Sewage toxicity joint effect prediction and discharge traceability decision management system

Abstract

The invention relates to the technical field of sewage management, and discloses a sewage toxicity joint effect prediction and discharge traceability decision management system, which comprises a data acquisition module, a logic mapping module, a residual decision module, an active verification module and a decision instruction generation module, collecting a terminal comprehensive biological inhibition rate sequence and an upstream working condition feature vector, and establishing a logic time delay transfer model from each emission source to the terminal; calculating a dynamic residual error of the actually measured inhibition ratio relative to the sum of the independent contribution expectation values of the emission sources, and distributing a collaborative toxicity contribution weight according to a working condition activeness weight; and when the weight is in an uncertain interval, a pseudo-random flow perturbation instruction is issued, and the collaborative toxicity contribution weight is corrected by using the correlation between the terminal response waveform and the instruction. The synergistic effect is quantified into a marginal contribution weight with exclusive causal evidence efficacy, and an anti-distinguishing blind area in administrative responsibility determination is eliminated.

Description

Technical Field

[0001] This invention relates to a decision management system for predicting the combined effects of wastewater toxicity and tracing the source of emissions, belonging to the field of wastewater management technology. Background Technology

[0002] Current wastewater prediction and source tracing management systems largely rely on threshold judgment logic for single pollutants. They monitor chemical oxygen demand (COD), ammonia nitrogen, or heavy metal characteristics at controlled nodes in the drainage network to achieve administrative supervision and predictive data processing. The management approach assumes a linear correlation between the pollutant's responsibility and emission concentration, generating administrative decision-making instructions by comparing measured concentrations with preset emission thresholds. However, in complex drainage networks, low-concentration pollutants often exhibit nonlinear toxicity enhancement effects. The measured comprehensive biological inhibition rate at the terminal deviates from the sum of individual component toxicity contributions. Existing management logic and prediction models cannot quantify the marginal contribution of a single emission source in the combined effect from massive amounts of nonlinear data. This leads to ambiguity in the basis for determining responsibility in administrative management. Furthermore, the complex physical topology of the pipe network and the nonlinear time lag in wastewater flow, with phase drift caused by level fluctuations and pump station scheduling, result in temporal deviations between the terminal toxicity peak and the upstream emission entity. Controlled entities engage in adversarial games through falsifying monitoring data, falsely declaring emission reduction conditions, or illegally connecting underground pipes, making the conclusions of water quality concentration-based algorithms face the technical bottleneck of insufficient evidence exclusivity.

[0003] At the source tracing and verification level, the industry has attempted to introduce biological monitoring methods, but the analytical logic has not escaped the limitations of passive response. For example, Chinese invention patent CN116559247B discloses a method and system for early warning and source tracing of biotoxicity in industrial park drainage systems. This technology uses the slope of microbial electrode current for initial screening and combines characteristic peak comparison for source tracing. The core logic is limited to static matching of specific response waveforms. When there are intertwined and synergistic effects of multiple pollutants in the pipe network, the solution is difficult to quantify the marginal contribution weight of each entity to the total toxicity increment at the management level, leading to difficulties in determining liability in administrative law enforcement. The current system is inadequate. The complex physical topology of the pipeline network and the nonlinear time lag of sewage flow, which causes phase drift due to liquid level fluctuations, make it difficult for fixed scanning and comparison mechanisms to eliminate such causal mapping failures. The system is mostly in a passive receiving mode. In the face of adversarial game behaviors such as controlled entities tampering with operating data or using uncontrolled sources to interfere, there is a lack of an active verification mechanism with exclusive causal evidence. There is a logical break between the predicted conclusions and the evidence for administrative responsibility determination, which creates the risk of administrative defense. It is necessary to establish a supervisory decision-making management system that can sense the incremental joint effect, dynamically calibrate logical delays, and has the ability to identify adversarial games.

[0004] Therefore, the technical problem to be solved by this invention is how to establish a decision-making and management system that can perceive the incremental joint effect, dynamically calibrate logical delays, and have the ability to identify adversarial games, and generate administrative orders with legally closed-loop evidentiary effect. Summary of the Invention

[0005] To address the problems mentioned in the background art, the technical solution of the present invention is as follows: A wastewater toxicity joint effect prediction and emission source tracing decision management system, comprising:

[0006] The data acquisition module is used to synchronously acquire the time series sequence of the comprehensive biological inhibition rate of the drainage network terminal and the real-time operating condition feature vector of each controlled emission source upstream of the drainage network.

[0007] The logic mapping module is used to establish a logical time delay propagation model from each controlled emission source to the terminal based on the physical topology of the drainage network.

[0008] The residual decision module is used to calculate the difference between the sum of the expected values ​​of the independent toxicity contributions of each controlled emission source and the time series sequence of the comprehensive biological inhibition rate based on the logical delay propagation model to obtain the dynamic residual. Based on the operating activity weight of each controlled emission source, the dynamic residual is allocated as the synergistic toxicity contribution weight of each controlled emission source.

