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CN-121980460-A - Industrial wastewater abnormal discharge identification method based on multi-parameter collaborative analysis

CN121980460ACN 121980460 ACN121980460 ACN 121980460ACN-121980460-A

Abstract

The application belongs to the technical field of environmental monitoring and intelligent analysis, and particularly relates to an industrial wastewater abnormal discharge identification method based on multi-parameter collaborative analysis. The method aims to solve the problems of high false alarm rate, weak anti-interference capability and insufficient instantaneity caused by single threshold judgment, static feature fusion and lack of process logic and time sequence dynamic modeling in the prior art. The method comprises the steps of synchronously collecting four parameters of water quality, water quantity, process running state and time stamp, aligning the self-adaptive data, fusing short-time mutation and long-time period characteristics through a multi-scale time sequence characteristic extraction unit, generating a logic mask by combining a process logic constraint encoder, and dynamically updating a model by an abnormality discrimination engine through an unsupervised reconstruction error and supervised fine adjustment double-path fusion mechanism and an online increment learning module. The application can realize second-level response and high-sensitivity identification of hidden abnormal behaviors such as theft and omission.

Inventors

  • LIU SHILIANG
  • HUANG XUJUN

Assignees

  • 广州良森仪表科技有限公司

Dates

Publication Date
20260505
Application Date
20260126

Claims (10)

