and the heavy metal concentration signal. The bubble jump curve parameters are learned and classified by the training model to automatically distinguish the characteristic differences between the bubble jump signal and the heavy metal concentration signal.

[0060] The input of the machine learning model is the bubble jump curve parameters, and the model classifies the input features in real time.

[0061] The output is the recognition result, that is, whether the marked signal segment belongs to a bubble jump.

[0062] Machine learning pattern recognition can help the turbulence deconstruction module to intelligently classify the jump signals, ensuring efficient separation of bubble interference signals and actual monitoring signals.

[0063] The turbulence deconstruction module realizes the accurate identification and characterization of bubble interference in the electroplating wastewater monitoring signal through precise turbulence signal analysis and multi-level parameter extraction. Based on the comprehensive analysis of local fluctuation amplitude, transient mutation rate and turbulence factor, this module constructs a unique bubble jump curve parameter. This parameter captures the high-frequency fluctuation and non-steady-state characteristics of the bubble jump signal through nonlinear combination calculation, ensuring its high resolution and stability. In addition, combined with the real-time pattern recognition method of machine learning, the module can intelligently distinguish between bubble interference signals and heavy metal concentration signals, avoid error accumulation, and lay a data foundation for subsequent threshold correction. The design of the turbulence deconstruction module breaks the simple filtering processing of the jump signal by traditional methods. Through refined dynamic modeling and pattern classification, it provides an accurate and efficient means of bubble interference identification to ensure the credibility and stability of monitoring data.

[0064] In electroplating wastewater monitoring, the bubble interference signal generated by the turbulence effect has a jump characteristic, which directly affects the accuracy of heavy metal concentration monitoring. The bubble jump curve parameters generated by the turbulence deconstruction module provide the dynamic characteristic data of this interference signal, but to further eliminate the interference, it is necessary to conduct in-depth analysis and optimization screening of the adaptive threshold of the interference in the disturbance control criterion module to ensure that the threshold has high adaptability and accuracy in different monitoring environments. To this end, the disturbance control criterion module generates the optimal threshold through trend analysis and exponential decision-making methods to achieve dynamic correction of the bubble jump signal.

[0065] The operation process of the disturbance control criterion module includes the following:

[0066] In the electroplating wastewater monitoring system, the signal jump effect caused by bubble generation and collapse will significantly affect the accurate measurement of heavy metal concentration. The disturbance control criterion module aims to dynamically calculate and screen the optimal adaptive threshold parameters through advanced data analysis methods to effectively correct the jump signal and improve the accuracy and reliability of the monitoring system. This module relies on the bubble jump curve parameters generated by the turbulence deconstruction module, and realizes intelligent control and correction of bubble interference through multi-dimensional feature evaluation and complex decision-making mechanism.

[0067] S2.1, first receive the bubble jump curve parameters output by the turbulence deconstruction module The time series trend analysis model is used to calculate the initial threshold of the bubble jump curve parameters. The time series trend analysis model preliminarily estimates the adaptive threshold of bubble interference based on the slope and curvature changes of the trend line in the sliding window. .

[0068] Trendline slope : ;in, For the A point in time, is the sliding window size.

[0069] Curvature Change : ; Based on the slope and curvature , the initial threshold It can be calculated by the following formula: The preliminary threshold captures the dynamic trend characteristics of the bubble jump signal by combining the nonlinear combination of slope and curvature, providing a preliminary threshold estimate based on the signal change rate and complexity.

[0070] S2.2, after the preliminary threshold calculation is completed, it is necessary to further evaluate the applicability of the threshold. By calculating the sudden jump distribution characteristic index and the signal recovery stability index to generate the sudden jump disturbance index, the preliminary threshold is evaluated and screened.