[0009] The active verification module operates as follows: when any co-toxicity contribution weight is within a preset evidence uncertainty range, a flow micro-perturbation instruction containing pseudo-random time-series characteristics is generated and sent to the flow control actuator of the controlled emission source to drive the controlled emission source to generate physical flow fluctuations.

[0010] The residual decision module is also used to extract feature waveform sequences from the time series of the comprehensive biological inhibition rate within the response time window defined by the logical delay propagation model, and to adjust the gain of the synergistic toxicity contribution weight based on the mutual information correlation evaluation results between the feature waveform sequences and the flow micro-perturbation command.

[0011] The decision instruction generation module is used to generate emission source tracing conclusions, including the determination of exclusive liability, and control instructions for specific controlled emission sources, based on the revised co-toxicity contribution weights.

[0012] Preferably, the decision instruction generation module is further used to associate the modified co-toxicity contribution weight with an administrative enforcement document template with a unique timestamp; the active verification module, when generating the flow micro-disturbance instruction, determines the flow micro-disturbance adjustment amplitude based on the historical emission flow distribution of the controlled emission source, so that the intensity of the biological inhibition rate fluctuation generated by the flow micro-disturbance is greater than the background noise of the terminal, and the instantaneous emission flow corresponding to the flow micro-disturbance is within the preset compliance threshold range; the residual decision module determines the temporal correlation between the two by calculating the maximum mutual information between the characteristic waveform sequence and the flow micro-disturbance instruction.

[0013] Preferably, the residual decision module executes the following quantization rules when calculating the dynamic residual: ,in, For dynamic residuals, The values ​​are the real-time measured values ​​at the corresponding sampling times in the comprehensive biological inhibition rate time series. The number of controlled emission sources, For the first Expected independent toxicity contribution of each controlled emission source. The contribution correction factor is determined by performing nonlinear correction through an association mapping model based on the discharge intensity fluctuation frequency and material compatibility coefficient contained in the real-time operating condition feature vector.

[0014] Preferably, the data acquisition module is used to acquire the operating energy consumption characteristic sequence of wastewater treatment facilities in each controlled emission source; the residual decision module is also used to establish a correlation mapping model between the operating energy consumption characteristic sequence and the co-toxicity contribution weight; based on the correlation mapping model, the numerical deviation between the fluctuation pattern of the operating energy consumption characteristic sequence and the co-toxicity contribution weight is calculated; when the numerical deviation exceeds the preset deviation threshold, it is determined that the controlled emission source has data tampering behavior, and the weight re-evaluation procedure is triggered to reduce the confidence level of the source tracing conclusion of the corresponding controlled emission source.

[0015] Preferably, the logic mapping module is used to extract the physicochemical index waveform with random fluctuation characteristics from the real-time operating condition feature vector and use it as the physicochemical feature reference signal; calculate the dynamic transfer entropy between the corresponding physicochemical index waveform and the physicochemical feature reference signal obtained by the terminal to determine the real-time transmission delay of the drainage network; and update the time offset parameter in the logic delay transmission model according to the real-time transmission delay to calibrate the temporal phase relationship of the collaborative toxicity contribution weight.

[0016] Preferably, the residual decision module is used to perform spatiotemporal correlation regression analysis on the dynamic residual; when the mutual information between the dynamic residual and the real-time operating condition feature vectors of all controlled emission sources is lower than the preset correlation threshold, the dynamic residual is defined as the uncontrolled source interference residual; the uncontrolled source interference residual is separated from the collaborative toxicity contribution weight of each controlled emission source, and an administrative verification instruction pointing to a specific pipeline physical area is generated by back-calculating based on the logical time delay propagation model.

[0017] Preferably, the data acquisition module, while acquiring the real-time operating condition feature vector, simultaneously extracts the local water level fluctuation features caused by the discharge behavior; the logic mapping module uses the local water level fluctuation features as the water level pulsation fingerprint, retrieves the response period with waveform consistency in the comprehensive biological inhibition rate time series, and calculates the time difference between the water level pulsation fingerprint and the response period to correct the timing alignment accuracy of the logic delay propagation model.

[0018] Preferably, the decision instruction generation module includes an evidence collection and storage unit, which encapsulates the generation record of the traffic micro-disturbance instruction, the characteristic waveform readback sequence of the terminal, and the verification conclusion of the residual decision module into an electronic regulatory evidence file with unique timestamp and digital signature verification attributes.

[0019] Preferably, the residual decision module performs nonlinear correction on the allocation ratio of dynamic residuals based on the fluctuation frequency of the emission intensity of the controlled emission source in the current sampling period and the material compatibility coefficient between emission components when calculating the operating condition activity weight.

[0020] Compared with the prior art, the beneficial effects of the present invention are:

[0021] 1. In the prediction and emission of the combined effects of wastewater toxicity, the synergistic effect arbitration module calculates the dynamic residual of the measured toxicity inhibition rate at the terminal of the drainage network relative to the sum of the expected independent contributions of the controlled emission sources. It then assigns the incremental weight of the combined effect by combining the characteristic activity entropy of the controlled emission sources, transforming the nonlinear toxicity enhancement risk into a quantifiable marginal contribution weight. This addresses the problem of ambiguous responsibility attribution caused by the synergistic effects of pollutants in the supervision of multi-source emissions, and provides a basis for determining responsibility when issuing administrative emission reduction orders.

