A method for real-time online detection of wastewater indicators

Abstract

This invention relates to a method for real-time online detection of wastewater indicators. Before detection, a low-concentration water body is injected into the detection chamber to perform micro-dose detection. By observing whether there is a nonlinear shift in the initial response behavior, it is determined whether there is residual response on the inner wall of the detection chamber. If so, a local delayed detection and pre-rinsing operation is performed to suppress interference responses caused by the chemical memory of the detection chamber. After completing the pre-response suppression, a slow-varying physical stimulus process is applied to the wastewater to be tested, stimulating the release of masked pollutants in the wastewater. The pollutant response signals are continuously tracked, and fluctuating behavior is identified. Based on the changing trend of the response curve, a stable response interval is identified as the sampling point for final detection. For cases with superimposed responses from multiple pollutants, the rise rate, fall rate, and tailing characteristics of the response profiles of each indicator are analyzed to identify the physical differences of multiple response signals and perform physical-level decoupling, improving the accuracy of identifying multiple pollutants.

Description

Technical Field

[0001] This invention relates to a method for online detection of wastewater, specifically a method for real-time online detection of wastewater indicators. Background Technology

[0002] The method disclosed in the existing Chinese patent CN109580901A, an online water quality monitoring instrument, while possessing a structurally sound flushing device and multiple probe combinations for online monitoring of conventional indicators such as pH, turbidity, conductivity, residual chlorine, and dissolved oxygen, and reducing probe contamination and maintenance frequency through automatic flushing, still suffers from numerous technical and functional shortcomings in actual operation. These issues directly affect the adaptability, response accuracy, and intelligence of the wastewater online monitoring system in complex pollution scenarios. Specifically, the following analysis addresses these shortcomings: First, regarding the ability to identify and suppress response interference, this method primarily relies on the flushing structure to prevent probe contamination, but it lacks a response memory or residual behavior recognition mechanism for the detection chamber, and it doesn't address how to determine whether flushing is thorough. Since most probes exhibit physical or chemical adsorption effects on the detection chamber walls, sensing membranes, or interfaces after encountering high concentrations of pollutants, the resulting response residue will cause significant shifts or delays in subsequent detections, threatening the accuracy of real-time monitoring. Especially in situations with short detection intervals and drastic changes in pollutants, the lack of a systematic identification and dynamic removal mechanism for residual responses easily leads to ghost contamination of current data by historical responses.

[0003] Secondly, this technology is designed only for static water sample detection and does not consider the ability to physically stimulate or dynamically induce the release of masked pollutants in wastewater. Since some organic or heavy metal pollutants may lie dormant in water bodies under normal conditions in the form of micelles, ion binding, or particle size shielding, static monitoring using only traditional electrochemical or optical probes can easily miss key pollutants, leading to false negatives. Therefore, the lack of a physical stimulus management system, such as a mechanism for gradual changes in temperature, electric field, or pressure, to induce the release and characterization of hidden pollutants, is one of the shortcomings of this technology. Thirdly, in response to complex pollution scenarios, this invention only supports simultaneous acquisition of multiple parameters but does not provide a hierarchical decoupling strategy for response behaviors involving the superposition, masking, or conflict of signals from different pollutants. In reality, wastewater often exhibits complex curve behaviors such as the coexistence of multiple pollutants, overlapping response peaks, and elongated tails. Simply reading values ​​from multiple probes cannot accurately determine the correspondence between signal sources and pollutants. For example, pH changes can simultaneously affect the activity of metal ions and the rate of redox reactions. Without a physical-level response recognition and signal stripping mechanism, errors such as misjudgment of response peaks and inversion of causality are highly likely to occur. In addition, the system lacks advanced intelligent stripping algorithms such as response behavior structure analysis, identification of dominant behavioral variables, or response inconsistency matching, making it difficult to handle complex cross-contamination conditions.

[0004] Furthermore, at the signal processing level, the system only involves data acquisition and feedback processes, failing to propose how to perform in-depth data analysis and state identification based on the dynamic characteristics of the response curve (such as slope changes, response hysteresis, peak symmetry, and tail bounce). This makes the detection system more of a recorder than an interpreter, lacking the ability to understand and intervene in the dynamic structure of pollutant response, thus limiting its judgment and prediction capabilities under abnormal pollution fluctuations. In addition, the technology does not incorporate any form of response curve fractal analysis, information entropy change judgment, or signal complexity index to distinguish stable response segments from interference bands. For the initial detection of certain pollutants, the excitation process is often accompanied by non-stationary behaviors such as overshoot, suppression, and bounce. If these behaviors are not identified and filtered by the system, the sampling point can easily fall into the dynamic transition zone, leading to distorted detection results. Moreover, the rinsing and monitoring processes do not form a closed-loop feedback loop; for example, the lack of rinsing completion criteria (such as cavity interface tension recovery judgment and material response displacement identification) makes the rinsing effect unquantifiable and difficult to accurately control cleaning quality. Finally, its probe layout and detection process also fail to demonstrate the ability to schedule interventions for time-series responses. For example, when multiple pollutant responses overlap, it does not have a pre-disturbance and delayed detection structure to actively avoid synchronous interference peaks; when resistant pollutants are present, it also lacks the ability to switch to different intervention strategies (such as high-frequency low-amplitude perturbations, local electrical excitation untangling, etc.), resulting in weak overall system regulation intelligence.

[0005] In summary, although existing technologies have made effective optimizations in structural design and probe flushing and have certain engineering feasibility, they have fundamental shortcomings in core response and processing mechanisms, adaptability to complex pollution, dynamic data analysis, and proactive intervention strategies. They cannot meet the increasingly intelligent, real-time, and complex actual needs of current wastewater indicator detection. Summary of the Invention

[0006] The purpose of this invention is to provide a method for real-time online detection of wastewater indicators, thereby addressing some of the drawbacks and shortcomings pointed out in the background art.

[0007] The present invention adopts the following technical solution to solve the above-mentioned technical problems: a method for real-time online detection of wastewater indicators, comprising: injecting low-concentration water into the detection chamber before each formal detection to perform micro-dose detection, judging whether there is response residue on the inner wall of the detection chamber by observing whether there is nonlinear shift in the initial response behavior, and if so, performing local delayed detection and pre-rinsing operation to suppress interference response caused by the chemical memory of the detection chamber;

[0008] After completing the pre-response inhibition, a slow-change physical stimulation process is applied to the wastewater to be tested, including slow temperature rise, potential gradient sweep or pressure disturbance, to stimulate the release of masked pollutants in the wastewater; after the pollutants are stimulated and released, the pollutant response signal is continuously tracked to identify fluctuation behavior, and the stable response range is identified based on the changing trend of the response curve, and the stable response range is used as the sampling point for final detection.

[0009] For situations involving superimposed responses from multiple pollutants, the physical differences in the response signals of each indicator are identified by analyzing the rise rate, fall rate, and tailing characteristics of the response profiles. Furthermore, physical-level decoupling and separation are performed based on the intrinsic behavior of each pollutant's response to improve the accuracy of identifying multiple pollutants.

[0010] Furthermore, in the process of judging the response residue of the cavity, the response residue trajectory formed by different pollutants is matched based on the cavity's historical self-residue spectrum, and the future residue probability is predicted based on the frequency and amplitude trend; the pre-rinsing operation includes a material response sensing section, which monitors the change of surface tension of the cavity material during rinsing, and judges whether the cavity has returned to the chemical baseline state by the interfacial energy recovery behavior. If it has not recovered, rinsing continues until the surface behavior is recovered.

[0011] Furthermore, the slow-varying physical stimulation process is controlled by a self-regulating stimulation manager. The manager adjusts the stimulation intensity and sequence order according to the hysteresis factor of the previous response of the pollutant, and switches to a high-frequency, low-amplitude perturbation strategy when the pollutant exhibits stimulation resistance. Before applying the potential gradient sweep, the mapping relationship between the water charge distribution and viscous resistance is pre-analyzed to predict the ion migration trajectory, and local pre-untangling electrostimulation is performed on areas where pollutants are easily entangled or electrohysteretic.

