Sewage monitoring and early warning method and system

Abstract

The invention discloses a sewage monitoring and early warning method and system, and aims to solve the problems of lagging and high misjudgment rate of traditional monitoring. The multi-modal acquisition module acquires water quality parameters, equipment states and pipe network data in real time through a distributed sensor network, a timestamp is calibrated through linear interpolation of a dynamic analysis engine, spatial features are extracted through concentration gradient calculation, and data are cleaned in combination with Kalman filtering and an isolated forest algorithm. And the intelligent analysis and decision module mines time sequence features based on the LSTM model and outputs pollution level indexes. And the grading response execution module triggers early warning according to three-level threshold values: a first-level APP pushes data, a second-level superposition short message and a third-level telephone alarm and links equipment, a standby purification device is started within 10 seconds, and a WebGL visual diffusion report is synchronously generated. The system constructs an acquisition-analysis-decision-response closed loop which is suitable for industrial and urban pipe network scenes.

Description

Technical Field

[0001] The present invention belongs to the technical field of sewage treatment and intelligent monitoring, and particularly relates to a sewage monitoring and early warning method and system. Background Art

[0002] In the field of sewage treatment monitoring, traditional solutions often use sensor networks combined with fixed data acquisition terminals to build early warning systems based on periodic sampling of physical indicators such as water turbidity and dissolved oxygen. This model uses industrial control PLCs as the core data transmission architecture, which can achieve routine detection of basic pollutants, but there is a lag of 15-30 minutes, making it difficult to capture the instantaneous peaks of sudden increases in pollutants. In recent years, although some systems have introduced STM32-based embedded coprocessors to capture key parameters such as ammonia nitrogen and total phosphorus in near real time through distributed sensor nodes, the data throughput efficiency of the industrial bus protocol is limited, and the frequency of data synchronization between the on-site control unit and the cloud analysis platform is low, which can easily lead to early warning vacuums when responding to incidents such as the illegal discharge of industrial wastewater.

[0003] Existing patented technologies often focus on monitoring pollutant concentrations while neglecting toxicity assessment. While some systems utilize cyclops biosensors for acute toxicity testing, their bioactivity maintenance modules often require frequent consumable replacement, significantly shortening the biofilm's shelf life under high-temperature and high-humidity conditions, impacting the reliability of monitoring data. As technology evolves, early Zigbee wireless networking solutions are being replaced by multi-protocol gateways that integrate LoRa narrowband communications, enhancing signal penetration. Academics have achieved breakthroughs in data fusion algorithms, such as integrating the XGBoost optimization algorithm into FPGA chips to create intelligent edge computing units capable of simultaneously analyzing the spatiotemporal correlations of multiple water quality parameters. However, existing technologies face bottlenecks in building dynamic prediction models. Most monitoring devices still rely on fixed thresholds to trigger alarms, which can easily misjudge pollutants with cumulative concentration effects. Furthermore, industry attempts to integrate machine vision technology to calculate water toxicity indices are facing pressures from hardware costs and algorithm power consumption, hindering commercial adoption.

[0004] The current technological breakthrough faces three major challenges: First, there is a contradiction between real-time data transmission and analytical accuracy. Although high sampling frequency increases the probability of capturing pollution peaks, it results in a low proportion of valid data. Second, the decay of the lifespan of bioactive carriers restricts the continuity of online monitoring. Although the latest carbon nanotube immobilized enzyme technology extends the service life of biosensors, the initial response sensitivity is still affected by temperature fluctuations. Third, there are technical barriers to the transplantation and adaptation of deep learning models on embedded devices. The accuracy of existing lightweight networks for identifying new organic pollutants on embedded platforms is lower than that of laboratory environmental data. These factors result in a high overall underreporting rate for existing monitoring systems when responding to sudden complex pollution events, and a surge in the frequency of false alarms under pipe network overflow conditions during rainstorms, highlighting the imbalance between algorithm robustness and hardware reliability in traditional technical frameworks. Summary of the Invention

[0005] The purpose of the present invention is to propose a sewage monitoring and early warning method and system, which is aimed at the intelligent collection and dynamic early warning of sewage water quality parameters and equipment operating status, and is suitable for scenarios such as industrial sewage treatment and urban drainage network monitoring.