[0022] 2. The decision command generation module issues flow micro-disturbance commands with specific pseudo-random sequence characteristics to the controlled emission points. The situation acquisition module extracts the same-frequency resonance response component from the terminal toxicity time series within the logical delay window, establishes a closed-loop verification mechanism based on command impulse response, and uses the existing flow control execution mechanism to logically color the physical emission entities, so that the source tracing conclusion is transformed from a correlational speculation into an exclusive conclusion, eliminating the risk of defense caused by the non-closed chain of evidence in environmental administrative law enforcement.

[0023] 3. The situation acquisition module synchronously acquires the energy consumption power fingerprint of sewage treatment facilities, establishes a symmetric mapping model with the toxicity contribution weight obtained by decoupling, identifies the logical offset between the fluctuation pattern of power consumption and the emission contribution weight, penetrates the adversarial game behavior of malicious tampering of monitoring data or false declaration of emission reduction conditions, and combines residual autoregressive analysis to remove ghost residual components that are uncorrelated with the characteristics of controlled source conditions, identify illegal emission behavior in uncontrolled areas, avoid compliant emission entities from bearing unrelated collaborative responsibilities, and improve the fairness of administrative supervision decision-making and management resilience. Attached Figure Description

[0024] Fig. 1 This is a schematic diagram of the closed-loop process of logic control and active verification in this invention;

[0025] Fig. 2 This is a numerical comparison chart of the mutual information content and energy consumption characteristics of the micro-perturbation command response of the present invention;

[0026] Fig. 3 This is a schematic diagram of the overall hardware deployment architecture and data interaction topology of the present invention. Detailed Implementation

[0027] This section is intended to describe the present invention in detail. The following embodiments are used to explain and illustrate the present invention, and are not intended to limit the scope of protection of the present invention.

[0028] This invention provides a wastewater toxicity co-effect prediction and emission source tracing decision management system, including a data acquisition module, a logic mapping module, a residual decision module, an active verification module, and a decision instruction generation module. The data acquisition module synchronously acquires the time series sequence of the comprehensive bioinhibition rate at the drainage network terminal and the real-time operating condition feature vectors of each controlled emission source upstream of the drainage network. The logic mapping module establishes a logical time delay propagation model from each controlled emission source to the terminal based on the physical topology of the drainage network. The residual decision module calculates the difference between the time series sequence of the comprehensive bioinhibition rate and the sum of the expected values ​​of the independent toxicity contributions of each controlled emission source according to the logical time delay propagation model to obtain the dynamic residual. Based on the operating condition activity weights of each controlled emission source, the dynamic residual is allocated as the co-toxicity contribution weights of each controlled emission source. The operating condition activity weights are obtained by performing nonlinear correction on the discharge intensity fluctuation frequency and material compatibility coefficient of the controlled emission source within the current sampling period, and the dynamic residuals are numerically allocated according to the corrected weight ratios. The active verification module operates by generating a package when any co-toxicity contribution weight falls within a preset evidence uncertainty range. A flow micro-disturbance command containing pseudo-random temporal characteristics is sent to the flow control actuator of the controlled emission source. In terms of execution details, this command directly drives the variable frequency pump or proportional-integral regulating valve of the electromagnetic flowmeter actuator at the controlled point through the PLC interface to generate a pseudo-random square wave fluctuation with an amplitude of 10% to 15% of the reference flow. This fluctuation is controlled by a system-preset M-sequence generator to ensure that its spectral characteristics are decoupled from the natural pump station scheduling and gravity flow fluctuations of the pipeline network in the frequency domain, thereby imprinting a unique physical coloring mark on the physical water mass to drive the physical flow to generate waves. Within the response time window defined by the logical delay propagation model, the residual decision module extracts characteristic waveform sequences from the comprehensive biological inhibition rate time series, calculates the maximum mutual information between the characteristic waveform sequences and the flow micro-disturbance command, and adjusts the gain of the synergistic toxicity contribution weight based on the mutual information correlation evaluation results. The decision command generation module generates emission source tracing conclusions including liability determination and control commands for specific controlled emission sources based on the adjusted synergistic toxicity contribution weight, thereby transforming the joint effect risk into a quantifiable basis to support administrative emission reduction.