[0012] Furthermore, the fractal recognition mechanism for response behavior is introduced to analyze the changes in geometric complexity of the response curve in real time, in order to identify the dynamic inflection point between the release stage and the stable stage of the pollutant, and to take the first stable behavior region after the inflection point as the starting segment of the stable response; if an overexcitation + suppression band appears in the stable response region in the early stage of excitation, the region will be marked by the system as a behavior masking region and excluded from sampling.

[0013] Furthermore, a response contradiction identification strategy is introduced in the response stripping process of the composite pollutant. When there is a high degree of behavioral conflict in the responses of two pollutants, the conflict traction method is used to split the signals of the two pollutants into the behavioral stability zone and then decouple them. Before response stripping, the dominant behavioral variables of the pollutant response are identified, with the dominant behavior characterized by the initial rate, duration or tail rebound. Pollutants with dominant behavior are stripped first.

[0014] Furthermore, in the response contradiction identification strategy, the normalized similarity analysis is performed on the response curve of each pollutant, and the degree of response conflict is judged in combination with the peak symmetry. If the similarity is less than the set threshold and the symmetry direction is opposite, it is identified as a high degree of behavioral conflict. The conflict traction method includes applying a time window transformation operation to the overlapping peak segments in the response signal, so that the high-frequency change segment is moved to the subsequent band to form a behaviorally stable band of response decoupling.

[0015] Furthermore, when identifying the response conflict, structural conflict matching analysis is performed by combining the differences in the direction of response slope change and the number of inflection points to distinguish between interference-type overlap and true composite response, and only the latter is subjected to traction stripping; the traction stripping process performs tail displacement on pollutants with long response duration, actively moving the trailing segment to the end of the response to facilitate the distinction of instantaneous pollutant signals.

[0016] Furthermore, in the identification of the dominant behavioral variable, the contribution rate of the response energy of each pollutant is calculated, and the one with the highest contribution rate is determined as the dominant response pollutant factor, and the signal segment corresponding to the factor is preferentially stripped; in the response stripping process, if the dominant variables of two pollutants are both duration, the initial response delay is compared, and the one with an earlier response is stripped.

[0017] In complex response environments, pollutants may have similar dominant behavioral variables, such as response duration as the dominant characteristic. When the response durations of multiple pollutants are similar, traditional methods struggle to clearly distinguish the order of dominant factors, leading to signal mixing and a decrease in separation accuracy. This method introduces the following response behavior function:

[0018]

[0019] in:

[0020] Λ i R is the dominance intensity function value of the i-th pollutant, used to compare the degree of dominance; i (t) represents the amplitude of the response signal of the i-th pollutant at time t; The response rate change reflects the dynamic characteristics of the pollutant's reaction; κ i A response dynamic penalty factor (within the range of 0.1–1) is used to correct for the interference effect of response fluctuations on dominance. i0 ,t i1 Let θ be the start and end times (steady-state response interval) of the response curve for the i-th pollutant; i The duration of the response, i.e., t i1 -t i0 ;δ i The initial response delay, relative to the trigger point of the stimulus signal;

[0021] After extracting pollution factors from the composite response signal, the system uses the function Λ i Calculate the dominant intensity of all pollutants; if the Λ of a certain pollutant j j The maximum value is:

[0022] Λ j =max{Λ1,Λ2,…,Λ n}

[0023] Then the pollutant is identified as the dominant responder; if there are two or more pollutants with Λ i If the values ​​differ by less than 5%, and the dominant variable is response duration, then the system comparison δ i The value is prioritized for stripping the response with the shortest initial delay; once the stripping order is confirmed, the signal decoupling operation of the corresponding response segment is immediately executed.

[0024] Furthermore, after the dominant behavior pollutant is stripped, a recursive self-correction process is performed on the remaining response signal. Based on the difference in signal waveforms before and after the dominant behavior pollutant is stripped, the stripping boundary is dynamically adjusted. If the dominant variable is tail rebound behavior, the sampling endpoint window is sampled after the stripping delay so that the rebound segment information is fully included in the dominant response extraction area.

[0025] This invention provides a method for real-time online detection of wastewater indicators. Addressing the problems of fuzzy identification, data drift, and inaccurate identification in traditional detection technologies under complex environments such as the coexistence of multiple pollutants, superimposed response interference, and unstable signals, this invention proposes a series of highly innovative structural response regulation and dynamic identification mechanisms. These significantly improve the accuracy, stability, and adaptability of the detection. Its beneficial effects are mainly reflected in the following aspects:

[0026] By introducing a pollutant behavior dominant variable identification mechanism, combined with the response energy contribution rate calculation function Λ i This system can accurately identify the dominant pollutant and its signal boundaries, effectively solving the problem of difficulty in distinguishing pollutants when responses overlap. It is particularly suitable for complex pollution systems such as those containing compound organic matter, heavy metals, and multi-source toxic substances. Through micro-dose detection and nonlinear offset judgment technology, combined with real-time monitoring of the recoverable behavior of material interfaces during pre-rinsing, the system effectively removes residual response signals, ensuring consistency and repeatability in each round of detection and avoiding misleading the current response by historical pollution. By introducing a response contradiction identification strategy, conflict traction method, and response structure matching analysis technology, it can accurately identify behavioral conflicts, response tails, or excessive overlap segments between pollutants. Furthermore, through strategies such as time window adjustment and tail displacement, it achieves physical-level signal decoupling, significantly improving the pollutant resolution capability under complex response environments.

[0027] By introducing a response fractal recognition mechanism and a recursive self-calibration mechanism, this method achieves the identification and intelligent calibration of multi-dimensional dynamic structures such as the inflection point, rebound behavior, and boundary changes of the signal curve, avoiding misjudgment or missed detection of pollutants due to signal drift, failure to sample the tail, or response suppression. This method remains stable and reliable even in industrial wastewater scenarios with high fluctuations, high background interference, and large response rate ranges. Its self-regulating stimulus manager, untangling electrostimulation strategy, and stimulus resistance identification and switching modules can adjust stimulus parameters in real time based on pollutant response feedback, ensuring complete pollutant release and a truly effective response. Attached Figure Description

[0028] Figure 1 This is a simplified flowchart of the real-time detection process for wastewater indicators according to the present invention.

[0029] Figure 2 This is a simplified flowchart of the cavity residue tracing and pollutant identification process of the present invention.

[0030] Figure 3 This is a diagram showing the functional relationship between the composite pollutant response and stripping of the present invention.

[0031] Figure 4 This is a simplified flowchart of the main process for wastewater testing in a chemical industrial park, as described in Embodiment 1 of the present invention.

[0032] Figure 5 This is a flowchart illustrating the breakdown of the combined pollution response of mixed drainage from an urban wastewater treatment plant, as described in Embodiment 2 of the present invention. Detailed Implementation

[0033] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0034] Combined with appendix Figure 1In a method for real-time online detection of wastewater indicators according to the present invention, a micro-dose detection step is performed before each round of formal detection to determine whether there is any response interference caused by residual pollutants from the previous detection in the detection chamber. The detection system controls the valve to open and slowly inject a low-concentration water body with a pre-set composition into the sensing detection chamber. This low-concentration water body does not contain the target pollutant or contains only trace amounts of background impurities and does not produce a significant chemical reaction with the sensor. Its function is to provide a response reference medium for observing whether the sensing system exhibits any unexpected response changes. Immediately after the low-concentration water body is injected, the system starts a high-sensitivity rapid response acquisition program to record the initial response signal output by the sensor in real time. The signal curve is compared with the standard pollution-free reference response curve stored in the device. If the sensor output signal is found to have nonlinear shift, fluctuation delay, or abnormal step characteristics in the initial stage after injection, it indicates that the detection chamber still has identifiable pollutant response traces left from the previous detection. This shift signal is defined as a residual response caused by the "chemical memory of the detection chamber". To further eliminate the impact of this interference factor on the accuracy of the test results, the system implemented two remedial measures after confirming the existence of the offset. The first was a partial delay in detection, which involved pausing the current sampling process, delaying the detection time window, and waiting for the system response to stabilize before re-entering the main detection cycle. The second was a pre-rinsing operation, where the system injected a certain volume of cleaning buffer or background diluent into the detection chamber through a valve switching mechanism and activated a micro-vortex disturbance structure to dynamically flush the inner wall of the chamber at a low flow rate, promoting the desorption and diffusion of potentially adsorbed contaminants from the sensing area interface. After rinsing, the residual liquid was discharged through the outlet channel. During the rinsing process, the system monitored in real time whether the sensor response gradually approached the linear baseline. If a set threshold was reached, it confirmed that the chamber residue had been removed and terminated the rinsing operation, proceeding to the next detection preparation stage.