[0006] In one aspect, the present invention provides a sewage monitoring and early warning method, comprising the following steps: A sewage monitoring and early warning method comprises the following steps: S1: Real-time collection of multi-source sewage monitoring data through a distributed sensor network. The multi-source sewage monitoring data includes water quality parameters, equipment operating status, and pipe network environment information; S2: Performing dynamic data analysis on the multi-source sewage monitoring data to generate structured monitoring data, wherein the dynamic data analysis includes performing time series alignment processing and spatial correlation feature extraction on the multi-source sewage monitoring data; S3: Input the multi-source feature vectors corresponding to the multi-source sewage monitoring data into the pre-trained pollution assessment model to perform pollution assessment operations, output the target pollution degree index, and judge the cumulative effect of pollutants through dynamic thresholds; S4: Based on the comparison results between the target pollution level index and the preset threshold, a graded warning signal is triggered and corresponding emergency response operations are performed.

[0007] In one aspect, the present invention provides a sewage monitoring and early warning system, comprising: Multimodal data acquisition module: used to collect water quality parameters, equipment operating status and pipe network environment information in real time through a distributed sensor network; Dynamic parsing engine: used for time series alignment, spatial correlation feature extraction and data cleaning of multi-source data; Intelligent analysis and decision-making module: A pollution assessment model built based on the LSTM neural network outputs the target pollution level index; Graded response execution module: triggers differentiated early warning signals according to pollution levels and performs emergency response operations.

[0008] Compared with the existing technology, the sewage monitoring early warning and system proposed by the present invention has the following beneficial effects: 1. Multimodal data acquisition enables comprehensive real-time monitoring: Through the collaboration of a distributed sensor network and an operations and maintenance monitoring platform, water quality parameters, equipment operating status, and pipe network environmental information are collected in real time, building a three-dimensional monitoring system covering multi-dimensional indicators. The embedded sensor array supports high-frequency data acquisition, accurately capturing fluctuations in pollutant concentrations and abnormal equipment conditions, significantly shortening the response time to pollution incidents. Combining narrowband communication with an edge computing architecture enables near-real-time data synchronization, effectively addressing the monitoring lag and early warning gaps in traditional solutions and providing strong support for the timely detection of sudden pollution incidents.

[0009] 2. Dynamic Parsing and Feature Extraction Optimize Data Value: The dynamic parsing engine calibrates multi-source sensor data using time series alignment technology to ensure temporal and spatial consistency. It also deeply explores the spatial correlation characteristics of pollutant concentrations and establishes a pollutant migration model within the pipeline network, significantly improving the accuracy of pollution tracing. Furthermore, advanced algorithms are used to clean the raw data, filter noise, and fill missing values, significantly improving data quality and providing a reliable data foundation for subsequent intelligent analysis. This addresses the data fragmentation and insufficient spatial analysis capabilities of traditional solutions.

[0010] 3. Intelligent decision-making and tiered response improve early warning effectiveness: A feature database is built based on historical pollution events, and an anomaly detection module is trained using machine learning algorithms to achieve dynamic threshold determination for pollutants with cumulative concentration effects, significantly improving detection sensitivity and reducing the risk of missed reports. Combining the pipeline network topology with the real-time operating status of equipment, intelligent algorithms automatically match the optimal emergency response plan and generate geographically tagged work orders, significantly improving the efficiency of emergency resource allocation, reducing manual intervention costs, and achieving a seamless intelligent transition from pollution identification to decision-making and execution.

[0011] 4. Closed-loop control and modular design enhance system efficiency: A pollution level classification warning mechanism is established, linking equipment automatic control, personnel alarm notification, and visual diffusion simulation to form a "monitoring-analysis-response" closed-loop control system, significantly improving the efficiency of handling abnormal events. The system adopts a modular design, supports plug-and-play of sensors and monitoring units, is compatible with multiple industrial protocols, and can be seamlessly integrated into existing sewage treatment systems, effectively reducing system upgrade costs. Through the innovative combination of pure physical and chemical sensors and machine learning, it breaks through the bottlenecks of traditional biosensors such as short lifespan and difficult model transplantation, and can still maintain stable and reliable operation under complex working conditions, providing efficient pollution monitoring and early warning guarantees for urban pipeline networks, industrial treatment plants and other scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0013] Figure 1 It is a schematic diagram of the overall architecture of the system of the present invention; Figure 2 It is a schematic diagram of the data collection process in one embodiment of the present invention; Figure 3 This is a schematic diagram of a dynamic data parsing process in one embodiment of the present invention; Figure 4 is a schematic diagram of constructing a pollution assessment model in one embodiment of the present invention; Figure 5 is a schematic diagram of hierarchical warning and emergency response in one embodiment of the present invention; Figure 6 is a detailed schematic diagram of a multimodal data acquisition module in one embodiment of the present invention; Figure 7 is a schematic diagram of the dynamic parsing engine architecture in one embodiment of the present invention; Figure 8 This is a schematic diagram of the process of generating an emergency response work order in one embodiment of the present invention; Figure 9 is a schematic diagram of system data flow in one embodiment of the present invention; Figure 10 FIG. 1 is a schematic diagram of a device control and notification mechanism in an embodiment of the present invention. DETAILED DESCRIPTION