[0029] To address the technical challenge of nonlinear velocity shifts caused by liquid level fluctuations within drainage networks, leading to phase deviations in source tracing analysis, the logic mapping module extracts physicochemical index waveforms with random fluctuation characteristics from real-time operating condition feature vectors as physicochemical feature reference signals. It then calculates the dynamic transfer entropy between the physicochemical index waveforms acquired by the terminal and the physicochemical feature reference signals to determine the real-time transmission delay of the drainage network. This information is used to update the time offset parameters in the logic delay transfer model. This system calibrates the temporal phase relationship of the synergistic toxicity contribution weights. When drainage operations cause local water level fluctuations, the data acquisition module synchronously extracts the local water level fluctuation features as a water level pulsation fingerprint. The logic mapping module retrieves response periods with waveform consistency from the comprehensive biological inhibition rate time series and calculates the time difference between the water level pulsation fingerprint and the response period to correct the temporal alignment accuracy of the logic delay propagation model. Considering the common nonlinear toxicity enhancement effect among various low-concentration pollutants in industrial parks, the management logic needs to quantify the marginal contribution of individual emission sources in the combined effect. The residual decision module calculates the dynamic residual... At that time, the quantification rules are as follows: ,in, For dynamic residuals, The values ​​are the real-time measured values ​​at the corresponding sampling times in the comprehensive biological inhibition rate time series. The number of controlled emission sources, For the first Expected independent toxicity contribution of each controlled emission source. The contribution correction factor is determined by performing nonlinear correction through an association mapping model based on the emission intensity fluctuation frequency and material compatibility coefficient contained in the real-time operating condition feature vector. Furthermore, the logic for obtaining this material compatibility coefficient is as follows: the system pre-stores an industrial characteristic pollutant reactivity matrix. By performing a real-time topological comparison between the chemical declaration list of upstream controlled emission sources and the molecular polarity characterized by online conductivity and UV254 spectral fingerprints, when two or more pollutants exhibit acid-base neutralization, redox, or complexation potential, the system maps them to a nonlinear scalar value between 0.8 and 1.5 based on the chemical reaction kinetic constant, representing the amplification factor of synergistic toxicity on the single contribution value. The residual decision module, based on the operating condition activity weights of each controlled emission source, calculates the dynamic residual... The weights for the synergistic toxicity contribution of each controlled emission source are assigned. The weight for operational activity is derived by performing nonlinear correction on the frequency of emission intensity fluctuations of the controlled emission source in the current sampling period and the material compatibility coefficients between emission components, thereby transforming the fuzzy synergistic risk into quantifiable evidence for administrative accountability.

[0030] Example 1: In a controlled operating condition of an industrial park containing controlled emission source 1 and controlled emission source 2, the wastewater discharged by both converges at the terminal of the drainage network. The flow velocity within the drainage network experiences a nonlinear time lag of 150 minutes due to pump station scheduling. The logic mapping module extracts the physicochemical index waveform with random fluctuation characteristics from the real-time operating condition feature vector of controlled emission source 1 and uses it as the physicochemical feature reference signal. By calculating the dynamic transfer entropy between the physicochemical index waveform obtained from the terminal and the physicochemical feature reference signal, the real-time transmission delay of the drainage network is determined, the time offset parameter in the logic delay transfer model is updated, and the temporal phase relationship of the co-toxicity contribution weight is calibrated. The residual decision module calculates the difference between the time series sequence of the comprehensive biological inhibition rate and the sum of the expected values ​​of the independent toxicity contributions of each controlled emission source based on the logic delay transfer model to obtain the dynamic residual. The calculation process follows these rules: ,in, For dynamic residuals, The values ​​are the real-time measured values ​​at the corresponding sampling times in the comprehensive biological inhibition rate time series. The number of controlled emission sources, For the first Expected independent toxicity contribution of each controlled emission source. The contribution correction factor is determined by performing nonlinear correction through an association mapping model based on the discharge intensity fluctuation frequency and material compatibility coefficient contained in the real-time operating condition feature vector.

[0031] The active verification module issues a flow micro-perturbation command containing pseudo-random temporal characteristics to the flow control actuator of controlled emission source 2. This command is essentially a detection signal actively initiated by the management system to verify the logical correlation between emission behavior and environmental impact, driving waveform fluctuations in physical flow. The logic mapping module extracts the feature waveform sequence from the terminal signal after the time delay locked by the logic delay propagation model, calculates the maximum mutual information between the feature waveform sequence and the flow micro-perturbation command, and uses the logical frequency co-frequency characteristic of the command issuance and physical response to determine the synergistic toxicity contribution weight of controlled emission source 2 in the joint effect. Because the generation of the flow micro-perturbation command directly drives the valve opening of the flow meter actuator at the controlled emission point to produce pseudo-random pulsations, it induces the physical emission entity to record specific logical coloring characteristics on the drainage time chain. When this characteristic is transported to the terminal with the water mass and captured by the biotoxicity monitor, the residual decision module identifies the energy proportion of this specific spectrum through mutual information calculation. In specific implementation, when the maximum mutual information I maxWhen the emission source exceeds the preset correlation threshold of 0.65, it is determined that the emission source has a unique causal relationship with the increase in the terminal inhibition rate. This solidifies the statistical correlation conclusion into an exclusive evidence document with a legal closed-loop effect. Through this proactive verification mechanism, the system transforms the regulatory will of the management end into the fluctuation verification of the physical end, thereby forming exclusive evidence in the administrative supervision process. The residual decision module performs spatiotemporal correlation regression analysis on the dynamic residuals, and removes the unrelated toxicity increments as ghost residuals from the responsibility weights of each controlled entity. The energy consumption power fingerprint of controlled emission source 2, extracted synchronously by the data acquisition module, shows that the sewage treatment facility maintains power fluctuations during the reported shutdown period. The residual decision module compares this data. The numerical deviation between the energy consumption power fingerprint and the co-toxicity contribution weight is used to determine that the controlled emission source 2 has engaged in data tampering and automatically lowers the confidence level of its source tracing conclusion. The decision instruction generation module, based on the corrected co-toxicity contribution weight, uses digital accountability logic to link it to electronic regulatory evidence documents containing flow micro-disturbance instruction verification records and energy consumption symmetry verification conclusions, and outputs administrative management decision suggestions with a complete causal logic closed loop to the management department. The decision instruction generation module, based on the corrected co-toxicity contribution weight, links it to electronic regulatory evidence documents containing flow micro-disturbance instruction verification records and energy consumption symmetry verification conclusions, and outputs emission source tracing conclusions with a closed evidence chain to the management department.