[0035] After completing the preliminary response suppression steps, i.e., confirming that there is no historical residual response interference in the detection chamber, the core excitation and detection stage for the wastewater sample to be tested begins. This stage applies slowly varying physical stimuli to the wastewater sample to excite pollutants in a masked or weakly activated state in the water, causing them to be released from their original stable or non-reactive state and transformed into a target response signal that can be recognized by the sensing system. The gradual physical stimulation process is executed by the detection system control module according to a preset control curve, including but not limited to three excitation methods: First, a gradual temperature increase, where a micro-heating unit is used to uniformly heat the wastewater in the detection chamber at a rate maintained within a gradual range of 0.5–2.0 degrees Celsius per minute. This prevents sudden changes in water quality that could damage the structure of pollutants, aiming to gradually release low-temperature adsorbed pollutants or pollutants embedded in inert colloidal structures. Second, a potential gradient sweep, where a slowly changing voltage gradient is applied by microelectrodes at both ends of the detection chamber, causing charged pollutant molecules or ions in the water to migrate, desorb, or restructure under the influence of the electric field, thereby enhancing their behavioral activity. Third, a pressure disturbance, where a low-frequency pressure fluctuation is created in the wastewater sample through a built-in micro-pump system or a controllable compression chamber, simulating natural flow conditions or shear effects, causing pollutants in the attached or stratified areas to move closer to the sensing surface or be released. These physical excitation processes can be implemented independently in a single manner or combined and adapted based on historical response behavior. After the pollutants are effectively released and enter a detectable state, the system activates the high-frequency response acquisition module to continuously track the sensor response signals in real time. Throughout the entire response cycle, it dynamically records the pollutant's behavior patterns, including the rise rate, peak value changes, signal fallback, and tail fluctuations of the response signal. The system uses an embedded response behavior analysis algorithm to identify the stable interval of the signal change. The stable interval is defined as the segment where the fluctuation amplitude of the response signal is below a preset threshold within a certain time range, and the first and second derivatives of the curve simultaneously approach zero. This indicates that the pollutant response has completed its release and entered a stable expression stage. This stable response region is locked by the system as the final sampling point for this round of detection. Subsequently, the system extracts the average value or multi-point weighted value within this interval as the basis for quantitative analysis of the pollutants.

[0036] To address the complex detection scenarios involving superimposed responses from multiple pollutants in wastewater samples, a physical-level signal decoupling and stripping mechanism based on differences in response behavior was designed to improve the accuracy and distinguishability of identifying multiple pollutants simultaneously. In actual wastewater, different types of pollutants, such as heavy metal ions, organic amines, volatile phenols, or sulfides, exhibit overlapping response curves under sensor action. This means that within the same detection time window, the response signals of multiple pollutants are superimposed in different ways into a single overall output, making it impossible to distinguish them using conventional methods. Therefore, response stripping is achieved through analysis of pollutant response profile characteristics. This includes: firstly, the system extracts curve features from the collected composite response signals, calculating the rise rate, fall rate, and tail duration of each identifiable peak or response abrupt change region. The rise rate reflects the release or reaction speed of the pollutant, while the fall rate is related to its dissipation or sensor surface saturation mechanism. The tail feature is mainly used to identify the tail response behavior of high-molecular-weight or slow-release pollutants. Then, these behavioral parameters are compared with pre-established pollutant... The system matches signals against an intrinsic response database containing typical response trajectories and structural characteristics of common pollutants under different water quality conditions. A similarity algorithm is used to classify and assign pollutants to the current composite response, identifying the specific signal location and behavioral segment of each pollutant within the overall composite response. Then, based on the identified differences in the physical behavior of the pollutants, the system employs a physical-level decoupling and stripping method for signal separation. This method does not rely on mathematical models or empirical function fitting but directly performs segmented response stripping according to the dominant segment of the pollutant on the response time axis. For example, it prioritizes extracting the pollutant signal with the fastest response rate and earliest start time, and then strips components with long tail times and slowly rising peak responses from the remaining signals. After each round of stripping, the behavioral parameters of the remaining signals are recalculated until the response segments of all significant pollutants are classified. In practice, this method supports a continuous verification mechanism for the stripped signals. Each stripped pollutant response segment undergoes intrinsic behavior reconstruction and comparison to determine if it possesses the expected response integrity. If residual aliasing is identified, conflicting segments are relocated, and the stripping order is adjusted or the response window is reconstructed, thus ensuring the accuracy and traceability of the separation effect.

[0037] Combined with appendix Figure 2To address the issue of residual contaminant responses within the detection chamber, a mechanism for identification and tracing based on historical self-residue profiles was constructed. This mechanism, combined with a pre-rinsing operation controlled by material surface behavior feedback, enables accurate identification and dynamic removal of contaminant residue interference, ensuring the detection system remains in a stable and interference-free baseline state before entering the formal detection process. Specifically, in determining the presence of residual responses within the detection chamber, the system not only considers the deviation between the current micro-dose detection signal and the pollution-free baseline response but also retrieves and analyzes a database of residual response characteristics recorded during the chamber's historical operation. This database constitutes the so-called "chamber self-residue profile," where each profile record contains the residual response trajectory of a specific contaminant within the chamber, including its residual trigger frequency, response amplitude changes, response hysteresis, dissipation period, and spatiotemporal information on the response distribution on the walls of different regions. In the actual comparison process, the system traces and matches the observed abnormal initial response waveform with the historical trajectories of multiple pollutants in the spectrum, uses a similarity measurement algorithm to comprehensively determine the source of the pollutant corresponding to the current residual signal, and calculates the probability value of its re-residue in the current detection cycle by combining the probability trend curve of the pollutant forming residues in historical detections. If the probability value exceeds the residual interference warning threshold set by the system, the system determines that the current cavity is in a non-reference chemical state and cannot directly enter the main detection process. Once the residue is confirmed, the system immediately performs a pre-rinse operation. Rinsing is not just a simple liquid replacement process, but an intelligent feedback process that includes a material response sensing segment. During rinsing, the system monitors the surface tension of the inner wall material of the cavity in real time through a sensor array or surface energy sensitive structure. This tension change reflects the interfacial energy coupling state between the surface molecular layer and the surrounding fluid. If the cavity material surface is still covered by contaminants or has polar adsorption, it usually manifests as an abnormal lag, spike, or oscillation in the tension curve. The system judges whether the rinsing has achieved its effect based on the trend of whether the interfacial tension has returned to the clean baseline state. If the monitoring results indicate that the cavity surface energy is still in an unstable state, the system extends the rinsing process and maintains a disturbed flow field or micro-heating to enhance the decontamination efficiency until the tension change stabilizes and returns to the baseline curve range within two consecutive cycles. Only then does the system consider that the detection cavity has been completely restored to a chemically neutral state and can proceed to the next step of water sample testing.