  1. 1. The industrial wastewater abnormal discharge identification method based on the multi-parameter collaborative analysis is characterized by comprising the following steps of: Synchronously acquiring water quality parameters, water quantity parameters, process running state parameters and time stamp information from an industrial wastewater discharge monitoring system, performing time stamp alignment according to a unified time reference, and generating continuous multi-parameter time sequence fragments through a sliding time window mechanism; inputting the multi-parameter time sequence segment into a self-adaptive data alignment module, carrying out interpolation complementation and resampling processing on each parameter sequence based on timestamp information, carrying out noise filtering on water quality parameters, deducing a confidence interval of the missing parameters according to process running state parameters and generating a substitution value with confidence weight for the parameter missing situation continuously exceeding a preset time threshold value, and obtaining aligned time sequence data; Inputting the aligned time sequence data into a multi-scale time sequence feature extraction unit, respectively extracting local high-frequency time sequence features and long-term period time sequence features through short-time window convolution branches and long-time window circulation branches which are arranged in parallel, and performing weighted fusion through a channel attention mechanism to generate a joint time sequence representation; Inputting the joint time sequence representation into a process logic constraint encoder, converting a preset industrial wastewater discharge process rule into a computable logic constraint condition by the process logic constraint encoder, and generating a binary mask matrix aligned with the joint time sequence representation dimension, wherein positions violating the process rule are marked as a first value, and the rest positions are marked as a second value; The combined time sequence characterization and the binary mask matrix are input into an anomaly discrimination engine together, the anomaly discrimination engine calculates an anomaly confidence score through a dual-path structure, wherein a main path calculates a reconstruction residual error through a time sequence self-encoder as an initial anomaly score, an auxiliary path adjusts a discrimination boundary based on a supervised fine adjustment module and outputs a fine-adjusted anomaly score, and the two path outputs are fused through dynamic weights to generate a final anomaly confidence score; and according to a comparison result of the abnormal confidence score and a preset threshold, judging whether to trigger abnormal emission early warning and generating a corresponding event report by combining the illegal state indicated by the binary mask matrix.
  2. 2. The method for identifying abnormal industrial wastewater discharge according to claim 1, wherein the operations performed by the adaptive data alignment module specifically comprise: Up-sampling the water quantity parameter and the process running state parameter to the sampling frequency of the water quality parameter by adopting a linear interpolation method; smoothing the water quality parameters by adopting a sliding median filter to inhibit sensor drift noise; When it is determined that a certain parameter has no valid sampling value within thirty seconds, a reasonable confidence interval of the missing parameter is deduced according to the current process running state parameter, and a substitute value with a confidence weight is generated, wherein the confidence weight is determined by the relative width of the confidence interval.
  3. 3. The method for identifying abnormal discharge of industrial wastewater according to claim 1, wherein in the multi-scale time series feature extraction unit: The short-time window convolution branch adopts a three-layer one-dimensional convolution stacking structure, the size of each layer of convolution kernel is 3, the step length is 1, and the activation function is a correction linear unit and is used for processing the last sixty seconds of time sequence data to capture local high-frequency fluctuation characteristics; The long-time window circulation branch adopts a bidirectional gating circulation unit network, the dimension of a hidden layer is 128, and the hidden layer is used for processing the latest seven thousand two hundred seconds of time sequence data to model a process periodicity rule of a cross-hour level, and the dimension is reduced to 64 dimensions through a full connection layer after the forward output and the backward output are spliced; The channel attention mechanism obtains channel statistics by carrying out global average pooling on the feature vectors of each channel, and then generates normalized channel weight coefficients through a two-layer fully-connected network and a Sigmoid function, and the feature vectors output by the short-time window convolution branches and the long-time window circulation branches are weighted and summed according to the weight coefficients to generate 128-dimensional joint time sequence representation.
  4. 4. The method for identifying abnormal emissions of industrial wastewater according to claim 1, wherein the operations performed by the process logic constraint encoder specifically comprise: Reading process rule items in a configuration file through a rule analyzer, wherein each rule comprises a precondition set and a conclusion action, and the precondition consists of a parameter name, a comparison operator and a threshold value; And when the method runs, traversing all rules, carrying out piece-by-piece matching on the current multi-parameter time sequence fragments, if the data of a certain time step violates any rule, generating a binary mask value of 0 at a corresponding position, and otherwise, generating 1.
  5. 5. The method for identifying abnormal discharge of industrial wastewater according to claim 1, wherein, in the abnormality determination engine: the encoder of the time sequence self-encoder comprises two layers of gating circulating units, a decoder adopts a deconvolution and circulating unit mixed structure, a reconstruction residual is defined as a mean square error between an input multi-parameter time sequence segment and a reconstruction output of the reconstruction residual, and the reconstruction residual is used as an initial anomaly score after being subjected to exponential smoothing filtration; The supervised fine tuning module adopts a twin network structure, shares the encoder part of the time sequence self-encoder, has a loss function of ternary group loss, and adjusts the discrimination boundary by shortening the embedding distance of similar abnormal samples and pushing away the embedding distance of normal and abnormal samples; in the dynamic weight fusion, a weight value is obtained by calculating the average cosine similarity between the current data segment and a normal sample in a sliding experience playback buffer zone, the average cosine similarity is mapped to a [0,1] interval through a Sigmoid function, and a final abnormal confidence score is obtained by adding one to the product of the weight value and an initial abnormal score and subtracting the product of the weight value and a fine-tuned abnormal score.
  6. 6. The method for identifying abnormal emissions of industrial wastewater according to claim 1 or 5, wherein the abnormality discrimination engine incorporates an online incremental learning mechanism that performs operations comprising: receiving an external confirmation signal through an event feedback interface, adding the current time window and the data of the five time windows before and after the current time window into an experience playback buffer zone when the abnormal confirmation signal is received, and starting a gradient feedback fine tuning process of a self-encoder decoder part and a twin network full-connection projection layer; When a false alarm correction signal is received, marking the corresponding data segment as a normal sample, adding the normal sample into the experience playback buffer zone, and adjusting a discrimination threshold value to be lower than the current abnormal confidence score by 0.1 unit; the experience playback buffer zone adopts a first-in first-out strategy, the capacity is fixed to be one thousand time windows, the experience playback buffer zone is used for periodically calibrating and judging the threshold value, the calibration period is twenty-four hours, and the 99 percentile of the normal sample abnormal confidence score in the buffer zone is calculated to be used as a first threshold value, and the 99.9 percentile is calculated to be used as a second threshold value.
  7. 7. The method for identifying abnormal discharge of industrial wastewater according to claim 1, wherein the rule for determining whether to trigger abnormal discharge early warning is specifically as follows: Triggering a first-level early warning when the abnormal confidence score continuously exceeds a first threshold for sixty seconds or exceeds a second threshold for a single time; If the first-level early warning is triggered and the binary mask matrix indicates illegal operation in the corresponding time step, upgrading to the second-level early warning; After triggering the early warning, generating a structured event report, wherein the structured event report comprises abnormal starting time, duration, dominant abnormal parameters, associated process states, confidence scores and an original data snapshot.
  8. 8. The method for identifying abnormal industrial wastewater discharge according to claim 1, wherein the water quality parameters comprise chemical oxygen demand, ammonia nitrogen concentration, total phosphorus concentration, pH value and conductivity, the water quantity parameters comprise instantaneous flow and accumulated discharge, and the process operation state parameters comprise a production equipment start-stop signal, a pump valve on-off state and a sewage treatment unit operation mode identifier.
  9. 9. The method for identifying abnormal industrial wastewater discharge according to claim 1, wherein the window length of the sliding time window mechanism is three hundred sixty seconds and the sliding step length is ten seconds.
  10. 10. The method for identifying abnormal industrial wastewater discharge according to claim 1, wherein the method is characterized in that the execution time sequence of each functional module is uniformly scheduled through a central cooperative controller, the central cooperative controller sequentially calls an adaptive data alignment module, a multi-scale time sequence feature extraction unit, a process logic constraint encoder and an abnormal judging engine, and all the modules adopt a memory sharing queue for data transmission and adopt a pipeline parallel architecture for processing, so that the end-to-end delay from data input to early warning output is not more than five seconds.

