Real-time detection method and system for effluent quality of sewage treatment

Abstract

The invention relates to the technical field of sewage treatment, in particular to a sewage treatment effluent quality real-time detection method and system.The method is applied to a sewage treatment system.The method comprises the steps that a discharge path of the sewage treatment system is divided into at least two effluent monitoring nodes, presetting a weight rule base of water quality characteristic parameters for each effluent monitoring node; collecting a water quality characteristic parameter set of each effluent monitoring node, an operation state data set of the sewage treatment system and an environment parameter data set in real time; determining the current operation condition of the sewage treatment system according to the operation state data, and matching corresponding parameter weight configuration in a weight rule base; performing weight analysis on the water quality characteristic parameter data and the parameter weight configuration to generate a water quality comprehensive abnormal index corresponding to the effluent monitoring node; the abnormal process position of sewage treatment can be accurately traced, and the accuracy of abnormal positioning is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of sewage treatment, and particularly to a real-time detection method and system for the water quality of sewage treatment effluent. Background Art

[0002] With the acceleration of the industrialization process and the improvement of the urbanization level, sewage treatment has become an important part of environmental protection. The operation effect of the sewage treatment system is directly related to the water quality of the water body environment and ecological safety. Existing water quality monitoring technologies mostly rely on fixed-terminal detection devices. Usually, a single sensor is set at the discharge port, such as an on-line chemical oxygen demand analyzer or an ammonia nitrogen probe. Water quality parameters are obtained through periodic sampling, and the water quality is judged to be qualified based on this.

[0003] However, due to the differences in the water quality change characteristics of different process sections in the treatment process, a single monitoring point cannot trace the abnormal process position of sewage treatment, resulting in difficult abnormal positioning. At the same time, the existing methods do not fully consider the differences in the influence degrees of various water quality characteristic parameters on the water quality under different operating conditions of the sewage treatment system. For example, when the load of the sewage treatment plant is high and the treatment process is complex, the importance of some water quality parameters may change significantly, and the existing methods cannot adaptively adjust the weights, making the water quality evaluation results inaccurate.

[0004] Therefore, there is an urgent need for a real-time detection method for the water quality of sewage treatment effluent that can comprehensively monitor the discharge path, accurately analyze water quality characteristic parameters, make full use of multi-source data, and can timely and accurately locate abnormal links in sewage treatment. Summary of the Invention

[0005] The present invention provides a real-time detection method and system for the water quality of sewage treatment effluent that can accurately trace the abnormal process position of sewage treatment and improve the accuracy of abnormal positioning, and can effectively solve the problems in the background art.

[0006] To achieve the above object, in the first aspect, the present invention provides a real-time detection method for the water quality of sewage treatment effluent. The method is applied to a sewage treatment system, and the method includes: Dividing the discharge path of the sewage treatment system into at least two effluent monitoring nodes, and presetting a weight rule library for water quality characteristic parameters for each of the effluent monitoring nodes; Real-time collecting a water quality characteristic parameter set, an operating state data set of the sewage treatment system, and an environmental parameter data set for each of the effluent monitoring nodes; Determining the current operating condition of the sewage treatment system according to the operating state data, and matching a corresponding parameter weight configuration in the weight rule library based on this; Perform weighted analysis on the water quality characteristic parameter data and the parameter weight configuration to generate a comprehensive water quality anomaly index corresponding to the effluent monitoring node; Input the environmental parameter data and the operation status data into a preset environmental compensation model, output an environmental compensation coefficient, and use this to compensate and correct the comprehensive water quality anomaly index to obtain a corrected anomaly index; Compare the corrected anomaly index with a preset water quality compliance threshold: If the corrected anomaly index exceeds the threshold, trigger an over-standard warning and locate the abnormal effluent monitoring node; otherwise, continuously execute the data collection and anomaly index calculation steps.

[0007] Combined with the first aspect, in a possible design, the weight rule library includes parameter weight configurations corresponding to different operating conditions, and the operating conditions include the treatment water volume range, the influent water quality category, and the equipment load level.

[0008] Combined with the first aspect, in a possible design, the water quality characteristic parameter set includes at least any two of pH value, turbidity, dissolved oxygen concentration, ammonia nitrogen concentration, and total phosphorus concentration; The operation status data set includes at least the current treatment water volume and equipment operation parameters; The environmental parameter data set includes at least environmental temperature, environmental humidity, and environmental air pressure.

[0009] Combined with the first aspect, in a possible design, the calculation formula for the comprehensive water quality anomaly index is: ; where represents the comprehensive water quality anomaly index corresponding to the jth effluent monitoring node, represents the real-time acquisition value of the ith water quality characteristic parameter corresponding to the jth effluent monitoring node; represents the standard limit value of the ith water quality characteristic parameter corresponding to the jth effluent monitoring node; represents the weight value of the ith water quality characteristic parameter corresponding to the jth effluent monitoring node, and the weight value is obtained by matching from the weight rule library according to the current operating condition; n represents the total number of characteristic parameters in the water quality characteristic parameter set corresponding to the jth effluent monitoring node; k represents a non-linear dynamic index factor used to enhance the sensitivity of over-standard parameters.

[0010] Combined with the first aspect, in a possible design, the calculation formula for the non-linear dynamic index factor k is: ; By setting the non-linear dynamic index factor, when the ratio of the real-time acquisition value of the water quality characteristic parameter to the standard limit value ≥ 1.2, k = 1.5 is adopted, otherwise k = 1.2 is adopted.

[0011] In combination with the first aspect, in a possible design, the calculation formula of the environmental compensation model is as follows: ; Wherein, represents the energy change caused by temperature change; represents the real-time temperature; represents the reference temperature; represents the temperature sensitivity coefficient; represents the energy change caused by humidity change; represents the real-time humidity; represents the humidity sensitivity coefficient; represents the energy change caused by pressure change; represents the real-time pressure; represents the standard pressure; represents the pressure sensitivity coefficient; represents the dissolved oxygen activation flag, with a value of 0 or 1, indicating whether compensation is enabled; represents the energy change caused by the change in the sewage treatment flow rate; represents the real-time sewage treatment flow rate; represents the maximum flow rate; represents the rated flow rate; represents the flow rate sensitivity coefficient; represents the energy change caused by the equipment load level; represents the real-time equipment load level, such as the percentage of the aerator power; represents the equipment load level sensitivity coefficient; represents the environmental compensation coefficient; 、 、 、 and respectively represent the normalization functions of temperature, humidity, pressure, sewage treatment flow rate, and equipment load level.