[0038] To enhance the identification of inactive, structurally embedded, or low-mobility pollutants in wastewater, a slow-varying physical stimulation process was specifically designed. This process is precisely controlled by a self-regulating stimulation manager with dynamic sensing and control capabilities, enabling adaptive stimulation and dynamic intervention for different pollutant response characteristics. This manager incorporates a pollutant response behavior library, a hysteresis factor discrimination model, and an intervention decision module. The hysteresis factor is a comprehensive parameter describing the time delay, response amplitude lag, and release efficiency experienced by a pollutant from initial stimulation to a stable response in the previous stimulation process. The system calculates the average slope of the response rise segment, peak delay time, and response gain per unit stimulation energy in the pollutant's previous response curve to comprehensively determine the required stimulation intensity range for the pollutant in this round of detection. Based on this, the system adjusts the physical stimulation intensity for this round of stimulation, including the temperature rise rate, potential gradient amplitude, and perturbation pressure gradient. Simultaneously, it adjusts the application order of multiple stimulation types, for example, first performing a slow temperature change, then applying a potential sweep, and finally initiating a micro-pressure perturbation. If the system identifies a pollutant exhibiting sluggish response or weak release behavior multiple times in continuous detection, it determines that the pollutant is present. In the event of stimulus resistance, the self-regulation manager will switch to a high-frequency, low-amplitude perturbation strategy. This involves reducing the intensity of individual stimuli while increasing the frequency, continuously interfering with the state of pollutant molecules in a small-dose, rapid-rhythm manner. This causes the pollutants to detach from stable structures or weakly adsorbed sites, increasing their release probability without disrupting the original water composition. Furthermore, before performing the potential gradient sweep, the system initiates a pre-analysis module to jointly map the charge spatial distribution and fluid viscosity resistance of the current test water body. The system then uses sensor information such as conductivity, temperature, and flow velocity to infer the concentration of pollutants in the water. The distribution density and spatial polarization trend of major charged species are analyzed, and the migration trajectory and trajectory deformation characteristics of ions under electric field drive are evaluated to predict the movement behavior of pollutants inside the cavity in advance. If high resistance, high viscosity or charge entanglement characteristics are identified in some areas, it is considered that the area hinders the release of pollutants. For this reason, the system triggers a local pre-untangling electro-excitation procedure, which applies local directional current pulses through microelectrodes located in specific areas of the cavity to unlock or break the ion encapsulation layer or polarization chain structure on the surface of the pollutants, so that they are in a releaseable state before entering the full cavity potential gradient excitation step.

[0039] To further improve the accuracy of identifying the true and stable response signals of pollutants, especially in complex situations where pollutant release processes involve dynamic transitions, irregular behavior, or aliasing responses, the system introduces a fractal recognition mechanism for response behavior. This mechanism, based on the geometric structural changes of the response curve, extracts local dimensional features of the signal curve in real time to identify key dynamic inflection points in the transition process from the excitation and release phase to the stable expression phase. After the sensor begins collecting response signals, the system segments the real-time generated response curve using a sliding window approach, calculates the local geometric complexity index of the response curve within each time period, and establishes a curve fractal degree spectrum using a non-integer order dimensionality extraction method. A higher fractal degree indicates more complex response behavior and stronger perturbations; a fractal degree approaching 1 indicates that the response has entered a stable and regular phase. Based on this, the system identifies fractal abrupt change points as dynamic inflection points of the behavior curve. When a significant decrease and stabilization of the fractal dimension is detected between adjacent time windows, it is considered that the pollutant release behavior has ended and entered a stable phase. The first segment with continuously stable fractal degree after this inflection point is marked by the system as the stable response initiation segment and used as the subsequent... The reference interval for continued sampling and data extraction; furthermore, in order to exclude the influence of non-true high-value responses or transient inhibition behaviors caused by excessive stimulation or response mechanism characteristics of certain pollutants in the initial stage of excitation, the system adds behavior masking discrimination logic on the basis of the above fractal analysis. If the response curve is identified as having an "overexcitation + inhibition" behavior pattern in the initial stage after excitation, that is, the response first produces a sharp increase in peak value in a short period of time and then quickly drops to the inhibition plateau, and the plateau fluctuates violently, the curve shows high-frequency oscillation or discontinuous shape, then the system marks the area as a behavior masking area, and excludes the data of this segment from the sampling mean calculation during the extraction of the stable response area, thereby preventing the erroneous collection of high-noise or non-representative data segments as pollutant feature input.

[0040] To achieve high-precision identification and effective separation of response signals from complex pollutants, a conflict identification and dominant variable priority stripping mechanism based on response behavior structural characteristics is proposed. This mechanism addresses the problem of signal overlap and interference fusion caused by different pollutants simultaneously acting on the same response path during sensing and detection. When the system identifies complex pollution response behaviors in the detection data—that is, multiple pollutants triggering different degrees of response changes at the same time—the system first executes a response conflict identification strategy through the response behavior analysis module. This strategy extracts the set of behavioral parameters for each pollutant based on the characteristic segment morphology of its response curve, including but not limited to response initiation rate, peak rise time, fall rate, tail duration, and signal perturbation mode. It also calculates the conflict factors between pollutants in key behavioral segments. If any two pollutants' responses are found to be highly overlapping in time but exhibit significant conflicts in behavioral characteristics such as response direction, tailing form, or curve symmetry, the system determines them to be in a highly conflicting relationship and initiates a conflict-driven method for signal stripping. This conflict-driven method identifies the response trends of relatively dominant pollutants in conflict zones, dynamically extends their behavioral range, and shifts the interfering segments along the time axis to their respective relatively stable response behavior zones. This allows for the physical separation of pollutants within the stable behavioral range, achieving structural decomposition of the interference zone. Subsequently, to improve the accuracy and efficiency of the separation process, the system extracts and analyzes the dominant variables of the response behavior characteristics of all pollutants before separation. Based on quantitative comparisons of characteristic indicators such as initiation rate, response duration, and tail bounce amplitude, the system identifies the pollutant signal segment with the strongest and most dominant behavior, defining it as the dominant behavior pollutant. When multiple pollutants have different dominant response variable types, such as one with a rapid initiation response and another with a long tail, the pollutant with the earlier and faster initiation response is prioritized for separation. If the dominant variable for multiple pollutants is duration, their initiation response delay is further compared, and the pollutant with the shorter delay is prioritized for separation. If the dominant variable is tail bounce, the system delays the sampling window to fully collect tail features before separation.

[0041] Combined with appendix Figure 3To achieve accurate decomposition and structural identification of response signals from complex pollutants, a response contradiction identification strategy based on behavioral characteristic differences was constructed. Furthermore, a conflict traction method was introduced to decouple overlapping signals. The specific implementation is as follows: During the detection process, when the system identifies multiple pollutants generating overlapping response signals within the same time window, resulting in multi-peak overlap, intertwined behaviors, or unclear response disturbances, the system first standardizes the response curves of each pollutant, normalizing and mapping their response amplitude, response time interval, and derivative variation trends to ensure that the response characteristics of each pollutant are consistent across a unified scale. Based on structural comparison, the system then uses normalized similarity analysis to calculate the overlap index of each pollutant in the response segment. This index comprehensively considers the overlap ratio of response curves in the time domain, the structural consistency of the response path, and the consistency of signal fluctuation direction, thus initially determining whether curve overlap exists. Subsequently, the system further confirms the degree of conflict by combining the symmetry characteristics of each response peak, that is, by calculating the left and right symmetry index of the response peak shape, including whether the peak position is offset, the difference in the left and right half-height-to-width ratio, and the velocity symmetry of the rising and falling segments of the response. If the system determines that a certain pair of pollutant responses... If the similarity between the responses is below a set threshold (e.g., less than 0.35), and the peaks of the two responses exhibit obvious symmetry and opposite directions in structure (e.g., one rises rapidly and falls slowly, while the other rises slowly and falls rapidly), then the response pair is considered to have a high degree of behavioral conflict. This means they cannot share the same response structure segment and require response deconstruction through a stripping operation. In this case, the system executes a conflict-driven approach to achieve response separation. The core of this method is to perform a time window transformation on the structurally conflicting peak segments. Specifically, this involves extracting overlapping response segments from the detection data and, based on the dominant behavioral trends of the two pollutants, separating the peak segments with higher high-frequency change rates. The signal shifting algorithm moves the entire signal to subsequent bands while maintaining its response amplitude and curve shape. Only its time calibration is adjusted to avoid overlapping segments with other pollutant responses, thus forming a new non-overlapping stable band at the time level. This stable band becomes the new response segment for the pollutant, achieving decoupling of the signal in the time dimension. Subsequently, the system reassembles the response sequences of each pollutant and performs intrinsic behavior verification to confirm whether the integrity and behavioral consistency of the stripped signal meet the typical response standards of the pollutant. If the reconstructed behavioral features after stripping are accurate, the system records the result of this round of conflict stripping and applies it to the pollutant identification process.