[0014] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0015] First, the formulas used in the embodiments of the present technical solution are given and numbered for easy reference in the subsequent description, specifically including: 1. Calculation of spatial concentration gradient, formula (1): in, :Pipeline network node The pollutant concentration gradient vector at is used to describe the spatial variation trend and direction of pollutant concentration; :Pipeline network node The pollutant concentration at the location, such as ammonia nitrogen concentration, total phosphorus concentration, etc., is calculated by Figure 3 The data is collected by the water quality sensor array. 、 : The horizontal and vertical spacing between adjacent nodes in the pipe network topology, the numerical information is derived from Figure 7 The pipe network topology library in .

[0016] Furthermore, in the process of extracting spatial correlation features in the dynamic data analysis phase, Figure 10 The three-dimensional pipe network model shown in FIG (simplified to a two-dimensional plane) is used to calculate the pollutant concentration gradient at each node of the pipe network using formula (1).

[0017] Furthermore, it is closely related to the “spatial correlation feature extraction” in this technical solution and is a key mathematical tool for analyzing the diffusion law of pollutants in the pipe network. At the same time, based on the gradient calculation results, the concentration and pipe network transportation capacity (such as node flow) are further established. ) to provide data support for subsequent pollution assessment models.

[0018] Furthermore, the direction of the concentration gradient vector in this technical solution represents the direction of pollutant diffusion, and its modulus reflects the severity of the change in pollutant concentration. By calculating this gradient, it is possible to clearly understand the diffusion trend of pollutants in the pipe network, such as the direction of pollutant diffusion and the speed of diffusion.

[0019] Furthermore, the present technical solution is applied to Figure 4 In the spatial correlation feature extraction process shown in the figure, the extracted concentration gradient features are used to support Figure 10 The construction of the pollutant diffusion model provides a basis for the system to judge the diffusion path and impact range of pollutants, and then assists in locating pollution sources and predicting pollution trends.

[0020] Furthermore, in accordance with the concept of gradient in mathematics in this technical solution, in two-dimensional space, the concentration change rate at the node can be approximated by calculating the concentration difference between adjacent nodes in the horizontal and vertical directions. and The purpose is to normalize the concentration change and obtain a standardized concentration gradient vector.

[0021] 2. LSTM (Long Short-Term Memory) is a special recurrent neural network (RNN) designed specifically to address the long-term dependency issues (such as vanishing gradients and exploding gradients) that traditional RNNs face when processing long time series data. By introducing memory cells (cell states) and gating mechanisms, LSTM can effectively capture long-term patterns in time series and selectively retain or forget information. In sewage monitoring systems, it is used to dynamically analyze multi-source sensor data, generate accurate pollution warnings, and significantly improve the real-time and robustness of the system. LSTM state update equation, formula (2): in, : Forget gate output, the value range is between 0 and 1, used to control the cell state at the previous moment the degree of information retention in : sigmoid activation function, which maps the input to between 0 and 1 and is used to calculate the activation values of the forget gate, input gate, and output gate; : The weight matrix of the forget gate is used to perform weighted processing on the input information; : Hide the previous state and the current input feature vector Perform splicing; : Bias vector of forget gate; : The cell state at the current moment, used to store long-term information; : Element-wise multiplication operator; : The cell state at the previous moment; : Indicates the degree of retention of new information at the current moment; : The candidate cell state at the current moment is calculated by the current input and the hidden state at the previous moment, and is used to update the cell state; : The hidden state at the current moment, used to output the feature information of the current moment and serve as one of the inputs of the next moment; : Hyperbolic tangent activation function, which maps the input to between -1 and 1 and is used to process the cell state to obtain the hidden state.