[0012] In combination with the first aspect, in a possible design, a weight rule library for presetting water quality characteristic parameters for each of the effluent monitoring nodes is provided, including: Based on the historical operation data of the sewage treatment system, analyze the water quality change characteristics of different treatment process sections, and determine the influence weights of each water quality characteristic parameter on the water quality under different working conditions; According to the influence weights, use machine learning algorithms to construct a weight rule library, and the machine learning algorithms include at least one of support vector machines, random forests, or gradient boosting trees.

[0013] Combined with the first aspect, in a possible design, the real-time acquisition of the water quality characteristic parameter set, the operation status data set of the sewage treatment system, and the environmental parameter data set of each effluent monitoring node includes: Adopt a distributed sensor network, deploy multiple types of sensors at each of the effluent monitoring nodes, and the sensors include at least two of optical sensors, electrochemical sensors, and microbial sensors; Through data interaction with the automatic control system and environmental monitoring system of the sewage treatment system, real-time obtain operation status data and environmental parameter data, and perform real-time verification and cleaning on the collected data to remove outliers and noise data.

[0014] Combined with the first aspect, in a possible design, the determination of the current operating condition of the sewage treatment system according to the operation status data, and the matching of the corresponding parameter weight configuration in the weight rule library includes: Perform feature extraction and feature engineering on the operation status data to construct a feature vector reflecting the operation status of the sewage treatment system; Use a clustering algorithm to divide the feature vector into different operating condition categories, and the clustering algorithm includes at least one of K-Means clustering, DBSCAN clustering, and Gaussian mixture model clustering; According to the current operating condition category, match the corresponding parameter weight configuration in the weight rule library.

[0015] In the second aspect, the present invention also provides a real-time detection system for the water quality of sewage treatment effluent, including: A monitoring node module for dividing the discharge path of the sewage treatment system into at least two effluent monitoring nodes, and presetting a weight rule library for water quality characteristic parameters for each effluent monitoring node; A data acquisition module for real-time acquiring the water quality characteristic parameter set, the operation status data set of the sewage treatment system, and the environmental parameter data set of each effluent monitoring node; An operating condition analysis module for determining the current operating condition according to the operation status data of the sewage treatment system, and based on the current operating condition, matching the corresponding parameter weight configuration in the weight rule library; An abnormal index calculation module for performing weighted calculation on the water quality characteristic parameter data and the matched parameter weight configuration to generate a water quality comprehensive abnormal index corresponding to each effluent monitoring node; A compensation module for inputting the environmental parameter data and the operation status data into a preset environmental compensation model, outputting an environmental compensation coefficient, and compensating and correcting the water quality comprehensive abnormal index according to the environmental compensation coefficient to obtain a corrected abnormal index; An alarm module is used to compare the corrected abnormal index with a preset water quality compliance threshold. If the corrected abnormal index exceeds the water quality compliance threshold, an over-standard warning is triggered, and the abnormal effluent monitoring node is located. Otherwise, the data collection and abnormal index calculation steps are continuously executed.

[0016] Through the technical solution of the present invention, the following technical effects can be achieved: By dividing the discharge path into multiple effluent monitoring nodes, the limitation of the previous single monitoring point is changed, and the comprehensive coverage of the discharge path of the sewage treatment system is realized. Each node can collect the water quality characteristic parameter set in real time. When the water quality is abnormal, the abnormal effluent monitoring node can be quickly located based on the data of each node, and the abnormal process position of the sewage treatment can be accurately traced, improving the accuracy of abnormal positioning and changing the dilemma that it is difficult to determine the abnormal link by traditional methods. A weight rule library for water quality characteristic parameters is preset for each effluent monitoring node, and the current operating condition is determined according to the operating state data of the sewage treatment system, and then the corresponding parameter weight configuration is matched, fully considering the differences in the influence degree of various water quality characteristic parameters on the water quality under different operating conditions. When the load of the sewage treatment plant is high and the treatment process is complex and other different operating conditions, the weight can be adjusted adaptively, making the water quality evaluation result more in line with the actual situation. Compared with the traditional evaluation method with fixed weights, the accuracy of water quality evaluation is improved. The operating state data set and the environmental parameter data set of the sewage treatment system are collected in real time and combined with the water quality characteristic parameter data. The operating state data reflects the internal working conditions of the system, and the environmental parameter data reflects the influence of the external environment on the water quality. Through a preset environmental compensation model, these multi-source data are used to output an environmental compensation coefficient to compensate and correct the comprehensive water quality abnormal index, and a corrected abnormal index is obtained. By comprehensively considering various factors affecting the water quality, the one-sidedness of a single data source is avoided, thereby optimizing the water quality abnormal evaluation system, making the final abnormal evaluation result more comprehensive and reliable, and being able to judge whether the water quality meets the standard more timely and accurately. Description of the Drawings

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0018] Figure 1 It is the logic flowchart of the real-time detection method for the sewage treatment effluent water quality in the present invention. Figure 2 It is the structural block diagram of the real-time detection system for the sewage treatment effluent water quality in the present invention. Detailed implementation manners

[0019] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments.

[0020] The present application will be described below in conjunction with the accompanying drawings in the present application.

[0021] As Figure 1 shown, the real-time detection method for the effluent water quality of sewage treatment in the present invention is applied to a sewage treatment system, and specifically includes the following steps: Step S1: Divide the discharge path of the sewage treatment system into at least two effluent monitoring nodes, and preset a weight rule library for the water quality characteristic parameters of each effluent monitoring node; the weight rule library includes parameter weight configurations corresponding to different operating conditions, and the operating conditions include the treatment water volume range, the influent water quality category, and the equipment load level; Step S2: Real-time collect the water quality characteristic parameter set, the operating state data set of the sewage treatment system, and the environmental parameter data set of each effluent monitoring node; the water quality characteristic parameter set includes at least any two of the pH value, turbidity, dissolved oxygen concentration, ammonia nitrogen concentration, and total phosphorus concentration; the operating state data set includes at least the current treatment water volume and equipment operating parameters; the environmental parameter data set includes at least environmental temperature, environmental humidity, and environmental air pressure; Step S3: Determine the current operating condition of the sewage treatment system according to the operating state data, and match the corresponding parameter weight configuration in the weight rule library accordingly; Step S4: Perform weight analysis on the water quality characteristic parameter data and the parameter weight configuration to generate a water quality comprehensive anomaly index corresponding to the effluent monitoring node; Step S5: Input the environmental parameter data and the operating state data into a preset environmental compensation model, output an environmental compensation coefficient, and use this to compensate and correct the water quality comprehensive anomaly index to obtain a corrected anomaly index; Step S6: Compare the corrected anomaly index with a preset water quality compliance threshold: if the corrected anomaly index exceeds the threshold, trigger an over-standard warning and locate the abnormal effluent monitoring node; otherwise, continuously execute the data collection and anomaly index calculation steps.