[0042] To improve the accuracy and precision of identifying and separating multiple pollutants in cases of overlapping response curves, a processing mechanism based on differences in response structural behavior characteristics was designed for conflict identification and tail displacement separation. During response analysis, when the system detects significant overlap of multiple pollutant response signals within the same detection time window, making it impossible to directly identify the behavior of each pollutant through peak separation or conventional fitting methods, the system first extracts the structural behavior of each pollutant's response curve and then activates the response conflict identification analysis module. This module combines the slope change direction and overall curvature change pattern of the response curve in the main rising and falling segments to identify the dynamic trend distribution of the response behavior. Simultaneously, it calculates the number and location of inflection points in the response curve. Inflection points are key nodes where the direction of change of the first derivative of the response curve reverses; their number and distribution characteristics reflect the pollutants contained in the response signal. The complexity of pollutants and the stratification of their internal behavior are considered. If the system identifies two pollutants with similar slope changes within the same response window, minimal difference in the number of inflection points, and consistent response morphology across multiple scales, it is judged as interference-type overlap. This is due to signal superposition caused by instantaneous sensor jitter, electrochemical co-response, or non-polluting factors. In such cases, traction stripping is not performed, but a filtering suppression strategy is adopted. However, if the response curves show different slope directions, one with a gradual rise and fall, and the other with a rapid rise and fall, and a significant difference in the number of inflection points, it indicates that the two responses have different kinetic processes and each possesses structural independence. This is judged as a true composite response, and the system initiates traction stripping. For the stripping process in such true composite responses, the system prioritizes identifying pollutants with longer response durations as dominant response factors and implements a tailed displacement strategy to achieve time separation between their responses and those of shorter-duration pollutants. The system extracts and translates the tail signal of the pollutant after the main peak response ends from the original response window to the tail of the current response sequence. The translation does not change the signal strength or waveform structure, but only changes its position on the time axis. This ensures that the long tail signal does not interfere with or overlap with the fast response signals of other instantaneous pollutants after the main response ends. At the same time, it provides a clear and interference-free structural interval for subsequent curve fitting and identification of short-time response pollutants.

[0043] To achieve accurate identification and priority separation of dominant pollutants in the response signals of complex pollutants, a dominant variable identification mechanism based on response energy contribution rate is proposed to guide the determination of signal decoupling order in cases of overlapping responses from multiple pollutants. Specifically, in complex response environments where multiple pollutants respond simultaneously in wastewater, the pollutants exhibit similar behavioral characteristics in terms of response amplitude, duration, and onset delay. Especially when the dominant variables of multiple pollutants are all reflected in the duration of the response, traditional separation methods based on the order of response amplitude or peak values ​​are difficult to effectively distinguish, leading to response misjudgment and decreased separation accuracy. Therefore, a response behavior dominance function is designed to quantitatively assess the degree of dominance of each pollutant on the overall response signal. This function is defined as follows:

[0044]

[0045] in:

[0046] Λ i This represents the dominance strength function value of the i-th pollution factor, used for dominance comparison among multiple pollution factors;

[0047] R i (t) represents the response signal amplitude of the i-th pollutant at time t, which represents its energy expression during the sensing process;

[0048] This represents the instantaneous rate of change of the response curve, used to reflect the rate of pollutant release or reaction;

[0049] κ i The response dynamic penalty factor is an empirical parameter (0.1–1) used to limit excessive dominance shift caused by drastic changes in response.

[0050] t i0 ,t i1 These are the start and end times of the pollutant's response curve, respectively, defining the stable response range of the pollutant.

[0051] θ i =t i1 -t i0 The duration of this response segment;

[0052] δ i This indicates the initial response delay time, which is the time when the first identifiable response to the pollutant occurs relative to the trigger point of the stimulus signal.

[0053] The core idea of ​​this function is that the higher the response energy of a pollutant, the more stable its release process, and the earlier its response, the stronger its dominance. Conversely, pollutants with delayed onset or excessively long response durations will have their dominance naturally weakened by the denominator term in the function structure, thus achieving fair evaluation in complex overlapping responses. During implementation, the system first extracts pollutant behavior segments from the composite response signal, and then extracts R based on the time window, response amplitude, and derivative behavior of each pollutant. i (t) and Then, the Λ of all pollutants is calculated according to the above formula. i The value is used to determine the dominant response contamination factor through the following comparison operation:

[0054] Λ j =max{Λ1,Λ2,…,Λ n}

[0055] If a certain pollutant j has Λ j If the value is the maximum, it is determined to be the dominant pollutant in the current composite response, and the system prioritizes the removal of its corresponding response segment; if the Λ value of multiple pollutants is the maximum, it is considered the dominant pollutant in the current composite response. i If the relative difference between the values ​​does not exceed 5%, and the dominant variable is the response duration, then the system further compares its initial response delay δ. i The size should be chosen preferentially for δ. i The smallest pollutant, i.e., the one with the earliest response, is selected for stripping to ensure that the stripping operation begins with the primary pollutant source, reducing the residual impact of subsequent pollutant responses. Once the dominant pollutant is identified, the system initiates a signal separation process to structurally decouple the response segment of that pollutant and reassess the Λ of the remaining pollutants after stripping. i The value is used to execute the next round of identification and processing until all contamination responses are separated.

[0056] To address the separation and identification of response signals from complex pollutants, a signal stripping mechanism with recursive self-correction capability was constructed to ensure the accuracy of the dominant pollutant stripping operation and the completeness of subsequent pollutant signal identification. Specifically, once the system determines a pollutant as the dominant pollutant in the current composite response structure based on the dominant variable discriminant function, the system prioritizes performing a stripping operation on its corresponding response segment and isolates and extracts its signal range from the overall response sequence. After completing the initial stripping, the system does not immediately proceed to the identification process of the next pollutant. Instead, it performs a self-correction analysis on the stripping results. That is, the system compares the waveform differences between the complete composite response curve before stripping and the residual response curve after stripping, analyzing whether there are abrupt changes or excessive shearing phenomena in the continuity of the signal slope, the rate of change of the derivative, and the degree of waveform connection near the response boundary. If a discontinuity point or derivative jump value is identified at the stripping boundary, it indicates that the initial stripping range failed to completely cover the dominant pollutant response or mistakenly cut into the boundary segment of other pollutants. Therefore, the system will initiate a recursive adjustment mechanism, perform a small expansion or rollback operation in the suspicious intervals on both sides of the stripping boundary, and update the stripping window in real time. At the same time, it will re-evaluate the stability index of the remaining response after stripping until the stripped segment and the residual segment reach the condition of continuous dynamic curve and no cross-disturbance in response behavior, at which point the stripping result correction is determined to be complete. Specifically, when the dominant variable is tail rebound behavior, i.e., the pollutant response shows a significant reverse upward wave after the main peak decreases, the system does not use a fixed termination point interception method when performing stripping. Instead, it identifies the starting point of the tail rebound zone and its slope change trend, and extends the sampling endpoint window to completely include the entire rebound structure in the stripping zone. This avoids the problem of response behavior breakage caused by missing tail information. The length of this delayed window is adaptively adjusted according to the duration and magnitude of the rebound wave, and is uniformly included in the intrinsic response file of the pollutant after the stripping is completed.

[0057] Example 1:

[0058] Combined with appendix Figure 4 In this embodiment, a wastewater online monitoring station in a chemical industrial park deploys a real-time online detection system for wastewater indicators to continuously monitor the presence of organic amines and heavy metal ions (such as Cr) in the mixed wastewater discharged from the plant area. 3+ The three main pollutant indicators are organic amines, volatile phenols, and acetic acid. Since the previous testing chamber had processed samples with high concentrations of organic amine contamination, and these substances easily form weakly polar residues through hydrogen bonding adsorption on the chamber walls, the "Residue Response Identification" module must be executed before the system enters the main testing process to determine if there are any potential interferences.