[0032] Example 2: The test environment simulated controlled drainage conditions. Data was sourced from a physical simulation platform including a pressure sensor, flow meter, and online biotoxicity monitor. The pressure sensor had a range of 0 to 100 kPa and a sampling frequency of 20 Hz. The biotoxicity monitor's detection cycle was set to 15 minutes. 25 dB of Gaussian white noise was superimposed on the test signal to simulate random fluctuations in the drainage environment. The sampling cycle was set to balance the spatiotemporal resolution of physical water mass diffusion with administrative response delay. When the drainage flow rate was between 10.0 L / s and 50.0 L / s, the sampling interval was set to 1 minute to capture transient characteristics. The test group used the wastewater toxicity joint effect prediction and emission source tracing decision management system of this invention, while the control group used... In the static hydraulic model, the experiment generated time delay offset gradients of different intensities by changing the waveforms of the physicochemical indicators of the controlled discharge source (see Table 1). The logic mapping module determined the real-time transmission delay by calculating the dynamic transfer entropy between the physicochemical indicator waveforms obtained by the terminal and the physicochemical characteristic reference signal. When the fluctuation amplitude of the water level in the pipeline increased from 2.5 cm to 7.5 cm, the real-time transmission delay identified by the logic delay transfer model was adjusted from 42.5 min to 48.2 min. At this time, the waveform correlation coefficient remained above 0.89, while the control group, due to the use of fixed time delay parameters, caused waveform misalignment, and the correlation coefficient dropped from 0.85 to 0.42. This confirms the compensation effectiveness of the logic delay transfer model for physical transmission phase deviation.

[0033] Table 1: Data Recording Table for Delay Calculation Process

[0034] ;

[0035] To address the combined toxicity effects caused by the synergistic effects of controlled emission sources, the experimental group calculated the dynamic residuals using a residual decision module. See Table 2. When controlled emission source 1 and controlled emission source 2 emit characteristic organic matter at a concentration of 1.2 mg / L, the measured time series values ​​of the integrated bioinhibition rate obtained at the terminal are as follows: The contribution rate is 42.5%, the sum of the expected values ​​of independent toxicity contributions is 28.6%, and the calculated dynamic residuals are... The percentage was 13.9%. As the discharge intensity increased from 0.5 kg / h to 2.5 kg / h, the accuracy of the system in allocating the co-toxicity contribution weight based on the operating condition activity weight remained above 94.5%. The calculation process followed the formula... ,in, For dynamic residuals, The values ​​are the real-time measured values ​​at the corresponding sampling times in the comprehensive biological inhibition rate time series. The number of controlled emission sources, For the first Expected independent toxicity contribution of each controlled emission source. Based on the frequency of pollution intensity fluctuations and the material compatibility coefficient contained in the real-time operating condition feature vector, the contribution correction factor was determined by performing nonlinear correction through the correlation mapping model. The control group had a responsibility assignment deviation of more than 30% due to the lack of a residual allocation mechanism, which confirms the ability of the residual decision module to quantify the nonlinear toxicity enhancement effect.

[0036] Table 2: Verification Table of Contribution Weights to Synergistic Toxicity

[0037] ;

[0038] The experiment simulated a scenario where the controlled entity tampered with the operating data. During the reporting period, the active verification module issued a flow micro-disturbance command (see Table 3). The residual decision module extracted the feature waveform sequence within the response time window. The maximum mutual information between the obtained feature waveform sequence and the flow micro-disturbance command was calculated to be 0.72. The energy consumption power fingerprint extracted by the data acquisition module showed that the operating power of the sewage treatment facility in this unit was 15.2kW, which was symmetrical with the fluctuation pattern of the co-toxicity contribution weight. Based on this, it was determined that there was data tampering and the confidence of the source tracing conclusion was reduced.