[0022] In this embodiment, by dividing the discharge path into multiple effluent monitoring nodes, the limitation of the previous single monitoring point is changed, and the comprehensive coverage of the discharge path of the sewage treatment system is realized; each node can collect the water quality characteristic parameter set in real time. When the water quality is abnormal, the abnormal effluent monitoring node can be quickly located based on the data of each node, and the abnormal process position of the sewage treatment can be accurately traced, improving the accuracy of abnormal positioning and changing the dilemma that it is difficult to determine the abnormal link by traditional methods; A weight rule library of water quality characteristic parameters is preset for each effluent monitoring node, and the current operating condition is determined according to the operating state data of the sewage treatment system, and then the corresponding parameter weight configuration is matched, fully considering the differences in the influence degree of various water quality characteristic parameters on the water quality under different operating conditions; in different operating conditions such as high load and complex treatment process in the sewage treatment plant, the weight can be adjusted adaptively, making the water quality evaluation result more in line with the actual situation. Compared with the traditional evaluation method with fixed weight, the accuracy of water quality evaluation is greatly improved; The operating state data set and the environmental parameter data set of the sewage treatment system are collected in real time and combined with the water quality characteristic parameter data; the operating state data reflects the internal working conditions of the system, and the environmental parameter data reflects the influence of the external environment on the water quality; through the preset environmental compensation model, these multi-source data are used to output the environmental compensation coefficient to compensate and correct the water quality comprehensive anomaly index, and the corrected anomaly index is obtained. By comprehensively considering various factors affecting the water quality, the one-sidedness of a single data source is avoided, so as to optimize the water quality anomaly evaluation system, making the final anomaly evaluation result more comprehensive and reliable, and being able to judge whether the water quality meets the standard more timely and accurately.

[0023] In some embodiments of the present invention, the sewage treatment system usually consists of multiple process sections, including pretreatment, biochemical treatment, advanced treatment, etc. The effluent water quality of each process section may vary significantly due to different treatment processes. Therefore, step S1 first divides the discharge path of the sewage treatment system into several effluent monitoring nodes. Specifically, the discharge path of the sewage treatment system is divided into three key monitoring nodes according to the process flow: Pretreatment node: the outlet between the grid decontamination machine and the grit chamber, equipped with an online suspended solids monitor and a pH sensor, used to capture abnormal fluctuations in the stage of removing large particle pollutants; Biochemical treatment node: set at the end of the aeration tank and the inlet weir of the secondary sedimentation tank, equipped with a dissolved oxygen probe, ammonia nitrogen analyzer and chemical oxygen demand rapid detection device to monitor the core parameters of the biodegradation process; Advanced treatment node: installed at the outlet of the disinfection contact tank, integrated with a total phosphorus online analyzer, total nitrogen detection module and residual chlorine sensor to ensure that the final effluent meets the standard; A data acquisition device is set at each effluent monitoring node to obtain the water quality characteristic parameter data of that node in real time; the setting of the monitoring nodes should cover the main process sections of the sewage treatment system to ensure that the change characteristics of the water quality can be comprehensively reflected; through multi-point monitoring, the abnormal process location can be traced more accurately, avoiding the limitation that a single monitoring point cannot locate the problem.

[0024] Furthermore, for each selected effluent monitoring node, a weight rule library for water quality characteristic parameters needs to be preset. The weight rule library is a database preset for each effluent monitoring node, used to store the weight configurations of water quality characteristic parameters under different operating conditions. The weight reflects the importance of each water quality characteristic parameter in the comprehensive evaluation of water quality under specific conditions. By dynamically adjusting the weight, the actual situation of the water quality can be reflected more accurately. The construction of the weight rule library is based on the operating conditions of the sewage treatment system, and the operating conditions mainly include the following three dimensions: Treatment water volume range: The treatment water volume of the sewage treatment plant may vary due to fluctuations in the influent volume, and is divided into low flow, medium flow, and high flow ranges; under different flow ranges, the importance of water quality characteristic parameters may be different; for example, in the high flow range, chemical oxygen demand and ammonia nitrogen concentration may be more important for water quality assessment; Influent water quality category: The influent water quality may vary due to different sources. For example, the components of industrial wastewater and domestic sewage are different; the difference in the influent water quality category will affect the weights of some water quality parameters; for example, when the influent water quality is mainly industrial wastewater, the weights of total phosphorus concentration and heavy metal content may need to be increased; Equipment load level: The operating load of the sewage treatment equipment may change due to equipment status or maintenance conditions; the equipment load level is divided into low load, normal load, and high load; when operating at high load, some water quality parameters (such as dissolved oxygen concentration) may be more sensitive to water quality assessment.

[0025] The weight configurations in the weight rule library are determined by combining historical data analysis, expert experience, and experimental verification; for example, under low flow conditions, the weights of turbidity and dissolved oxygen concentration may be higher; while under high flow conditions, the weights of chemical oxygen demand and ammonia nitrogen concentration may be higher; the construction of the weight rule library needs to fully consider the water quality change characteristics under different conditions to ensure the scientificity and rationality of the weight configuration; the weight rule library is not fixed, but can be dynamically adjusted according to the actual operation of the sewage treatment system; for example, when the effluent water quality of a certain process section is stable for a long time, the weights of some parameters can be appropriately reduced; while when the effluent water quality of a certain process section fluctuates greatly, the weights of relevant parameters can be increased to enhance the sensitivity to water quality changes.