[0022] Furthermore, in this technical solution, during the construction of the pollution assessment model, an LSTM neural network is used to learn and train historical pollution event data. Through the aforementioned state update equation, the LSTM network continuously updates the cell state and hidden state, thereby learning the time-dependent characteristics and changing patterns of the pollution data.

[0023] Furthermore, the "machine learning algorithm training" part of this technical solution is the core algorithm for building the pollution assessment model. Depend on Figure 3 The water quality parameters, equipment status and Figure 7 The pollution level index output is used to Figure 6 The hierarchical warning process in .

[0024] Furthermore, in this technical solution, the forget gate Determines the degree of forgetting the cell state information at the previous moment. When it is close to 1, most of the information of the previous moment is retained; when When it approaches 0, most of the information from the previous moment is forgotten. By combining the cell state at the previous moment and the candidate cell state at the current moment, effective storage and updating of long-term information can be achieved. As the feature output at the current moment, it can reflect the key information of the current pollution status.

[0025] Furthermore, in this technical solution, based on Figure 5 The LSTM model is trained based on the data in the "Historical Pollution Event Feature Database". The trained model can output the pollution level based on the multi-source data collected in real time. , providing a basis for the system to make graded warnings, for example, when When the set threshold is exceeded, the corresponding level of early warning response is triggered.

[0026] Furthermore, in this technical solution, the LSTM solves the vanishing and exploding gradient problems of traditional recurrent neural networks (RNNs) through the design of forget gates, input gates, and output gates, effectively processing the temporal dependencies in long sequences of data. The forget gate controls the retention and forgetting of information, the input gate controls the input of new information, and the output gate controls the output of cell state information. Through this mechanism, the LSTM can learn the complex changing patterns of contamination data over time, enabling accurate assessment and prediction of contamination status.

[0027] Example 1: Multi-source sewage monitoring data collection example. In one embodiment of the present invention, schematically, this embodiment proposes solutions to the problems existing in the prior art, such as the single dimension of sewage monitoring data, insufficient real-time performance, and poor sensor coordination.

[0028] Figure 1(Overall system architecture diagram), the multimodal data acquisition module (U1) serves as the front-end entrance of the system, obtains raw data through the distributed sensor network, and passes through the dynamic analysis engine (U2), the intelligent analysis and decision module (U3) to the hierarchical response execution module (U4), forming a closed-loop control ( Figure 1 ,U1). Figure 3 As shown in the figure, the multimodal data acquisition module includes a water quality sensor array (U11), an equipment health monitoring unit (U12), an edge computing gateway (U13), a data transmission submodule (U14), and an operation and maintenance platform (U15). Each component is connected to the LoRa / NB-IoT network via the RS485 / Modbus protocol to achieve real-time aggregation of multi-source data ( Figure 3 ,U11-U15).

[0029] Figure 6 (Details of the multimodal data acquisition module) The hardware architecture is shown in detail. The water quality sensor array (U11) adopts a multi-parameter integrated design, integrating detection units such as pH, dissolved oxygen (DO), ammonia nitrogen, and total phosphorus. The pH electrode is made of glass membrane material. The dissolved oxygen sensor is based on the principle of fluorescence quenching (polytetrafluoroethylene membrane), is resistant to chemical corrosion, and has a response time of less than 30 seconds. The equipment health monitoring unit (U12) integrates a MEMS three-axis accelerometer (silicon-based microelectromechanical structure, operating temperature -40°C to 85°C) and a current sensor. The data transmission submodule (U14) supports dual-mode communication, reflecting the modularity and environmental adaptability of hardware deployment ( Figure 6 ,U1-U14).

[0030] In one embodiment of the present invention, the multi-source data acquisition process includes the following core steps: S1: Real-time acquisition of multi-source sewage monitoring data, including water quality parameters, equipment operating status and pipe network environment information, through a multimodal data acquisition module (U1); S2: Transmitting the raw data to the operation and maintenance monitoring platform through a distributed communication network for preliminary preprocessing; S3: Outputting standardized data to a dynamic parsing engine (U2) to provide a basic data source for subsequent spatiotemporal alignment and feature extraction.

[0031] Furthermore, in step S1, the operating subject is a multimodal data acquisition module (U1), which includes a water quality sensor array (U11) and an equipment health monitoring unit (U12) deployed at key nodes in the pipe network. The water quality sensor array (U11) integrates detection units such as pH, dissolved oxygen (DO), ammonia nitrogen, and total phosphorus. The multimodal data acquisition module (U1) obtains multi-source sewage monitoring data in real time, including: water quality parameters (pH, dissolved oxygen, ammonia nitrogen, and total phosphorus concentrations, with a sampling frequency of ≥1Hz); equipment operating status (pump current, vibration amplitude, valve opening); and pipe network environmental information (node flow, pressure, and pipe diameter parameters, with a data update period of ≤10 seconds).