[0039] Table 3: Active Verification and Energy Consumption Symmetry Verification Data Table

[0040] ;

[0041] Example 3: This example combines Figs. 1 to 3This document describes a decision management system for predicting the combined effects of wastewater toxicity and tracing its sources. Fig. 1 As shown, the process begins with the data acquisition module, which is responsible for acquiring the time series sequence of the comprehensive biological inhibition rate and the real-time operating condition feature vector. The logic mapping module establishes a logic delay transmission model from each emission source to the terminal based on the physical topology. The residual decision module calculates the dynamic residual and the weight of the synergistic toxicity contribution according to the model, and triggers the active verification module when the weight is in the evidence uncertainty interval, generates a pseudo-random flow micro-disturbance command and sends it to the flow control actuator to drive the emission source to generate physical fluctuations. The residual decision module extracts the feature waveform sequence within the logic delay window for gain correction, and updates the weights according to the mutual information correlation evaluation results fed back by the active verification module. After the weights are determined, the decision command generation module generates the source tracing conclusion and control command according to the corrected weights, and outputs the emission source tracing conclusion and control command.

[0042] like Fig. 2 As shown, the horizontal axis lists three command states: pseudo-random sequence A, pseudo-random sequence B, and background flow. The left vertical axis represents the maximum mutual information, with a scale range of 0 to 1.0, and the right vertical axis represents the power consumption fingerprint in kW, with a scale range of 0 to 20. The legend clearly indicates that the bars filled with horizontal stripes represent the maximum mutual information, and the bars filled with diagonal stripes represent the power consumption fingerprint. The chart data shows that when pseudo-random sequence A or B is enabled, the values ​​of both the maximum mutual information and the power consumption fingerprint are at a high level, while in the background flow state, both values ​​decrease, confirming the positive correlation between command response and power consumption characteristics.

[0043] like Fig. 3 As shown, the overall system architecture mainly consists of a physical field layer on the left, an industrial IoT private network in the middle, and a digital decision layer on the right. The physical field layer includes controlled emission source nodes and pipeline end monitoring nodes. The controlled emission source nodes integrate flow micro-disturbance actuators, valves, pump stations, and operating condition and energy consumption sensing components, covering power, flow, and conductivity monitoring. The pipeline end monitoring nodes are equipped with online biotoxicity analyzers and hydraulic environment fingerprint collectors, covering water level, flow velocity, and pressure monitoring. The real-time multidimensional data stream generated by the physical layer is transmitted to the digital decision layer via the industrial IoT private network. The core of this layer is a high-performance decision computing cluster, which integrates a logic mapping engine, a residual decision core, an active verification micro-disturbance generator, and a decision command generator, and is connected to the electronic regulatory evidence chain database. The system calculates and generates source tracing conclusions, which are then sent to the administrative law enforcement and supervision end. At the same time, it sends micro-disturbance drive signals and verification and control commands to the physical field layer via the industrial IoT private network.

[0044] Example 4: In an industrial pipeline network monitoring scenario involving high-frequency pump station scheduling and multi-source intermittent emissions, the physicochemical index waveforms acquired by the drainage network terminal are affected by turbulence caused by instantaneous fluctuations in liquid level. The physicochemical index waveforms exhibit non-stationary random process characteristics. To calibrate the real-time transmission delay, the logic mapping module defines the conductivity waveform in the real-time operating condition feature vector of controlled emission source 1 as the source signal sequence. The conductivity waveform synchronously acquired by the terminal is defined as the response signal sequence. The logical mapping module uses a sliding window with a length of 128 sampling points to map the sequence. with sequence Reconstruction is performed, searching for time offsets in 1-minute steps within the candidate time delay range of 0 to 200 minutes. For each candidate time offset Calculate the sequence with sequence Dynamic transfer entropy between The calculation rules are as follows: ,in, For dynamic transfer entropy, the unit is bits. Let be the probability density function. For response signal sequence exist The value of the moment. Source signal sequence exist The value at time; the logical mapping module selects to make The candidate time offset that reaches the maximum value is used as the real-time transmission delay and written into the register of the logical delay propagation model to correct the phase misalignment of the comprehensive biological inhibition rate time series caused by the uncertainty of pipeline flow rate.

[0045] To determine the deviation threshold required for the residual decision-making module to identify data anomalies, the system uses a calibration method to determine parameter values. Specifically, the system reads the energy consumption power fingerprint and co-toxicity contribution weight data of the controlled emission sources during a historical continuous 72-hour normal emission reduction cycle, and calculates the residual fluctuation variance of both after normalization. The deviation threshold is set to To identify false data; when the real-time operating parameters of controlled emission source 2 show that it is operating at low load, but the co-toxicity contribution weight within its corresponding response time window is within the range of 15.0%, the residual decision module initiates symmetry verification and extracts the energy consumption power fingerprint sequence within that period. With the corresponding co-toxicity contribution weight sequence Calculate the normalized cross-correlation coefficient between the two. The calculation rules are as follows: ,in, To normalize the cross-correlation coefficient, Energy consumption fingerprint sequence The first in Values ​​of each sampling point The average value of energy consumption fingerprint. Weighted sequences contributing to synergistic toxicity The first in Values ​​of each sampling point The weighted mean of contribution to synergistic toxicity; If the value is below 0.45 and the deviation between the two values ​​exceeds the deviation threshold, it is determined that there is a logical conflict between the operating energy consumption data and water quality discharge performance of controlled emission source 2, triggering the weight reassessment procedure.