[0026] In this embodiment, by dividing the discharge path into multiple monitoring nodes, full-coverage monitoring of the sewage treatment system is achieved, avoiding the limitations of a single monitoring point. At the same time, by presetting a weight rule library, a basis is provided for the subsequent calculation of the comprehensive water quality anomaly index, ensuring the comprehensiveness and accuracy of water quality assessment. By presetting the weight rule library, the weights of water quality parameters can be dynamically adjusted according to the current operating conditions, thus more accurately reflecting the actual situation of the water quality. The construction of the weight rule library fully considers the complex operating conditions of the sewage treatment system, including factors such as the treatment water volume, influent water quality, and equipment load, and can adapt to the water quality change characteristics under different operating conditions, improving the adaptability and reliability of water quality assessment.

[0027] In some embodiments of the present invention, in step S2, through the data acquisition devices set at each effluent monitoring node, multi-source data is obtained in real time, including a water quality characteristic parameter set, an operating state data set of the sewage treatment system, and an environmental parameter data set. Specifically, the water quality characteristic parameter set reflects the current water quality status of each monitoring node, the operating state data set reflects the internal working conditions of the sewage treatment system, and the environmental parameter data set shows the influence of external environmental factors on the system. The above data are interrelated and jointly provide a basis for subsequent water quality assessment and anomaly judgment. Among them, the water quality characteristic parameter set covers a variety of key indicators, such as pH value, turbidity, dissolved oxygen concentration, ammonia nitrogen concentration, and total phosphorus concentration, etc., which can reflect the water quality characteristics from different angles. The current treatment water volume and equipment operating parameters in the operating state data set are used to judge the system load and equipment status. The environmental temperature, humidity, and air pressure, etc. in the environmental parameter data set will affect the physical and chemical reactions and microbial activities in the sewage treatment process to varying degrees.

[0028] For example, at the pre-treatment node, if the suspended solid concentration suddenly rises significantly during a certain period, it may indicate that the grille cleaner is malfunctioning and fails to effectively intercept large particulate pollutants; an abnormal fluctuation in the pH value may be due to a sudden change in the influent water quality or a deviation in the previous treatment process. At the biochemical treatment node, if the dissolved oxygen concentration at the end of the aeration tank continuously remains below the normal range, it will affect the aerobic respiration of microorganisms, reduce the degradation efficiency of pollutants, and cause an increase in indicators such as ammonia nitrogen and chemical oxygen demand. At the advanced treatment node, if the total phosphorus online analyzer detects that the total phosphorus concentration is approaching or exceeding the discharge standard, it indicates that the phosphorus removal effect in the advanced treatment stage is poor. For the operation status data, when the sewage treatment plant is in the high-flow range, the equipment needs to treat more sewage, which may lead to an increase in equipment load and a decline in treatment effect. If the operation parameters of the equipment show that the rotation speed of a certain water pump rises abnormally at this time, it may mean that the water pump is malfunctioning, affecting the sewage lifting and transportation. For the environmental parameter data, in summer with high temperature, the microbial activity in the biochemical treatment tank increases and the reaction rate accelerates, which may lead to an intensified consumption of dissolved oxygen; while in a high-humidity environment, some electrical equipment may be affected by moisture, affecting its operation stability and indirectly affecting the water quality treatment effect.

[0029] For the collection of water quality characteristic parameters, in addition to using traditional sensors, water quality monitoring equipment based on spectral analysis can also be adopted; for example, a UV spectrophotometer can be used to measure the concentrations of multiple pollutants simultaneously, and by analyzing the absorbance at specific wavelengths, parameters such as chemical oxygen demand and ammonia nitrogen can be obtained quickly. For the collection of operation status data, in addition to directly collecting the operation parameters of the equipment, the working status information of the equipment, such as the fault alarm records and operation duration statistics of the equipment, can also be obtained through the intelligent control system of the equipment. In terms of environmental parameter collection, in addition to using conventional meteorological sensors, the Internet of Things technology can be used to obtain more accurate environmental data from surrounding meteorological monitoring stations.

[0030] In this embodiment, the multi-parameter collection of the water quality characteristic parameter set can reflect the water quality from multiple dimensions, avoid the one-sidedness of single-parameter monitoring, and more accurately evaluate the water quality status; the collection of the operation status data set helps to promptly detect the abnormal operation and load changes of the internal equipment of the sewage treatment system, and prevent water quality problems caused by equipment failures or overloaded operations in advance; the inclusion of the environmental parameter data set fully considers the influence of the external environment on the sewage treatment process, making the water quality evaluation more comprehensive; through the multi-source data collection method, rich and accurate data can be provided for subsequent determination of operation conditions, weight matching, and water quality evaluation, improving the reliability and accuracy of the real-time detection method for the effluent water quality of the entire sewage treatment, facilitating the timely discovery of water quality abnormalities and taking corresponding measures, and ensuring the stable operation of the sewage treatment system and the compliance of the effluent water quality.

[0031] In some embodiments of the present invention, the operating conditions of the sewage treatment system are complex and variable. Factors such as the treated water volume, the type of influent water quality, and the equipment load will all affect the weights of water quality characteristic parameters. By analyzing these operating conditions in real time, the system can dynamically adjust the weight configuration to ensure the accuracy and adaptability of the water quality assessment results. Step S3 accurately identifies the current operating conditions through the analysis of operation status data and selects the most suitable weight configuration from the preset weight rule library, providing a scientific basis for the subsequent calculation of the comprehensive water quality anomaly index, as follows: Treated water volume range: The treated water volume of the sewage treatment plant varies due to fluctuations in the influent volume and is usually divided into low flow, medium flow, and high flow ranges. The low flow range (<50% of the design flow) indicates a low system load, and the fluctuations in water quality characteristic parameters may be small. The weights of chemical oxygen demand and ammonia nitrogen can be appropriately reduced. For example, during low flow operation at night, the system can reduce the weight of chemical oxygen demand (such as from 0.4 to 0.3) and focus on monitoring the changes in ammonia nitrogen and dissolved oxygen. The medium flow range (50% - 80% of the design flow) indicates that the system is operating normally, and the weights of each water quality parameter can be set to default values according to historical data. For example, the weight of chemical oxygen demand is 0.4, the weight of ammonia nitrogen is 0.3, and the weight of dissolved oxygen is 0.2. The high flow range (>80% of the design flow) indicates a high system load, which may lead to a decrease in treatment efficiency, and the weights of chemical oxygen demand and ammonia nitrogen need to be increased. For example, at a sewage treatment plant, when the treated water volume reaches 90% of the design flow during the peak period, the system automatically increases the weight of chemical oxygen demand to 0.6 and the weight of ammonia nitrogen to 0.4 to more sensitively capture water quality changes. Type of influent water quality: The influent water quality varies due to different sources, and the system needs to adjust the weight configuration according to the water quality type. When the proportion of industrial wastewater is high, the weights of total phosphorus concentration and heavy metal content need to be increased. For example, at an industrial wastewater treatment plant, the influent contains a large amount of phosphate and heavy metals, and the system increases the weight of total phosphorus from 0.1 to 0.3 and the weight of heavy metals from 0.2 to 0.4. When the proportion of domestic sewage is high, the weights of nitrogen and phosphorus parameters are higher. For example, at a domestic sewage treatment plant where the influent is mainly domestic sewage, the system increases the weight of ammonia nitrogen from 0.3 to 0.5 and the weight of total phosphorus from 0.1 to 0.2. Equipment load level: The equipment load level reflects the operating status of the sewage treatment equipment. When operating at a high load, the weight of dissolved oxygen concentration needs to be increased because the aeration efficiency may decrease, affecting the aerobic respiration of microorganisms. For example, at a sewage treatment plant operating at a high load, the system increases the weight of dissolved oxygen from 0.2 to 0.4 to ensure that the degradation efficiency of microorganisms is not affected.