[0059] The system first retrieves the historical residual spectrum database of the detection chamber, which records the hysteresis release trajectory of the organic amine response curves over the past 60 days. This includes the time window for the typical residual response (T = 20–60 seconds), the peak decay rate (50% decrease time is 42 seconds), the maximum residual amplitude (approximately 0.18V), and the frequency of recurrence of this type of substance after the previous flush (13%). Subsequently, the system performs "initial response detection" on the currently injected trace amount of low-concentration pure water, detecting a signal shift of 0.14V at T = 22 seconds. This signal shift matches the typical waveform pattern of organic amine residues in the spectrum database, with a matching curve similarity of 92.3%. Based on this, the system calculates the predicted probability of this round of residue occurrence as 14.8%, exceeding the system's set residual interference judgment threshold of 10%. Therefore, the system determines that the chamber has a high residual risk and is not allowed to directly enter the wastewater detection process.

[0060] The system immediately initiates the pre-rinse module and enters the "material response sensing segment," employing a tension-sensitive interfacial energy sensing membrane (e.g., a polyvinylidene fluoride coated membrane) compatible with the cavity material to monitor changes in surface tension on the inner wall of the cavity. Within the first 30 seconds of rinsing, the system detects a slow recovery of the surface tension value from 31.5 mN / m to 38.2 mN / m (reference value 41.0 ± 0.5 mN / m). However, the recovery curve shows a plateau trend with no significant upward slope, indicating that some polar contaminants remain adsorbed and unremoved. The system extends the rinsing cycle to 90 seconds, at which point the tension data reaches 40.7 mN / m, and the tension recovery slope recovers to 0.12 mN / m·s. Based on this, the system determines that the interfacial energy has essentially recovered to the clean baseline state, and no residual adsorption behavior exists in the cavity; therefore, the rinsing process terminates.

[0061] Subsequently, the system began injecting the actual wastewater to be tested into the chamber and executed a slow-varying potential gradient and micro-pressure perturbation excitation process according to the set physical excitation strategy. Residual stripping calibration data from the previous pollutant response value was introduced here. When collecting the new round of response data, the system excluded the slow-rising curve segment within T = 20–60 seconds and corrected for background potential drift during this period, ensuring that the new round of response originated entirely from the pollutant release behavior of the current wastewater sample.

[0062] The results of the entire process show that without the residual spectrum identification and tension feedback flushing mechanism, the volatile organic amine response in this round of wastewater detection will be superimposed with the residual signal from the previous round, resulting in a shift in the response peak value of approximately 0.12V and an error of up to 18% in concentration estimation. However, after introducing the combined strategy of residual identification and tension monitoring, the response baseline recovery is accurate, the behavioral curve structure is clear, and the system's mean identification error for the three pollutants in the wastewater sample is less than ±3%, achieving closed-loop judgment throughout the entire process.

[0063] After completing the identification and cleaning of the detection chamber residues, the formal wastewater sampling and pollutant identification process begins. The sample to be tested comes from a newly established metal surface treatment company within the industrial park. The wastewater is suspected to contain various charged organic complexes and metal chelating agents, with complex release characteristics and a tendency for mutual shielding and tailing interference between different pollutants. To ensure complete release of pollutants, the system first initiates a slow-change physical stimulation process.

[0064] The entire process was controlled by a self-regulating stimulation manager. The system read the response hysteresis data of the main pollutants in the previous round of homologous process samples, identifying that chelated heavy metals (such as ethylenediamine-chelated chromium) exhibited a 17-second initial response delay and drastic fluctuations in release hysteresis under potential excitation. Based on this, the stimulation manager set the initial sweep rate of the potential gradient to 0.1V / s, lower than the default value of 0.3V / s, and simultaneously adjusted the sequence to a combination strategy of "micro-heating → low potential → voltage stabilization perturbation" to avoid short-term suppression caused by strong excitation. Furthermore, during the detection process, it was found that the pollutant response was still not significantly released. The system identified its stimulation resistance behavior and switched to a high-frequency, low-amplitude perturbation mode, initiating a pulsed 0.05MPa pressure wave train every 2 seconds to apply rhythmic perturbation to the inner wall of the chamber and the solution interface. Within 10 seconds, the pollutant release was successfully activated, and the response peak rapidly rose to 0.31V, indicating successful excitation.

[0065] To improve the accuracy of pre-excitation prediction, the system performed a joint mapping analysis of water charge distribution and viscous resistance before the onset of potential stimulation. Calculations based on sampled conductivity and temperature data showed that the viscous resistance region was mainly concentrated in the lower right corner of the cavity's posterior wall (20 cm). 2 In areas with high charge density and partial polar entanglement, the system deploys a local pre-electrode at these locations and performs a 5-second local electro-induced untangling process. This operation rapidly untangles and redistributes the charge coupler, reducing interference with the main potential excitation path and improving overall response release efficiency.

[0066] During the sampling response, the system activates a fractal recognition mechanism to perform geometric complexity analysis on the real-time curves of all detected signals. It detects that the fractal dimension of a certain pollutant's response suddenly decreases and stabilizes at T=12 seconds, indicating that its kinetic inflection point has occurred. Subsequently, the first response segment (T=13–19 seconds) is stable and without abrupt changes, which the system marks as the stable response initiation segment. Notably, another pollutant's response exhibits a typical "overreaction + inhibition" characteristic within T=5–10 seconds, i.e., the curve first rises to 0.45V and then rapidly falls and oscillates, with dramatic fluctuations in the fractal dimension. The system classifies this segment as a behavioral masking zone and excludes it from sampling analysis, retaining only the subsequent stable segment (from T=11 seconds) for analysis to avoid misleading concentration judgments due to overreaction.

[0067] Because the wastewater contained two easily co-responsive pollutants, the system performed a composite pollutant response stripping process. The response conflict identification module first identified that the responses of the two pollutants completely overlapped between T = 20–35 seconds, but their curve trends were significantly different: pollutant A exhibited a steady rise and tailing pattern, while pollutant B showed a rapid rise and fall pattern. Their rise rates, symmetry directions, and other indicators were highly conflicting, leading the system to determine a high degree of behavioral conflict. Based on the conflict-driven approach, the system redirected the high-frequency band of pollutant B along the time axis to T = 36–45 seconds, avoiding its main response zone with pollutant A, thus completing signal decoupling after the behavioral stabilization zone.

[0068] To ensure the rationality of the stripping sequence, the system identified the dominant behavioral variables for pollutants A and B. The dominant characteristic of pollutant A was its duration (approximately 16 seconds), while that of pollutant B was its initial response rate peak. Because pollutant B's initial response was earlier and its slope steeper, it was identified as the dominant pollutant. The system prioritized stripping the segment containing pollutant B. After stripping, recursive waveform correction was performed again, and the remaining curve of pollutant A was fine-tuned at the boundaries. It was found that a response tail still remained at T=38 seconds. Therefore, the system extended the end point of the stripping region to T=40 seconds to ensure a complete response.

[0069] The final results show that, after structural decoupling, the system identified pollutant A concentration as 1.12 mg / L and pollutant B concentration as 0.39 mg / L, with error rates controlled within ±4%. Compared with the error of traditional non-stripping detection (with a maximum deviation of 17%), the accuracy is significantly improved.

[0070] Example 2:

[0071] Combined with appendix Figure 5 A large city wastewater treatment plant, after connecting to a mixed industrial wastewater branch line, implemented a real-time online monitoring method for wastewater indicators to identify potential pollutant aggregation behaviors in wastewater from complex sources. This branch line's wastewater originates from two types of enterprises: a pharmaceutical factory, whose wastewater contains various aromatic amine intermediates and typically exhibits a rapid rise and fall in response characteristics; and an electronic waste recycling plant, whose wastewater is rich in heavy metal complex ions, exhibiting a slow rise and long tail in response. During one monitoring cycle, the system identified a clear bimodal fusion of peaks in the response curve within the time interval T = 18–32 seconds during the signal acquisition phase. However, the edges of the two peaks were blurred, making it difficult to determine their origin. Therefore, the system activated a response inconsistency identification strategy.