[0071] The sudden jump distribution characteristic index and signal recovery stability index play a key role in the decision-making construction of bubble interference identification. Their applicability is based on their profound reflection of the sudden jump signal characteristics and accurate screening of the noise impact, which can provide the necessary criteria for threshold screening:

[0072] Applicability of the jump distribution characteristic index: The jump distribution characteristic index evaluates the frequency and concentration trend of the jump signal within the time window through Gaussian weighted time distribution measurement. Bubble jumps are random and intermittent phenomena, often showing the characteristics of short-term violent fluctuations. The jump distribution characteristic index can effectively identify the time concentration and discreteness of the jump signal due to its sensitivity in describing the signal distribution law, ensuring that the significance of the jump phenomenon in the signal data is captured, while eliminating non-jump factors in the continuous fluctuation signal. This characteristic makes the jump distribution characteristic index a reliable indicator for judging the impact strength of the jump signal, and can provide a direct basis for whether to set a certain threshold as the interference threshold.

[0073] Applicability of the signal recovery stability index: The signal recovery stability index focuses on evaluating the speed and stability of the monitoring signal returning to a steady state after the bubble jump interference subsides. As an instantaneous interference, the impact of the jump signal on the monitoring signal is often short-lived. However, after the interference subsides, the recovery of the signal can reflect the intensity of the interference and its impact on the monitoring system. The signal recovery stability index measures the smoothness and continuity of signal recovery, and clarifies whether the system can quickly return to a steady state after the interference occurs, thereby ensuring the sustainability and reliability of the monitoring data. This index reflects the recovery characteristics of the signal to determine whether a certain threshold can effectively adapt to a variety of jump signals, and then screen out the ideal threshold that can accurately reflect bubble interference.

[0074] In summary, the jump distribution characteristic index provides the time distribution information of bubble jumps, helps to distinguish the concentration of jump signals, and screens out real short-term interference; while the signal recovery stability index provides the reliability basis for the rapid recovery of signals after interference, ensuring that the threshold selection adapts to the fluctuation of various interference intensities. The two complement each other, and the decision value constructed by nonlinear combination can provide full support for interference threshold screening in terms of accuracy and adaptability, ensuring the scientificity and efficiency of jump signal correction.

[0075] The jump distribution characteristic index is mainly used to evaluate the concentration and dispersion of bubble jump signals in the time dimension. Since bubble jump signals have obvious randomness and discontinuity in the frequency and distribution of occurrence, they are unevenly distributed in different time windows. The jump distribution characteristic index can be used to measure the distribution characteristics of bubble jump signals in the sliding window, and then identify whether the concentrated jump phenomenon is suitable for setting as the basis for the interference threshold. A high distribution concentration means that the jump signal is relatively dense in the time window, indicating that the interference effect is strong, and it is therefore suitable to adjust the threshold parameters.

[0076] The calculation formula of the sudden jump distribution characteristic index is: ;in, is the mean value of the bubble jump curve parameter, is the center position of the window, is an exponential modulation parameter used to adjust the decay speed. Represents the index of the data point, i.e. the Points, The range is from 1 to , Representative The index measures the concentration of the burst distribution by Gaussian weighting, effectively quantifying the distribution characteristics of the bubble burst signal in the window.

[0077] The signal recovery stability index is used to evaluate the speed and amplitude of the signal returning to a stable state after a bubble jump. The appearance of a bubble jump is usually accompanied by a sudden change in the monitoring signal, and after the jump subsides, the signal gradually returns to stability. The speed and amplitude of signal recovery can effectively reflect the degree of transient impact of the jump on the monitoring system. If the signal recovery speed is slow and the fluctuation is unstable, it means that the jump has a strong interference on the monitoring signal and is suitable as the basis for the adaptive threshold. The signal recovery stability index captures the signal recovery characteristics while avoiding over-adjusting the threshold by calculating the inverse distance weighted value of the signal recovery amplitude after the jump.