[0032] For example, in the high-flow range, when the system recognizes an increase in the treated water volume, it automatically increases the weights of chemical oxygen demand and ammonia nitrogen. For example, at a sewage treatment plant, when the treated water volume during the peak period reaches 90% of the designed flow rate, the system matches the weight configuration for the high-flow condition, increasing the weight of chemical oxygen demand from 0.3 to 0.5 and the weight of ammonia nitrogen from 0.2 to 0.4 to more accurately reflect the water quality changes. When the influent water quality category is industrial wastewater and the system recognizes an abnormal increase in the total phosphorus concentration, it automatically increases the weight of total phosphorus. For example, when the total phosphorus concentration in the wastewater discharged from a factory exceeds the standard, the system increases the weight of total phosphorus from 0.1 to 0.3 to ensure that the impact of total phosphorus on the comprehensive water quality assessment is more significant.

[0033] At the same time, machine learning algorithms can be used to classify the operating conditions. For example, using historical data to train a classification model to classify the operating conditions into categories such as "normal condition", "high-load condition", and "low-load condition" to automatically identify the current condition. Fuzzy logic models can also be used to perform fuzzy classification of the operating conditions. For example, taking the treated water volume, influent water quality, and equipment load as input variables, and determining the fuzzy membership degree of the current condition through fuzzy logic reasoning to select the weight configuration.

[0034] In this embodiment, the system can dynamically adjust the weight configuration according to the real-time operating state of the sewage treatment system to ensure that the water quality assessment results can adapt to the changes in different operating conditions. For example, in the high-flow condition, the system automatically increases the weights of chemical oxygen demand and ammonia nitrogen, which can more sensitively capture the water quality changes and avoid evaluation errors caused by fixed weights. Through the analysis of the operating conditions, the system can accurately match the weight configuration suitable for the current condition to ensure that the weights of the water quality characteristic parameters are consistent with their actual influence degrees. For example, in the condition with a high proportion of industrial wastewater, the system increases the weights of total phosphorus and heavy metals to more accurately reflect the water quality status. The dynamic weight adjustment can reduce the misjudgment or missed judgment problems caused by fixed weights and improve the reliability of the water quality assessment. For example, in the low-flow condition, the system reduces the weights of turbidity and dissolved oxygen to avoid abnormal alarms caused by minor fluctuations. The system can comprehensively consider multi-dimensional factors such as the treated water volume, influent water quality, and equipment load to adapt to the water quality assessment requirements under complex operating conditions. For example, in the mixed condition of high flow and industrial wastewater, the system can simultaneously increase the weights of chemical oxygen demand, ammonia nitrogen, and total phosphorus to ensure the comprehensiveness of the water quality assessment.

[0035] In some embodiments of the present invention, the water quality characteristic parameter data is deeply integrated and analyzed with the parameter weight configuration matched according to the operating conditions to generate a comprehensive water quality anomaly index corresponding to each effluent monitoring node; during the sewage treatment process, the influence degree of the water quality characteristic parameters of each monitoring node on the overall water quality changes dynamically with the operating conditions; therefore, it is necessary to associate the water quality characteristic parameters such as pH value, turbidity, dissolved oxygen concentration, ammonia nitrogen concentration, total phosphorus concentration, etc. collected in real time with the specific weight configuration determined for different operating conditions in step S3; by constructing a scientific mathematical model, the importance degree of each parameter under the current operating conditions is comprehensively weighed, and then a quantitative index that can accurately reflect the degree of deviation of the water quality of this monitoring node from the normal state is obtained; this index is not a simple stacking of parameters, but a scientific operation result based on the weight differences of each parameter, which can significantly improve the accuracy of judging water quality anomalies.

[0036] Specifically, the calculation formula of the comprehensive water quality anomaly index is designed as follows: Wherein, represents the comprehensive water quality anomaly index corresponding to the j-th effluent monitoring node, represents the real-time collected value of the i-th water quality characteristic parameter corresponding to the j-th effluent monitoring node; represents the standard limit value of the i-th water quality characteristic parameter corresponding to the j-th effluent monitoring node; represents the weight value of the i-th water quality characteristic parameter corresponding to the j-th effluent monitoring node, and the weight value is obtained by matching from the weight rule library according to the current operating conditions; n represents the total number of characteristic parameters in the water quality characteristic parameter set corresponding to the j-th effluent monitoring node; k represents a non-linear dynamic index factor, which is used to enhance the sensitivity of the exceeded standard parameters.

[0037] In this embodiment, by considering the real-time collected value, standard limit value, weight value and non-linear dynamic index factor of the water quality characteristic parameters, comprehensively covering the actual water quality situation, water quality standard requirements, the difference in parameter importance under different operating conditions and the special treatment of exceeded standard situations, it can comprehensively and systematically reflect the water quality anomaly situation; the weight value is obtained by matching from the weight rule library according to the current operating conditions; this makes the contribution degree of each water quality characteristic parameter to the comprehensive water quality anomaly index different under different operating conditions, and can more accurately reflect the real water quality situation under different operating conditions; for example, in the operating condition with a high proportion of industrial wastewater, the parameter weights related to the industrial wastewater pollution factors will increase, and correspondingly have a greater impact on the comprehensive water quality anomaly index; by introducing the non-linear dynamic index factor, it can have different degrees of influence on the calculation result according to the exceeded standard degree of the water quality characteristic parameters; for the parameters with significant exceeding of the standard, a higher amplification factor is given to highlight their impact on water quality anomalies and enhance the ability to capture water quality anomalies.