[0072] First, the system extracts the response curves of the two suspected pollutants from the total signal and normalizes them separately. After normalization, pollutant A (initially identified as an aromatic amine) exhibits a highly symmetrical, rapid symmetrical peak (maximum value at T=22 seconds, with left and right half-peak widths of 2.1 seconds and 2.3 seconds, respectively), while pollutant B (initially identified as complexed lead ions) responds slowly, with an asymmetrical waveform and significant tailing (maximum value at T=28 seconds, left half-peak width of 4.2 seconds, extending to T=34 seconds on the right). The system calculates the cosine similarity of the normalized response curves, resulting in 0.27, far below the set threshold of 0.5. Simultaneously, it detects that the two response peaks have opposite symmetry directions, i.e., A is a positive symmetrical peak, and B is a negatively shifted tailing peak. Based on this, the system determines that the two exhibit highly conflicting behaviors and are unsuitable for separation using linear decomposition methods; a conflict-driven decoupling method is required.

[0073] Subsequently, the system performs a time window transformation operation on the composite response signal within this segment. Specifically, it extracts the superimposed response curves within T = 18–32 seconds and identifies the high-frequency variation segment of pollutant A (the maximum value of the first derivative of the signal reaches 0.41 V / s within T = 20–24 seconds). The system confirms through behavioral trend matching that this segment belongs to aromatic amine pollutants and thus uses it as the target. The system completely shifts this segment to the rear of the detection window, T = 40–44 seconds, leaving a gap on the time axis to avoid its main tail region with pollutant B (T = 25–34 seconds). During this operation, the curve amplitude and structure remain unchanged; only the time calibration is altered to ensure the repositioned response remains identifiable. After repositioning, the response of pollutant A in the new time segment exhibits an independent peak shape and does not overlap with the signal of B, indicating that the system has successfully completed behavioral decoupling.

[0074] To verify the effectiveness of this operation, the system calculated the independent response integral area of ​​pollutants A and B separately. The independent response integral area of ​​pollutant A was 0.84 mV·s, corresponding to a concentration of 1.07 mg / L; the independent response integral area of ​​pollutant B was 1.53 mV·s, corresponding to a concentration of 2.44 mg / L. The comparison errors with the standard sample were 4.3% and 3.1%, respectively, which are far lower than the deviations in the fused state without stripping treatment (18.6% and 21.2%, respectively). Furthermore, the traction treatment did not destroy the main response structure; both curves retained their original dynamic characteristics after reconstruction, consistent with the historical response records of the pollutants.

[0075] After completing the initial stripping of aromatic amines and heavy metal complex pollutants, the system began processing the next round of composite response identification. In subsequent detection cycles, the system identified a new set of composite response peaks in the T = 50–65 second interval, initially identified as originating from phenol derivatives (such as nitrophenol) and high-viscosity alkyl sulfones. These two substances partially overlapped during this time period, particularly in the T = 52–58 second interval, where the system detected a composite curve that appeared to be approximately a fused double peak. However, the amplitude changes were not drastic, making it difficult to distinguish whether this was a genuine composite response or merely a interference-type overlap caused by a short-term sensor disturbance.

[0076] At this point, the system invokes the response conflict structure matching analysis module. First, it extracts the slope changes and inflection point distribution of the two peaks within the response segment. The phenol derivative exhibits a typical rapid rise and fall trend in this segment, with a maximum rise slope of 0.52 V / s and a peak value at T = 53.8 seconds. There are a total of two inflection points, and the peak shape shows high symmetry. In contrast, the alkyl sulfone derivative responds more slowly, with a gentler slope (maximum value only 0.19 V / s), a peak value at T = 58.5 seconds, a smooth response with a significant tail, and as many as five inflection points, which are asymmetrically distributed, mainly concentrated in the falling segment. Comparing the two response curves reveals a significant deviation in their slope direction changes, with a 2.7-fold difference in the rise speed and a difference of more than three inflection points, indicating significant differences in structural characteristics. Based on the aforementioned behavioral contradiction identification rules, the system determines that this segment is a true composite response and requires a traction stripping operation.

[0077] Considering that the response duration of alkyl sulfone pollutants is much longer than that of phenols (total duration approximately 14 seconds vs. 6 seconds), the system identifies them as long-behavior pollutants and further employs a tailed shift operation strategy to prevent their slow decline from interfering with the rapid response analysis of phenols. Specifically, the system identifies the main peak termination point of the alkyl sulfone response at T=60 seconds, after which a continuous slow decline signal between 0.08 and 0.12 V exists in the range of T=65 seconds. The system extracts the entire T=60–65 second segment and re-times it, shifting it to the end of the T=70–75 second segment, and retains the integrity label of the original response in the processing results for source tracing.

[0078] After the shift treatment, the phenol response peak was completely released within T = 52–56 seconds, with a response area of ​​0.66 mV·s and a corresponding concentration of 0.91 mg / L. The integrated area of ​​the shifted alkyl sulfone response was 1.78 mV·s, with a measured concentration of 2.12 mg / L. To verify the separation accuracy, the system simultaneously collected samples for laboratory chromatographic analysis. The comparison values ​​were 0.93 mg / L for phenol and 2.05 mg / L for alkyl sulfone, with errors controlled within ±4%. However, the concentration misjudgment caused by merging responses without separation reached over ±20%. Further analysis showed that if the tailed shift was not performed, the tailing segment of the alkyl sulfone would overlap with the response in the next cycle, severely interfering with the system's recognition rhythm of subsequent pollutants.

[0079] After undergoing response contradiction identification, traction stripping, and tail displacement steps, the system enters the next stage of behavior-dominant variable identification to address the complex situation where pollutant responses are highly similar and their waveforms are difficult to distinguish. In this round of detection, the system identified two pollutants (labeled C1 and C2) with severely overlapping responses within the time interval T = 78–94 seconds. They were initially identified as a type of thioester compound and a highly polar chlorinated aromatic hydrocarbon, both exhibiting characteristics of moderate amplitude, long-duration response, and slow release. Conventional methods are insufficient to accurately distinguish their dominant signal features. Therefore, to improve stripping accuracy, the system invokes the behavior-dominant intensity function Λ. i To conduct quantitative comparisons.

[0080] Step 1: Define parameters and extract response segments

[0081] The system first identifies the response ranges for two pollutants:

[0082] Pollutant C1 (thioester): t 10 =78s, 11=92s, θ1=14s, δ1=3.2s;

[0083] Pollutant C2 (chlorinated aromatic hydrocarbons): t 20 =79s, 21=94s, θ2=15s, δ2=2.5s;

[0084] With a sampling frequency of 10Hz, the system obtained the response curves R1(t) and R2(t) for two pollutants, and then fitted them with their first derivatives to obtain... We set κ1 = 0.5 and κ2 = 0.6 as the motivation penalty factors.

[0085] Step 2: Calculate the dominant function value

[0086] According to the formula of the invention method:

[0087]

[0088] Substitute them into the calculations respectively:

[0089] For pollutant C1:

[0090]

[0091] In the actual system test, the integral value was:

[0092]

[0093] therefore,

[0094]

[0095] For pollutant C2:

[0096]

[0097] The system measured:

[0098]

[0099] therefore,

[0100]

[0101] Step 3: Determination and Separation of Lead Pollutants

[0102] The system compares the dominant intensity function values ​​of the two:

[0103] Λ1 = 0.479

[0104] Λ2 = 0.464

[0105] The difference between the two is only about 3.2%, which is less than the system's set threshold of 5%. The system further compares the initial response delays of the two:

[0106] δ1 = 3.2s

[0107] δ2 = 2.5s

[0108] Therefore, although C1 has a slight dominant intensity advantage, due to its large response delay, the system determines that C2 is the first responding pollutant and preferentially strips its signal segment (T = 79–94 seconds), and then performs interval fitting and reconstruction of C1.

[0109] Step 4: Result Verification

[0110] After stripping, the system outputs the pollutant concentration values ​​respectively:

[0111] C2 (chloroaromatic hydrocarbons): 1.02 mg / L (compared to standard value 1.04 mg / L, error 1.9%)

[0112] C1 (thioester): 0.86 mg / L (compared to standard value 0.83 mg / L, error 3.6%)

[0113] If the dominant strength function comparison and delayed decision are not performed, C1 will be stripped in the traditional order, which will lead to the incorrect identification of the tail of C2, with an error exceeding 12%.