[0078] The calculation formula of signal recovery stability index is: ;in, is an index adjustment parameter used to enhance the sensitivity of the index to the signal recovery characteristics. Representative The signal strength of the data point, that is, The signal value observed at a certain moment or position. Through the inverse distance weighting method, the recovery amplitude of the signal after a sudden jump can be accurately evaluated and the overall recovery stability of the signal can be quantified.

[0079] S2.3, since the impact of bubble jump signals on heavy metal monitoring data is not only reflected in the concentration of distribution, but also in the stability of signal recovery, it may be biased to rely solely on a single characteristic index (such as the jump distribution characteristic index or the signal recovery stability index) to judge the applicability of the threshold. The introduction of the comprehensive jump disturbance index is intended to solve this problem. By nonlinearly combining the weights of these two core indexes, a comprehensive index that can fully reflect the characteristics of bubble jump interference is obtained. A high jump distribution characteristic index value means that the current preliminary threshold can fully capture the dynamic characteristics of bubble jumps and is suitable for subsequent monitoring signal filtering.

[0080] Sudden disturbance index The calculation of integrates the sudden jump distribution characteristic index and the signal recovery stability index through a combination of polynomials and fractions to generate a decision value that reflects the sudden jump characteristics of the bubble. The formula is as follows: ;in: It is the jump distribution characteristic index, which reflects the distribution concentration of the bubble jump signal in the time window. is a signal recovery stability index that quantifies the speed and amplitude of recovery after the outburst signal subsides. It is a nonlinear combination parameter used to balance the relative weights and influence of the two indices, making the sudden jump distribution characteristic index more sensitive and adaptive when comprehensively considering the sudden jump distribution and recovery stability.

[0081] The square term and cubic term in this formula respectively strengthen the contribution of distribution concentration and recovery stability to the comprehensive sudden disturbance index, and balance the relative importance of the two characteristic indexes through fractional combination and power operation. If the sudden signal distribution concentration at a certain moment is high and the recovery stability is good, the value of the sudden distribution characteristic index will increase significantly, indicating that the current preliminary threshold is reasonable; on the contrary, if the sudden distribution characteristic index value is low, it indicates that the adaptability of the threshold parameter is weak and needs further adjustment.

[0082] S2.4, compare the size of the sudden disturbance index with the preset system threshold. If the sudden disturbance index is greater than the system threshold, the current preliminary threshold is confirmed to be suitable as the final adaptive threshold; if the sudden disturbance index is lower than the system threshold, the preliminary threshold is adjusted according to the difference to obtain a more suitable interference correction parameter. This process ensures that the final selected threshold is not only accurate, but also can effectively cope with the dynamic characteristics of the signal sudden jump.

[0083] The disturbance control criterion module realizes adaptive interference correction in a complex bubble jump signal environment by constructing a rigorous multi-dimensional threshold screening and optimization mechanism. The module first uses the trend analysis model to perform preliminary threshold calculations on the bubble jump curve parameters, and then conducts an in-depth evaluation of the preliminary threshold through the two major characteristics of the jump distribution characteristic index and the signal recovery stability index, capturing the distribution concentration and recovery stability of the bubble jump signal to accurately reflect the actual impact of the interference. To ensure the rationality of the screening threshold, the module further introduces a comprehensive jump disturbance index, nonlinearly combines the two characteristic indexes, and screens the optimal threshold parameters with a comprehensive decision value, ensuring that the final output adaptive threshold can efficiently cope with the interference of dynamic jump signals. Through the intelligent and dynamic threshold optimization mechanism of this module, the monitoring system can maintain the stability and high precision of heavy metal detection data under different working conditions, effectively enhancing the system's anti-interference ability in complex fluid environments.

[0084] In the electroplating wastewater monitoring system, the interference of bubble jump signals causes discontinuous fluctuations and deviations in the monitoring data. These jump effects mask the actual changes in heavy metal concentrations, seriously affecting the monitoring accuracy. As the core link of precision filtration, the Chengyuan noise removal module receives the adaptive threshold parameters provided by the disturbance control criterion module, and performs point-by-point comparison and noise removal on the monitoring data based on a multi-stage filtering structure to reconstruct a high-precision heavy metal concentration curve. This module ensures the purity and continuity of the monitoring data through multi-stage filtering and reconstruction methods.