Description

Industrial wastewater abnormal discharge identification method based on multi-parameter collaborative analysis Technical Field The invention belongs to the technical field of environmental monitoring and intelligent analysis, and particularly relates to an industrial wastewater abnormal discharge identification method based on multi-parameter collaborative analysis. Background Along with the continuous increase of the national protection of the water ecological environment, the supervision of industrial pollution sources is gradually changed from terminal treatment to intelligent overall process monitoring, wherein the real-time and accurate identification of the abnormal discharge behavior of industrial wastewater has become a key link for improving the environment law enforcement efficiency and the risk prevention and control capability. Under the background, the traditional monitoring mode of setting a fixed threshold value to carry out overrun alarm depending on a single water quality index (such as chemical oxygen demand, ammonia nitrogen concentration and the like) has gradually revealed systematic defects of large recognition blind area, high false alarm rate, weak anti-interference capability and the like because complex emission behaviors under the multi-factor coupling effect are difficult to reflect. In order to break through the limitation, researchers in recent years begin to explore intelligent recognition paths based on multi-parameter collaborative analysis, attempt to construct an anomaly discrimination model with more robustness by fusing multi-dimensional information such as water quality, water quantity, process running state and time sequence characteristics, so as to effectively capture hidden illegal behaviors such as illegal discharge, missing discharge and exceeding standard discharge under dynamic working conditions. In this technology evolution context, some existing schemes attempt to introduce a multiparameter fusion mechanism. For example, patent publication number CN118395223B proposes an environmental sensitive area identification method based on a multi-parameter collaborative input matrix and a joint clustering network, which enhances feature expression capability through a re-parameterized layer, and extracts global collaborative characterization to support environmental assessment decisions. The method can effectively integrate multi-source environment data in a static geological sampling scene, and shows good clustering consistency and space association modeling capacity. However, the scheme is based on the data assumption of steady-state and discrete sampling points, the model architecture is not embedded with a time sequence dynamic modeling unit, and the non-stationary characteristics of flow mutation, equipment start-stop disturbance, intermittent emission and the like which are common in the industrial emission process are not considered. More importantly, the objective function focuses on regional sensitivity grading, rather than constructing a discrimination boundary for a specific event of abnormal emission, so that parameter deviation caused by normal production fluctuation cannot be distinguished from malicious emission behavior of artificial evasion supervision. Correspondingly, another patent with publication number CN120338615B constructs a depth neural network fused with physical constraints for collaborative simulation and driving mechanism analysis of multiple water quality indexes, and the core task of the depth neural network is still in the forward simulation category although the progress is made in the mechanism reduction of the water quality evolution process, and the training aim is to minimize the reconstruction error of historical data instead of learning the distribution characteristics of abnormal samples. On the basis, the scheme does not incorporate key non-water quality process parameters such as flow, discharge period, pump valve state and the like, so that the model lacks perception capability on typical avoidance strategies such as low concentration high flow dilution discharge or abnormal drainage during non-production period at night. In addition, the reasoning process depends on batch historical data backtracking, an online increment learning and low-delay response mechanism is lacked, and real-time requirements of a supervision platform on second-level early warning are difficult to meet. However, with the improvement of the automation level of industrial production and the deepening of the degree of refinement of environmental protection law enforcement, the structural contradiction of the technical scheme in principle is increasingly prominent, on one hand, the multi-parameter fusion should promote the dimension coverage and the context sensing capability of abnormal recognition, and on the other hand, if the fusion mechanism is not deeply coupled with the dynamic property, the burstiness and the artificial intervention characterist