[0038] More specifically, the calculation formula of the non-linear dynamic index factor k is: ; By setting a nonlinear dynamic index factor, when the ratio of the real-time collected value of the water quality characteristic parameter to the standard limit is ≥1.2 (i.e., significantly exceeded by ≥20%), k=1.5 is used, otherwise k=1.2 is used. This can increase its influence in the calculation of the comprehensive water quality anomaly index for exceeding the standard, especially for serious exceeding of the standard, making anomaly detection more sensitive and helping to promptly discover and pay attention to serious water quality anomalies.

[0039] In some embodiments of the present invention, an environmental compensation coefficient is calculated based on environmental parameter data and operating status data, and the coefficient is used to correct the comprehensive water quality anomaly index to obtain a corrected anomaly index; the introduction of the environmental compensation coefficient is to eliminate the interference of external environmental factors on water quality assessment and ensure the reliability of water quality assessment results; environmental parameter data include ambient temperature, humidity, and air pressure, etc. These factors will affect the physical and chemical reactions and microbial activity in the sewage treatment process; operating status data reflects the actual operation of the sewage treatment system, such as the amount of water treated and equipment operating parameters; the calculation of the environmental compensation coefficient is based on a preset environmental compensation model, which is trained by a deep learning algorithm and can output a compensation coefficient based on environmental parameters and operating status data; by multiplying the comprehensive water quality anomaly index by the compensation coefficient, the corrected anomaly index can be obtained, thereby more accurately reflecting the actual condition of the water quality; specifically, the calculation formula of the environmental compensation model is as follows: ; in, Represents the energy change caused by temperature change; Indicates real-time temperature; Indicates the reference temperature; represents the temperature sensitivity coefficient; Represents the energy change caused by humidity change; Indicates real-time humidity; Indicates the humidity sensitivity coefficient; It represents the energy change caused by pressure change; Indicates real-time pressure; Indicates standard pressure; Indicates the pressure sensitivity coefficient; Indicates the dissolved oxygen activation flag, with a value of 0 or 1, indicating whether compensation is enabled; Indicates the energy change caused by the change of sewage treatment flow; Indicates real-time sewage treatment flow; Indicates the maximum flow rate; Indicates rated flow; represents the flow sensitivity coefficient; represents the energy change caused by the equipment load level; represents the real-time equipment load level, such as the percentage of the aerator power; represents the equipment load level sensitivity coefficient; represents the environmental compensation coefficient; , , , and respectively represent the normalization functions of temperature, humidity, pressure, sewage treatment flow rate, and equipment load level; After that, multiply the comprehensive water quality anomaly index calculated in step S4 by the environmental compensation coefficient to obtain the corrected anomaly index, so as to more accurately reflect the actual situation of the water quality at this effluent monitoring node.

[0040] In this embodiment, various environmental parameter data such as environmental temperature, humidity, and air pressure are incorporated into the calculation; temperature changes will affect the microbial activity and the chemical reaction rate. For example, at low temperatures, the microbial activity decreases and the sewage treatment efficiency drops; humidity changes may affect the equipment operating environment and biochemical reaction conditions at high humidity; air pressure changes will act on reactions involving gas exchange, such as the acquisition of dissolved oxygen, etc.; comprehensively considering the above environmental factors can more accurately reflect the comprehensive impact of the external environment on sewage treatment; the operating state factors cover operating state data such as the treatment water volume and equipment operating parameters; changes in the treatment water volume will affect the water flow velocity, residence time, etc., and then change the treatment effect; different equipment load levels, such as changes in the percentage of the aerator power, will directly affect the oxygen supply conditions for microorganisms and the biochemical reaction process; comprehensively considering these operating state factors can accurately grasp the impact of the actual internal operation of the sewage treatment system on water quality; sensitivity coefficients are set for different influencing factors, such as temperature sensitivity coefficient, humidity sensitivity coefficient, etc., which can be adjusted and optimized according to the actual situation of the sewage treatment plant, regional characteristics, equipment characteristics, etc., so that the model can better adapt to different scenarios and improve the calculation accuracy; for example, in cold regions, the temperature sensitivity coefficient can be appropriately increased to more prominently reflect the impact of temperature on water quality; for the calculation of energy changes caused by humidity changes, a humidity condition judgment (H≥80%) is set; when the humidity reaches a certain level, the corresponding calculation is carried out, otherwise it is 0, which can avoid unnecessary calculations when the humidity impact is small and accurately capture the significant impact that high humidity may have on water quality; by setting the normalization function, the energy changes caused by different influencing factors are normalized; since the dimensions and numerical ranges of different factors are different, such as temperature, flow rate, equipment load level, etc., the normalization process can unify these factors to the same calculation scale, avoid calculation deviations caused by dimensional differences, and ensure the scientificity and accuracy of the calculation of the environmental compensation coefficient.

[0041] In some embodiments of the present invention, the corrected abnormal index after environmental compensation correction is compared with a preset water quality compliance threshold; if the corrected abnormal index exceeds the set threshold, it indicates that the current water quality condition does not meet the expected standard, and it is necessary to trigger an over-standard warning and locate the specific abnormal effluent monitoring node; conversely, if the corrected abnormal index is within the threshold range, it means that the water quality meets the requirements, and the system will continue to execute steps such as data collection and abnormal index calculation; the water quality compliance threshold is set based on national or local environmental protection regulations, industry standards or the specific process requirements of the sewage treatment plant; the water quality compliance threshold not only considers the safety limits of water quality characteristic parameters, but also combines the dynamic adjustment requirements under actual operating conditions to ensure that the evaluation results are both strict and reasonable.