[0085] like Figure 2 As shown in the figure, the operation process of the Chengyuan denoising module includes the following:

[0086] S3.1, first compare the monitoring data point by point, and apply the interference threshold parameter to identify the sudden jump effect contained in the signal. In the point-by-point comparison process, the dynamic weight elimination coefficient is used to automatically adjust the weight priority of the data point when eliminating it to ensure the flexibility of data processing.

[0087] Weight elimination coefficient : Defined as the nonlinear ratio of the signal value to the threshold, used to identify sudden interference data points.

[0088] ;in, is the current monitoring data point, is the previous data point. The weight elimination coefficient enhances the response to the fluctuation signal through power operation, making the weight amplified nonlinearly with the interference intensity.

[0089] S3.2, after completing the dynamic weight elimination, the monitoring signal is subjected to multi-level filtering based on the adaptive filtering structure. The filtering structure is divided into three filtering layers, and each layer of filtering is optimized for different signal characteristics to ensure the accuracy and continuity of the elimination process.

[0090] First level filtering: low frequency smoothing filtering.

[0091] The first filter layer uses an adaptive low-frequency smoothing method to smooth the low-frequency components of the signal in the current window to eliminate small fluctuations. Low-frequency smoothed output signal It can be expressed as: ;in, Represents the size of the sliding window, that is, the total number of time points or data points selected during the calculation process to define the local range of the signal. Indicates the index or position of a specific data point in the sliding window, ranging from 1 to . is the standard deviation of the window width, ensuring the smoothing effect on suppressing local jumps.

[0092] Secondary filtering: intermediate frequency tuned filtering.

[0093] The secondary filtering layer is based on the intermediate frequency component tuning method to identify and eliminate the intermediate frequency sudden jump interference with large fluctuations, and generate the intermediate frequency filtering result of the signal. : ; Here, the intermediate frequency of the low-frequency smooth signal is tuned to ensure that important heavy metal change information is retained while eliminating frequent sudden jump effects.

[0094] Three-stage filtering: high-frequency spike filtering.

[0095] The third-level filtering layer mainly targets high-frequency spike signals and removes strong interference data points that appear at higher frequencies. It is expressed as: ;in, It is the high frequency filtering control parameter to ensure that the high frequency jump is completely suppressed.

[0096] S3.3, after ensuring that the high-frequency spike filter signal after multi-stage filtering is eliminated, signal reconstruction is still required to fill in possible signal gaps or deviations. Based on the signal interpolation reconstruction method, interpolation reconstruction is performed on the data segment after the sudden interference is eliminated to obtain denoised data. The denoised data points It is expressed as: ;in, is the reconstructed data point obtained by interpolation, It is an adaptive balance factor to ensure the smoothness and continuity of data during signal reconstruction.

[0097] The Chengyuan noise removal module achieves efficient removal and precise filtering of bubble jump interference signals in electroplating wastewater monitoring by establishing a set of multi-stage adaptive filtering and reconstruction structures. The module first identifies the jump signal based on adaptive threshold parameters through point-by-point comparison and dynamic weight elimination methods, and then separates and eliminates various jump effects through multi-stage filtering and collaborative processing of low-frequency smoothing, medium-frequency tuning and high-frequency spikes, while retaining key information on changes in heavy metal concentrations. During the filtering process, the module effectively isolates interference signals in different frequency bands and avoids signal loss that may be caused by traditional single-stage filtering. Finally, through interpolation reconstruction and smoothing correction, the module accurately reconstructs the data segment after the jump is eliminated to ensure that the output heavy metal concentration data is balanced in purity and continuity. This complex and precise noise removal process significantly improves the anti-interference ability of the monitoring system in complex fluid environments, ensures the accuracy and stability of monitoring data, and provides highly reliable data information for downstream modules.