[0046] Example 5: In the on-site deployment of a newly built wastewater monitoring area, to establish the logical benchmark for source tracing, the system executes a continuous benchmark sampling program for 72 hours at the drainage network terminal. During the sampling process, the data acquisition module records the original physical signals from the pressure sensor and electromagnetic flowmeter at 1-minute intervals. Simultaneously, the online biotoxicity monitor acquires the time series sequence of the comprehensive biological inhibition rate under the benchmark water quality. Considering the physical interference of seasonal fluctuations in sewage discharge and the background of the pipe network environment on biological indicators, the logic mapping module defines the average comprehensive biological inhibition rate under the state of no discharge fluctuation in the continuous benchmark sampling program as the initial background noise value of the system. .

[0047] This value is used as the physical zero-point reference for subsequent dynamic residual calculations. When the controlled emission sources are in a stable operating state and the corresponding real-time operating condition feature vectors are within the preset fluctuation threshold range, the system calibrates the expected value of the independent toxicity contribution of each controlled emission source. The system injects standard toxicity-controlled samples into the sewage fluid at physical nodes of the drainage network, and the data acquisition module records the change in the overall biological inhibition rate at the terminal after the injection. And combined with the real-time transmission delay determined by the logical delay propagation model Establish a response mapping relationship, the calculation rule of which is as follows: ,in, For the first Expected independent toxicity contribution of each controlled emission source. To calibrate the number of times, For the first The measured value of the terminal inhibition rate increment generated after the second injection of controlled samples; the calibration data is stored in the database as the initial calculation data for the residual decision module to perform quantitative decision-making, and the system has a physical reference basis for the allocation of synergistic toxicity contribution weights when facing the nonlinear joint effects of pollutants.

[0048] Example 6: In the pre-deployment procedure for a newly built industrial area with a drainage branch pipe diameter of 800mm, the system determines the physical gain value of the flow micro-disturbance command based on the cross-sectional area of ​​the drainage network. and real-time flow rate of sewage Calculate the pulse injection flow rate of the standard toxicity-controlled sample. Its calculation rules follow the formula ,in, Instantaneous injection flow rate of the controlled sample, in m³ / s. 3 / s, The preset terminal target suppression rate gain value is 5.0%. The cross-sectional area of ​​the drainage branch pipe is expressed in m². 2 , The real-time flow velocity of the sewage is expressed in m / s. The original concentration of the standard toxicity-controlled sample; the duration of injection during calibration. The time interval is 10 minutes, and the signal-to-noise ratio of the terminal suppression rate response waveform is monitored by the logic mapping module. When the signal-to-noise ratio is lower than 15dB, the signal-to-noise ratio is adjusted upward in steps of 1.2 times. The numerical value is calculated until the response signal crosses the background noise threshold, thereby determining the baseline value of the command pulse intensity for that specific pipeline topology.

[0049] To ensure logical decoupling between the flow micro-disturbance command generated by the active verification module and background flow fluctuations, the system executes a frequency domain feature calibration procedure: the data acquisition module continuously records a 4-hour background flow time series, and the logic mapping module calculates the power spectral density distribution of the background flow signal; the system identifies the pump station scheduling frequency with the highest energy proportion in the background flow. and liquid level fluctuation frequency And select the carrier frequency for the micro-perturbation command within the low-energy frequency band. Its carrier frequency The selection rules follow Constraints, among which, For the selected carrier frequency, For frequency The corresponding power spectral density value, It is 0.01Hz. The frequency is 0.1Hz; the active verification module calculates the maximum mutual information between the terminal's characteristic waveform sequence and the traffic micro-perturbation command. The confidence level is determined by following the formula. ,in, Mutual information, measured in bits. For characteristic waveform sequence variables With flow micro-perturbation command variable The joint probability distribution function, and Variables With variables The marginal probability distribution function; after selecting the carrier frequency, the system generates a flow command with pseudo-random phase characteristics, so that the maximum mutual information calculated by the residual decision module crosses the relevant threshold when the controlled emission source produces a physical response, thus eliminating the interference of pipeline hydraulic fluctuations on the source tracing conclusion.

[0050] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0051] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A wastewater toxicity joint effect prediction and emission source tracing decision management system, characterized in that, include: The data acquisition module is used to synchronously acquire the time series sequence of the comprehensive biological inhibition rate of the drainage network terminal and the real-time operating condition feature vector of each controlled emission source upstream of the drainage network. The logic mapping module is used to establish a logical time delay propagation model from each controlled emission source to the terminal based on the physical topology of the drainage network. The residual decision module is used to calculate the difference between the sum of the expected values ​​of the independent toxicity contributions of each controlled emission source and the time series sequence of the comprehensive biological inhibition rate based on the logical delay propagation model to obtain the dynamic residual. Based on the operating activity weight of each controlled emission source, the dynamic residual is allocated as the synergistic toxicity contribution weight of each controlled emission source. The active verification module operates as follows: when any co-toxicity contribution weight is within a preset evidence uncertainty range, a flow micro-perturbation instruction containing pseudo-random time-series characteristics is generated and sent to the flow control actuator of the controlled emission source to drive the controlled emission source to generate physical flow fluctuations. The residual decision module is also used to extract feature waveform sequences from the time series of the comprehensive biological inhibition rate within the response time window defined by the logical delay propagation model, and to adjust the gain of the synergistic toxicity contribution weight based on the mutual information correlation evaluation results between the feature waveform sequences and the flow micro-perturbation command. The decision instruction generation module is used to generate emission source tracing conclusions, including the determination of exclusive liability, and control instructions for specific controlled emission sources, based on the revised co-toxicity contribution weights.