[0114] After identifying and preferentially stripping the dominant behavior of pollutant C2 (chloroaromatic hydrocarbons), the system enters the next stage of a recursive self-calibration process to address the uncertainty of the signal boundary and potential rebound interference after the dominant pollutant is stripped. The goal of this round of detection is to further accurately strip the response signal of the aforementioned pollutant C1 (thioester) residue. However, the system identified atypical tailing fluctuations in its response tail, i.e., tail rebound behavior. If this behavior is not fully incorporated into the response region, it can easily lead to an underestimation of the pollutant's integrated response area, thus affecting concentration determination.

[0115] The system first removed the stripping zone of pollutant C2 (T = 79–94 seconds) from the total response and then performed baseline alignment and difference curve extraction on the remaining response. Analysis of the difference curves before and after stripping revealed a secondary gradual rise in the original response curve within the T = 94–97 second region, with an amplitude of 0.05–0.08V. Initially, this appeared to be system noise, but historical data verification confirmed that this segment was consistent with the interfacial re-adsorption-desorption behavior of typical thioester pollutants after release. The system thus labeled this as a tail bounce. If the originally predetermined T = 92 seconds was used as the termination point for C1, this information was mistakenly removed, resulting in a true response area of ​​only 0.73 mV·s, corresponding to a concentration of 0.74 mg / L, approximately 14% lower than the laboratory control value (0.86 mg / L).

[0116] The system immediately initiates a recursive self-correction mechanism, analyzes the remaining response range of the dominant pollutant C1, and completes boundary dynamic optimization through the following three operations:

[0117] 1. Comparison of response decay rates: The first derivative rate of change of the response was calculated for the two segments T = 90–92 seconds and T = 94–96 seconds. It was found that although the value of the latter segment was smaller (-0.015V / s), it was still a non-zero slope, indicating that the response was effective.

[0118] 2. Response curvature continuity check: The system uses a sliding window to fit the second derivative of the curve, confirming that the curvature change of the rebound segment is stable after T=94 seconds, without sudden jumps, indicating that it is not a random disturbance;

[0119] 3. Integral analysis of energy contribution: The integral contribution of the rebound segment (T = 94–97 seconds) to the total energy is calculated to be 0.11 mV·s, accounting for 13.1% of the total response area of ​​C1. This is far beyond the boundary significance threshold (5%) set by the system and should be retained.

[0120] Accordingly, the system extended the sampling endpoint window from the original T=92 seconds to T=97 seconds, and included the T=94–97 second range within the response range of the dominant pollutant C1. After recalculating the response integral, the corrected concentration value of C1 was 0.86 mg / L, which was completely consistent with the laboratory control, with the error controlled within ±1%. In addition, the system outputs a comparison of the significance of the signal difference map before and after correction to record the effect of this round of boundary adjustment, and to provide prior reference for subsequent detection of similar pollutants, gradually optimizing the algorithm's adaptive capability.

[0121] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for real-time online detection of wastewater indicators, characterized in that... include: Before each formal test, a low concentration of water is injected into the test chamber to perform micro-dose detection. By observing whether there is a nonlinear shift in the initial response behavior, it is determined whether there is a response residue on the inner wall of the test chamber. If so, a local delayed detection and pre-rinsing operation is performed to suppress the interference response caused by the chemical memory of the test chamber. After completing the pre-response inhibition, a slow-change physical stimulation process is applied to the wastewater to be tested, including slow temperature rise, potential gradient sweep or pressure disturbance, to stimulate the release of masked pollutants in the wastewater. After the pollutants are released, the pollutant response signal is continuously tracked to identify fluctuation behavior, and the stable response range is identified based on the changing trend of the response curve. The stable response range is then used as the sampling point for final detection. For situations involving superimposed responses from multiple pollutants, the physical differences in multiple response signals are identified by analyzing the rise rate, fall rate, and tailing characteristics of the response profiles of each indicator. The conflict traction method is then used to decouple and separate the overlapping response signals. The conflict traction method involves applying a time window transformation operation to the overlapping peak segments in the response signals, moving the high-frequency variation segments to subsequent bands to form a stable behavioral band for response decoupling.

2. The method for real-time online detection of wastewater indicators according to claim 1, characterized in that... In the process of judging the response residue of the cavity, the response residue trajectory formed by different pollutants is matched based on the cavity's historical self-residue spectrum, and the future residue probability is predicted based on the frequency and amplitude trend. The pre-rinsing operation includes a material response sensing section, which monitors the change of surface tension of the cavity material during rinsing. It judges whether the cavity has returned to the chemical baseline state by the interfacial energy recovery behavior. If it has not recovered, rinsing continues until the surface behavior is restored.

3. The method for real-time online detection of wastewater indicators according to claim 2, characterized in that... The slow-varying physical stimulation process is controlled by a self-regulating stimulation manager. The manager adjusts the stimulation intensity and sequence order according to the hysteresis factor of the previous response of the pollutant, and switches to a high-frequency, low-amplitude perturbation strategy when the pollutant shows stimulation resistance. Before applying the potential gradient sweep, the mapping relationship between the water charge distribution and viscous resistance is pre-analyzed to predict the ion migration trajectory, and local pre-untangling electrostimulation is performed on areas where pollutants are easily entangled or electrohysteretic.

4. The method for real-time online detection of wastewater indicators according to claim 3, characterized in that it introduces... The fractal recognition mechanism for response behavior analyzes the changes in geometric complexity of the response curve in real time to identify the dynamic inflection point between the release and stable phases of pollutants, and takes the first stable behavior region after the inflection point as the starting segment of the stable response. If an overexcitation + suppression band appears in the stable response region at the initial stage of excitation, the region will be marked by the system as a behavior masking region and excluded from sampling.

5. The method for real-time online detection of wastewater indicators according to claim 4, characterized in that... The response contradiction identification strategy is introduced in the response stripping process of the composite pollutant. When there is a high degree of behavioral conflict in the responses of two pollutants, the conflict traction method is used to split the signals of the two pollutants into the behavioral stability zone and then decouple them. Before response stripping, the dominant behavioral variables of the pollutant response are identified, with the dominant behavior characterized by the initial rate, duration or tail rebound. Pollutants with dominant behavior are stripped first.

6. The method for real-time online detection of wastewater indicators according to claim 5, characterized in that... In the aforementioned response conflict identification strategy, a normalized similarity analysis is performed on the response curve of each pollutant, and the degree of response conflict is judged in combination with the peak symmetry. If the similarity is less than the set threshold and the symmetry direction is opposite, it is identified as a high degree of behavioral conflict.

7. The method for real-time online detection of wastewater indicators according to claim 6, characterized in that... When identifying the response conflict, structural conflict matching analysis is performed by combining the differences in the direction of response slope change and the number of inflection points to distinguish between interference-type overlap and true composite response, and only the latter is subjected to traction stripping; the traction stripping process performs tail displacement on pollutants with long response duration, actively moving the trailing segment to the end of the response to facilitate the distinction of instantaneous pollutant signals.

8. The method for real-time online detection of wastewater indicators according to claim 7, characterized in that... In the identification of dominant behavioral variables, the contribution rate of the response energy of each pollutant is calculated, and the one with the highest contribution rate is determined as the dominant response pollutant factor, and the signal segment corresponding to the factor is preferentially stripped. During the response stripping process, if the dominant variable of both pollutants is detected to be duration, the initial response delay is compared, and the one with an earlier response is stripped.

9. A method for real-time online detection of wastewater indicators according to claim 8, characterized in that... After the dominant behavior pollutant is stripped, a recursive self-correction process is performed on the remaining response signal, and the stripping boundary is dynamically adjusted according to the difference in signal waveform before and after the dominant behavior pollutant is stripped. If the dominant variable is tail bounce behavior, sample the endpoint window after removing the time delay to ensure that the bounce segment information is fully included in the dominant response extraction area.

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