[0098] The operation process of the net balancing module includes the following:

[0099] S4.1, first receive the reconstructed signal from the Chengyuan denoising module, integrate the denoised data with the historical monitoring data, and construct a long time series data set. By analyzing the trend changes of the reconstructed data and historical data layer by layer, identify the potential trend fluctuation pattern, and judge whether the current data remains stable within the long-term statistical trend. If the current monitoring data and historical data show consistency in trend characteristics, the reconstructed data is considered to have long-term stability; if the data fluctuation deviates from the historical trend, it is marked as a potential abnormal fluctuation and recorded.

[0100] By calculating the mean and fluctuation variance of historical data, the overall fluctuation characteristics of the monitoring data are obtained for subsequent traceability analysis.

[0101] S4.2, based on the trend stability analysis, by constructing a traceability analysis model, further evaluate whether there is any sudden jump interference that is not fully identified or corrected in the denoised data. By extracting the key differences between the current data and the historical data, a data traceability map is formed to intuitively display the characteristic fluctuations and impact range of the interference jump. The results of the traceability analysis can help identify the long-term effects of bubble jumps in the monitoring process, especially the residual interference on historical data. At the same time, the traceability analysis model summarizes these characteristic differences as dynamic evaluation results to provide accurate data support for the generation of compensation coefficients.

[0102] Provenance analysis quantifies the difference between the denoised data and the historical data by constructing a dynamic deviation factor. The dynamic deviation factor is used to identify the potential legacy effects of sudden jump interference in the denoised data in order to determine the applicable strength of the compensation coefficient. Dynamic Deviation Factor : ;in, To avoid decimal values ​​with zero denominator, The mean of historical data, Volatility variance. The dynamic deviation factor captures the residual jump interference characteristics in the denoised data by nonlinearly combining the deviation of the monitored value from the historical mean and the ratio of the volatility variance.

[0103] S4.3, based on the results of the traceability analysis, the degree of deviation between the denoised data and the historical trend data, the long-term fluctuation stability and the continuous impact of the sudden jump interference are comprehensively considered to generate the sudden jump compensation coefficient. This compensation coefficient reflects the long-term effect of the bubble sudden jump interference during the monitoring process, and aims to correct the potential sudden jump feature recognition bias in the turbulence deconstruction module. The process of generating the sudden jump compensation coefficient emphasizes the comparison between the long-term trend and the instantaneous fluctuation, ensuring that the recognition accuracy of the turbulence deconstruction module can be adaptively improved in the next monitoring cycle.

[0104] The jump compensation coefficient is obtained by combining the deviation factor and the historical data characteristics to achieve multi-level correction of the bubble jump signal. : The sudden jump compensation coefficient constructs an adaptive compensation force for sudden jump interference through a nonlinear combination of deviation factors, historical fluctuation characteristics and the degree of deviation of real-time data, ensuring monitoring accuracy under dynamic conditions.

[0105] S4.4 feeds back the generated jump compensation coefficient to the turbulence deconstruction module, and closes the loop to correct the jump feature recognition and dynamic threshold calculation of the turbulence deconstruction module, so that the system has adaptive optimization capabilities during continuous monitoring. Through this closed-loop feedback mechanism, the net tracing balance module realizes the long-term self-adjustment of the monitoring system, continuously improves the accuracy of jump interference recognition and correction accuracy, and ensures that the data remains stable and consistent in a dynamic environment.

[0106] The net tracing balance module achieves comprehensive integration and difference identification of denoised data and historical data through precise traceability analysis and dynamic compensation mechanism, generates jump compensation coefficients and feeds them back to the turbulence deconstruction module, thereby optimizing the anti-interference performance of the monitoring system. The module calculates the dynamic deviation factor based on real-time data and historical data, obtains the compensation coefficient to improve the long-term stability of the system, ensures the continuous improvement of monitoring accuracy, and constructs a closed-loop adaptive correction process, providing a high-precision and robust anti-interference solution for complex electroplating wastewater monitoring.