2. The wastewater toxicity joint effect prediction and emission source tracing decision management system according to claim 1, characterized in that, The decision instruction generation module is also used to associate the corrected co-toxicity contribution weight with the administrative law enforcement document template with a unique timestamp; the active verification module, when generating the flow micro-disturbance instruction, determines the flow micro-disturbance adjustment amplitude based on the historical emission flow distribution of the controlled emission source, so that the intensity of the biological inhibition rate fluctuation generated by the flow micro-disturbance is greater than the background noise of the terminal, and the instantaneous emission flow corresponding to the flow micro-disturbance is within the preset compliance threshold range. The residual decision module determines the degree of temporal correlation between the characteristic waveform sequence and the flow micro-disturbance command by calculating the maximum mutual information between the two.

3. The wastewater toxicity joint effect prediction and emission source tracing decision management system according to claim 1, characterized in that, When calculating dynamic residuals, the residual decision module executes the following quantization rules: ,in, For dynamic residuals, The values ​​are the real-time measured values ​​at the corresponding sampling times in the comprehensive biological inhibition rate time series. The number of controlled emission sources, For the first Expected independent toxicity contribution of each controlled emission source. The contribution correction factor is determined by performing nonlinear correction through an association mapping model based on the discharge intensity fluctuation frequency and material compatibility coefficient contained in the real-time operating condition feature vector.

4. The wastewater toxicity joint effect prediction and emission source tracing decision management system according to claim 1, characterized in that, The data acquisition module is used to obtain the operating energy consumption characteristic sequence of wastewater treatment facilities in each controlled emission source; the residual decision module is also used to establish a correlation mapping model between the operating energy consumption characteristic sequence and the co-toxicity contribution weight; based on the correlation mapping model, the numerical deviation between the fluctuation pattern of the operating energy consumption characteristic sequence and the co-toxicity contribution weight is calculated; when the numerical deviation exceeds the preset deviation threshold, it is determined that the controlled emission source has data tampering behavior, and the weight re-evaluation procedure is triggered to reduce the confidence level of the source tracing conclusion of the corresponding controlled emission source.

5. The wastewater toxicity joint effect prediction and emission source tracing decision management system according to claim 1, characterized in that, The logic mapping module is used to extract the waveforms of physicochemical indicators with random fluctuation characteristics from the real-time operating condition feature vector and use them as the reference signal for physicochemical characteristics. The dynamic transfer entropy between the waveforms of the corresponding physicochemical indicators and the reference signals of physicochemical characteristics obtained by the computing terminal is used to determine the real-time transmission delay of the drainage network. Based on the real-time transmission delay, the time offset parameter in the logical delay transmission model is updated to calibrate the temporal phase relationship of the co-toxicity contribution weights.

6. The wastewater toxicity joint effect prediction and emission source tracing decision management system according to claim 1, characterized in that, The residual decision module is used to perform spatiotemporal correlation regression analysis on the dynamic residuals. When the mutual information between the dynamic residuals and the real-time operating condition feature vectors of all controlled emission sources is lower than the preset correlation threshold, the dynamic residuals are defined as uncontrolled source interference residuals. The uncontrolled source interference residuals are separated from the collaborative toxicity contribution weights of each controlled emission source, and administrative verification instructions pointing to specific pipeline physical areas are generated by back-calculating based on the logical time delay propagation model.

7. The wastewater toxicity joint effect prediction and emission source tracing decision management system according to claim 1, characterized in that, The data acquisition module, while acquiring real-time operating condition feature vectors, simultaneously extracts the local water level fluctuation features caused by the discharge behavior; The logic mapping module uses local water level fluctuation characteristics as water level pulsation fingerprints, retrieves response periods with waveform consistency in the comprehensive biological inhibition rate time series, and calculates the time difference between the water level pulsation fingerprint and the response period to correct the timing alignment accuracy of the logic delay propagation model.

8. The wastewater toxicity joint effect prediction and emission source tracing decision management system according to claim 1, characterized in that, The decision instruction generation module includes an evidence collection and storage unit, which encapsulates the generation record of the traffic micro-disturbance instruction, the characteristic waveform readback sequence of the terminal, and the verification conclusion of the residual decision module into an electronic regulatory evidence file with unique timestamp and digital signature verification attributes.

9. The wastewater toxicity joint effect prediction and emission source tracing decision management system according to claim 1, characterized in that, The residual decision module performs nonlinear correction on the allocation ratio of dynamic residuals when calculating the operating condition activity weight, based on the frequency of pollution intensity fluctuations of controlled emission sources in the current sampling period and the material compatibility coefficients between emission components.

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