[0042] For example, assume that during the peak summer period of a certain sewage treatment plant, after the calculations in steps S4 and S5, the corrected abnormal index obtained is 11.0; according to the water quality compliance threshold set for this plant being 10.0 (assumed), the corrected abnormal index exceeds the threshold; then the system triggers an over-standard warning and, by analyzing the data of each effluent monitoring node, locates the specific abnormal point. For example, it is found that the dissolved oxygen concentration at the end of the aeration tank continues to be lower than the normal range, resulting in an increase in ammonia nitrogen and chemical oxygen demand indicators, affecting the overall water quality.

[0043] In this embodiment, the corrected abnormal index combines the influence of the environmental compensation coefficient to more accurately reflect the actual water quality condition; by comparing with the preset water quality compliance threshold, it can effectively avoid misjudgment or missed reporting caused by simply relying on the original water quality data, and improve the reliability of the warning system; an accurate over-standard warning mechanism helps to timely discover and solve problems, reducing fines and other economic losses caused by non-compliant water quality; at the same time, by accurately locating the abnormal effluent monitoring node, targeted measures can be taken to avoid waste of resources caused by blind investigation; according to the change trend of the corrected abnormal index, managers can more targeted adjust resource allocation and operation strategies; for example, increasing the aeration volume during high-temperature periods to maintain good water quality treatment effects, or taking heat preservation measures under low-temperature conditions to ensure the activity of microorganisms, so as to achieve the optimal allocation of resources.

[0044] In some solutions, multiple embodiments of the present application can be combined, and the combined solution can be implemented. Optionally, some operations in the processes of the method embodiments are optionally combined, and / or the order of some operations is optionally changed. Moreover, the execution order between the steps of each process is only exemplary and does not constitute a limitation on the execution order between the steps. The steps can also be in other execution orders. It is not intended to indicate that the execution order is the only order in which these operations can be performed. Those of ordinary skill in the art will think of various ways to reorder the operations described herein. Additionally, it should be noted that the process details involved in a certain embodiment herein are equally applicable to other embodiments in a similar manner, or different embodiments can be used in combination.

[0045] In addition, some steps in the method embodiments can be equivalently replaced with other possible steps. Or, some steps in the method embodiments can be optional and can be deleted in some usage scenarios. Or, other possible steps can be added to the method embodiments. Moreover, the method embodiments can be implemented separately or in combination.

[0046] As Figure 2 shown, the present invention also provides a real-time detection system for the effluent quality of sewage treatment, which specifically includes the following modules; The monitoring node module is used to divide the discharge path of the sewage treatment system into at least two effluent monitoring nodes, and preset a weight rule library for the water quality characteristic parameters for each effluent monitoring node; the weight rule library is used to store the weight configurations of each water quality characteristic parameter under different operating conditions; The data acquisition module is used to collect in real time the water quality characteristic parameter set, the operating state data set of the sewage treatment system, and the environmental parameter data set of each effluent monitoring node; The operating condition analysis module is used to determine the current operating condition according to the operating state data of the sewage treatment system, and match the corresponding parameter weight configuration in the weight rule library based on the current operating condition; The abnormal index calculation module is used to perform weighted calculation on the water quality characteristic parameter data and the matched parameter weight configuration to generate a water quality comprehensive abnormal index corresponding to each effluent monitoring node; the water quality comprehensive abnormal index is used to characterize the water quality abnormal level of the current monitoring node; The compensation module is used to input the environmental parameter data and the operating state data into a preset environmental compensation model, output an environmental compensation coefficient, and compensate and correct the water quality comprehensive abnormal index according to the environmental compensation coefficient to obtain a corrected abnormal index, and the corrected abnormal index is used to reflect the water quality abnormal level considering environmental factors; An alarm module for comparing the corrected abnormal index with a preset water quality compliance threshold; if the corrected abnormal index exceeds the water quality compliance threshold, an over-standard warning is triggered and the abnormal effluent monitoring node is located; otherwise, the data collection and abnormal index calculation steps are continuously executed.

[0047] In this embodiment, by monitoring multiple nodes to cover the entire discharge path of the sewage treatment system, the abnormal process position can be accurately located, and the efficiency of abnormal troubleshooting can be improved; by dynamically adjusting the weights of water quality characteristic parameters according to the operating conditions of the sewage treatment system, the water quality change characteristics under different operating conditions can be adapted, and the accuracy of water quality assessment can be improved; by correcting the influence of environmental factors on water quality through an environmental compensation model, external interference can be eliminated, and the reliability of detection results can be improved; the water quality condition can be judged in real time, and a warning can be quickly triggered when the standard is exceeded, and the abnormal node can be located at the same time, providing support for taking treatment measures in time; through continuous optimization of dynamic weight adjustment and environmental compensation model, it has strong adaptability; at the same time, the system supports the expansion of monitoring nodes and is applicable to sewage treatment systems of different scales.

[0048] In this embodiment, the functional modules are divided according to the above method examples. For example, each functional module can be corresponding to each function, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware. It should be noted that the division of modules in this embodiment is illustrative, only a logical function division, and there can be other division methods in actual implementation.

[0049] The various change methods and specific embodiments of the sewage treatment effluent water quality real-time detection method in the foregoing embodiments are equally applicable to the sewage treatment effluent water quality real-time detection system in this embodiment. Through the foregoing detailed description of the sewage treatment effluent water quality real-time detection method, those skilled in the art can clearly know the implementation method of the sewage treatment effluent water quality real-time detection system in this embodiment. Therefore, for the sake of simplicity of the specification, it will not be elaborated here.

[0050] The above shows and describes 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 by the above embodiments. What is described in the above embodiments and the specification only illustrates the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A real-time detection method for the effluent quality of sewage treatment, characterized in that, The method is applied to a sewage treatment system, and the method includes: Dividing the discharge path of the sewage treatment system into at least two effluent monitoring nodes, and presetting a weight rule library for water quality characteristic parameters for each of the effluent monitoring nodes; Collecting in real time the water quality characteristic parameter sets of each of the effluent monitoring nodes, the operating state data set of the sewage treatment system, and the environmental parameter data set; Determining the current operating condition of the sewage treatment system according to the operating state data, and matching the corresponding parameter weight configuration in the weight rule library accordingly; Performing weight analysis on the water quality characteristic parameter data and the parameter weight configuration to generate a water quality comprehensive anomaly index corresponding to the effluent monitoring node; Inputting the environmental parameter data and the operating state data into a preset environmental compensation model, outputting an environmental compensation coefficient, and compensating and correcting the water quality comprehensive anomaly index with this to obtain a corrected anomaly index; Comparing the corrected anomaly index with a preset water quality compliance threshold: If the corrected anomaly index exceeds the threshold, trigger an over-standard warning and locate the abnormal effluent monitoring node; otherwise, continuously execute the data collection and anomaly index calculation steps.