[0107] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0108] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0109] It should be noted that, in this article, if there are relational terms such as first and second, etc., they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "including a..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0110] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A system for monitoring heavy metal content in electroplating wastewater, characterized in that: include: Turbulence deconstruction module, disturbance control criterion module, source clarification and noise removal module and net tracing balance module; Turbulence deconstruction module: obtains electroplating wastewater monitoring signals, identifies bubble jump signal characteristics through turbulence signal feature extraction algorithm, generates bubble jump curve parameters; passes bubble jump curve parameters to disturbance control criterion module; Disturbance control criterion module: According to the bubble jump curve parameters, the trend analysis model is used to preliminarily calculate the adaptive threshold parameters of bubble interference, and the optimal threshold is selected by combining the comprehensive decision value of the jump distribution characteristic index and the signal recovery stability index to realize the dynamic correction of the jump signal; The threshold parameters obtained by screening are passed to the source denoising module; Chengyuan De-noising Module: Based on the adaptive threshold parameters, the multi-level filtering structure is used to compare the real-time monitoring data point by point, remove the data segments affected by the sudden bubble jump, and reconstruct the heavy metal concentration curve; the de-noised data is passed to the Jingsu Balance Module; Net tracing balance module: Integrate and trace the noise-removed monitoring data with historical data, generate a sudden jump compensation coefficient and feed it back to the identification module to optimize the correction accuracy; The generated jump compensation coefficient is fed back to the turbulence deconstruction module to optimize the recognition accuracy of bubble jump characteristics in the next turbulence deconstruction, forming a closed-loop adaptive correction process; The operation process of the turbulence deconstruction module includes the following: S1.1, after obtaining the electroplating wastewater monitoring signal, firstly conduct real-time dynamic feature analysis on the original data stream of the sensor; adopt the local variation analysis method of the signal to calculate the local fluctuation amplitude of the signal through a dynamic sliding window and transient mutation rate ; Local fluctuation amplitude: calculated by the variance and mean of the signal in the sliding window to reflect the fluctuation intensity of the signal in unit time; ;in, is the first The signal value at each position, is the signal mean in the window, is the window length; Transient mutation rate: calculate the relative mutation rate between the current signal point and the previous signal point; ;in, is the current signal value, is the signal value at the previous moment, To prevent tiny numbers from dividing by zero; S1.2, turbulence factor constructed using turbulence signal model , used to quantify the degree of turbulence in the signal; Turbulence factor: calculated by the structural entropy and local peak distribution of the signal, describing the complexity of the signal under turbulent conditions; ;in, It represents the structural entropy of the signal within a specific window and is defined as the probability distribution entropy of the signal fluctuation. It is defined as the local peak density within the window, that is, the ratio of the number of peak points in the window to the window length; S1.3, combining the local fluctuation amplitude, transient mutation rate and turbulence factor, the bubble jump curve parameter set is constructed through nonlinear combination; S1.4, after obtaining the bubble jump curve parameters, use the classification-based machine learning model to perform real-time pattern recognition on the bubble jump signal and heavy metal concentration signal; the input of the machine learning model is the bubble jump curve parameters, and the model classifies the input features in real time; the output is the recognition result, that is, whether the marked signal segment belongs to a bubble jump.

2. The system for monitoring heavy metal content in electroplating wastewater according to claim 1, characterized in that: The operation process of the disturbance control criterion module includes the following: S2.1, first receive the bubble jump curve parameters output by the turbulence deconstruction module The time series trend analysis model is used to calculate the initial threshold of the bubble jump curve parameters; the time series trend analysis model is based on the slope and curvature changes of the trend line in the sliding window to preliminarily estimate the adaptive threshold of bubble interference. .

3. The heavy metal content monitoring system for electroplating wastewater according to claim 2 is characterized by: The operation process of the disturbance control criterion module also includes the following: S2.2, after the preliminary threshold is calculated, the sudden jump distribution characteristic index and the signal recovery stability index are calculated to generate the sudden jump disturbance index, and the preliminary threshold is evaluated and screened; The sudden jump distribution characteristic index is used to evaluate the concentration and dispersion of bubble sudden jump signals in the time dimension; the calculation formula of the sudden jump distribution characteristic index is: ;in, is the mean value of the bubble jump curve parameter, is the center position of the window, is an exponential modulation parameter used to adjust the decay speed. Represents the index of the data point, i.e. the Points, The range is from 1 to , Representative The time value corresponding to each time point; The signal recovery stability index is used to evaluate the speed and amplitude of the signal returning to a stable state after a bubble jump. The calculation formula of the signal recovery stability index is: ;in, is an index adjustment parameter used to enhance the sensitivity of the signal recovery stability index to the signal recovery characteristics. Representative The signal strength of the data point, that is, The signal value observed at a certain time or position.

4. The system for monitoring heavy metal content in electroplating wastewater according to claim 3, characterized in that: The operation process of the disturbance control criterion module also includes the following: S2.3, calculation of the sudden jump disturbance index: the sudden jump distribution characteristic index and the signal recovery stability index are integrated by combining polynomials and fractions to generate a sudden jump disturbance index reflecting the sudden jump characteristics of the bubble; S2.4, compare the size of the sudden disturbance index with the preset system threshold. If the sudden disturbance index is greater than the system threshold, confirm that the current preliminary threshold is suitable as the final adaptive threshold; if the sudden disturbance index is lower than the system threshold, adjust the preliminary threshold according to the difference to obtain a more adaptable interference correction parameter.

5. The system for monitoring heavy metal content in electroplating wastewater according to claim 4, characterized in that: The operation process of the Chengyuan noise removal module includes the following: S3.1, first compare the monitoring data point by point, and apply the interference threshold parameter to identify the sudden jump effect contained in the signal; S3.2, after completing the dynamic weight elimination, the monitoring signal is subjected to multi-stage filtering processing based on the adaptive filtering structure; S3.3, after ensuring that the sudden jump interference is eliminated, the high-frequency spike filter signal after multi-stage filtering is reconstructed to fill the signal gap or deviation; based on the signal interpolation and reconstruction method, interpolation and reconstruction are performed on the data segment after the sudden jump interference is eliminated to obtain denoised data.

6. The system for monitoring heavy metal content in electroplating wastewater according to claim 5, characterized in that: The operation process of the net balancing module includes the following: S4.1, integrate the denoised data with the historical monitoring data to construct a long time series data set; by analyzing the trend changes of the reconstructed data and the historical data layer by layer, identify the potential trend fluctuation pattern and determine whether the current data remains stable within the long-term statistical trend; If the current monitoring data and historical data show consistency in trend characteristics, the reconstructed data is considered to have long-term stability; If data fluctuations deviate from historical trends, they are marked as potential abnormal fluctuations and recorded; S4.2, based on the trend stability analysis, by constructing a traceability analysis model, evaluate whether there is any sudden jump interference that is not fully identified or corrected in the denoised data; By extracting the key differences between current data and historical data, a data traceability map is formed to intuitively display the characteristic fluctuations and impact range of interference jumps; S4.3, based on the results of the traceability analysis, comprehensively considers the degree of deviation between the denoised data and the historical trend data, the long-term fluctuation stability and the continuous influence of the sudden jump interference, and generates the sudden jump compensation coefficient; it reflects the long-term effect of the bubble sudden jump interference during the monitoring process, and aims to correct the potential sudden jump feature recognition bias in the turbulence deconstruction module; S4.4 feeds back the generated jump compensation coefficient to the turbulence deconstruction module, and closes the loop to correct the jump feature recognition and dynamic threshold calculation of the turbulence deconstruction module.

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