2. The real-time detection method for the water quality of the treated sewage according to claim 1, characterized in that, The weight rule library contains parameter weight configurations corresponding to different operating conditions, and the operating conditions include the treatment water volume range, the influent water quality category, and the equipment load level.

3. The real-time detection method for the water quality of the effluent from sewage treatment according to claim 2, wherein The water quality characteristic parameter set includes at least any two of the pH value, turbidity, dissolved oxygen concentration, ammonia nitrogen concentration, and total phosphorus concentration; The operating state data set includes at least the current treatment water volume and equipment operating parameters; The environmental parameter data set includes at least the environmental temperature, environmental humidity, and environmental air pressure.

4. The real-time detection method for the effluent quality of sewage treatment according to claim 3, characterized in that The calculation formula of the water quality comprehensive anomaly index is: ； Among them, represents the comprehensive water quality anomaly index corresponding to the j-th effluent monitoring node, represents the real-time acquisition value of the i-th water quality characteristic parameter corresponding to the j-th effluent monitoring node; represents the standard limit value of the i-th water quality characteristic parameter corresponding to the j-th effluent monitoring node; represents the weight value of the i-th water quality characteristic parameter corresponding to the j-th effluent monitoring node, and the weight value is obtained by matching from the weight rule library according to the current operating conditions; n represents the total number of characteristic parameters in the water quality characteristic parameter set corresponding to the j-th effluent monitoring node; k represents the non-linear dynamic index factor, which is used to enhance the sensitivity of the exceeded standard parameters.

5. The real-time detection method for the effluent quality of sewage treatment according to claim 4, characterized in that, The calculation formula of the non-linear dynamic index factor k is: ； By setting the non-linear dynamic index factor, when the ratio of the real-time acquisition value of the water quality characteristic parameter to the standard limit value ≥ 1.2, k = 1.5 is adopted, otherwise k = 1.2 is adopted.

6. The real-time detection method for the water quality of the treated sewage according to any one of claims 1-5, characterized in that The calculation formula of the environmental compensation model is: ; Among them, represents the energy change caused by temperature change; represents the real-time temperature; represents the reference temperature; represents the temperature sensitivity coefficient; Indicates the energy change caused by humidity change; Indicates the real-time humidity; Indicates the humidity sensitivity coefficient; Indicates the energy change caused by the pressure change; Indicates the real-time pressure; Indicates the standard pressure; Indicates the pressure sensitivity coefficient; Indicates the dissolved oxygen activation flag, with a value of 0 or 1, indicating whether compensation is enabled; Indicates the energy change caused by the change in the sewage treatment flow rate; Indicates the real-time sewage treatment flow rate; Indicates the maximum flow rate; Indicates the rated flow rate; Indicates the flow rate sensitivity coefficient; Indicates the energy change caused by the equipment load level; Indicates the real-time equipment load level, such as the percentage of aerator power; Indicates the equipment load level sensitivity coefficient; represents the environmental compensation coefficient; , , , and respectively represent the standardized functions of temperature, humidity, pressure, sewage treatment flow rate, and equipment load level.

7. The real-time detection method for the effluent quality of sewage treatment according to claim 6, characterized in that Presetting a weight rule library for water quality characteristic parameters for each of the effluent monitoring nodes, including: Based on the historical operation data of the sewage treatment system, analyzing the water quality change characteristics of different treatment process sections, and determining the influence weights of each water quality characteristic parameter on the water quality under different operating conditions; According to the influence weights, constructing a weight rule library using a machine learning algorithm, and the machine learning algorithm includes at least one of support vector machine, random forest, or gradient boosting tree.

8. The real-time detection method for the effluent quality of sewage treatment according to claim 7, characterized in that, Collecting in real time the water quality characteristic parameter sets of each effluent monitoring node, the operating state data set of the sewage treatment system, and the environmental parameter data set, including: Adopting a distributed sensor network, deploying multiple types of sensors at each of the effluent monitoring nodes, and the sensors include at least two of optical sensors, electrochemical sensors, and microbial sensors; By interacting with the automatic control system and environmental monitoring system of the sewage treatment system, obtaining the operating state data and environmental parameter data in real time, and performing real-time verification and cleaning on the collected data to remove outliers and noise data.

9. The real-time detection method for the water quality of the effluent from sewage treatment according to claim 7, characterized in that Determining the current operating condition of the sewage treatment system according to the operating state data, and matching the corresponding parameter weight configuration in the weight rule base accordingly, including: Performing feature extraction and feature engineering on the operating state data to construct a feature vector reflecting the operating state of the sewage treatment system; Using a clustering algorithm to divide the feature vector into different operating condition categories, and the clustering algorithm includes at least one of K-Means clustering, DBSCAN clustering, and Gaussian mixture model clustering; According to the current operating condition category, matching the corresponding parameter weight configuration in the weight rule base.

10. A real-time detection system for the effluent quality of sewage treatment, characterized in that, Including: A monitoring node module for dividing the discharge path of the sewage treatment system into at least two effluent monitoring nodes, and presetting a weight rule base for water quality characteristic parameters for each effluent monitoring node; A data acquisition module for real-time collecting the water quality characteristic parameter set of each effluent monitoring node, the operating state data set of the sewage treatment system, and the environmental parameter data set; An operating condition analysis module for determining the current operating condition according to the operating state data of the sewage treatment system, and matching the corresponding parameter weight configuration in the weight rule base based on the current operating condition; An abnormal index calculation module for performing weighted calculation on the water quality characteristic parameter data and the matching parameter weight configuration to generate a water quality comprehensive abnormal index corresponding to each effluent monitoring node; A compensation module for inputting the environmental parameter data and the operating state data into a preset environmental compensation model, outputting an environmental compensation coefficient, and compensating and correcting the water quality comprehensive abnormal index according to the environmental compensation coefficient to obtain a corrected abnormal index; An alarm module for comparing the corrected abnormal index with a preset water quality compliance threshold; If the corrected abnormal index exceeds the water quality compliance threshold, triggering an over-standard warning and locating the abnormal effluent monitoring node; otherwise, continuously executing the data acquisition and abnormal index calculation steps.

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