[0032] Furthermore, in step S2, the raw data is transmitted to the operation and maintenance monitoring platform (U15) through the LoRa / NB-IoT dual-mode data transmission submodule (U14) for preliminary preprocessing, and the transmission delay is ≤5 seconds; the default calibration period of the water quality sensor is 24 hours. If the deviation between the measured values and the historical average for three consecutive times is greater than 5%, the standard solution (pH=7.01, DO=8.5mg / L) is automatically aspirated for zero point and slope calibration, and the calibration error is controlled within ±0.1pH (DO±0.5mg / L).

[0033] Furthermore, in step S3, structured data including timestamps, node coordinates, and measurement values are output to the dynamic parsing engine (U2), providing a basic data source for subsequent spatiotemporal alignment and feature extraction.

[0034] Furthermore, in step S4, a graded warning signal is triggered and emergency response operations are performed based on the comparison results of the target pollution level index and the preset threshold: the first-level warning is pushed through the APP real-time data link ( Figure 10 ,A42), the second-level warning adds SMS notifications containing the coordinates and levels of pollution sources ( Figure 10 ,A42), Level 3 warning triggers a telephone alarm with disposal suggestions ( Figure 10 ,A42); simultaneously execute emergency response, including starting backup purification equipment within 10 seconds ( Figure 10 ,A41), Generate visual pollution diffusion report based on WebGL technology ( Figure 10 ,A43), dynamically update the pipe network status database ( Figure 10 ,A44, and transmit the device control results and pipe network status change data back through the feedback link to adaptively adjust the sensor sampling frequency or algorithm parameter threshold ( Figure 9 ,P1 closed-loop processing architecture).

[0035] The multimodal data acquisition module (U1) of this embodiment serves as the front-end entrance of the system ( Figure 1 ,U1), consists of hardware layer, transmission layer and pre-processing layer. The hardware layer includes water quality sensor array (U11), equipment health monitoring unit (U12) and pipe network detection device (such as pressure sensor, flow sensor), which realizes the on-site collection of multi-dimensional physical quantities; the transmission layer aggregates and converts the scattered sensor data into a standard format through the edge computing gateway (U13) and the data transmission submodule (U14); the pre-processing layer is integrated into the operation and maintenance monitoring platform (U15), completing the fusion storage and preliminary quality verification of multi-source data ( Figure 3 ,U11-U15).

[0036] The dynamic analysis engine (U2) is connected to the acquisition module (U1) through a standardized interface. The former receives the structured data output by the latter, which includes timestamps, node coordinates, and measurement values. Figure 1 ,U1→U2).

[0037] The pH electrode in the water quality sensor array (U11) uses a glass membrane, while the dissolved oxygen sensor utilizes a polytetrafluoroethylene membrane based on the fluorescence quenching method. These sensors are chemically resistant and have a response time of less than 30 seconds. The vibration sensor utilizes a MEMS triaxial accelerometer, made of a silicon-based microelectromechanical structure and supporting an operating temperature range of -40°C to 85°C. The LoRa module in the transmission layer uses the SX1278 chip, while the NB-IoT module uses the BC95 chip, ensuring stable operation in industrial environments ranging from -40°C to 60°C. The system also complies with the IEC61000-4-3 standard for electromagnetic compatibility, with a radio frequency interference immunity of 30V / m.

[0038] This embodiment uses a multimodal data acquisition module (U1) to achieve "collection of multi-source sewage monitoring data, including water quality parameters, equipment operating status, and pipe network environment information." "Real-time acquisition of physical and chemical indicator data by a distributed sensor network" corresponds to the deployment of a water quality sensor array (U11) and an equipment health monitoring unit (U12), and "equipment operating parameters of the operation and maintenance monitoring platform" corresponds to the preprocessing functions of the edge computing gateway (U13) and the operation and maintenance platform (U15). Figure 1 、 3 ,6’s hardware architecture and process design provide visual support for the implementation plan, ensuring the feasibility and traceability of the technical solution.

[0039] Example 2: Dynamic data analysis and spatiotemporal alignment example; In one embodiment of the present invention, schematically, this embodiment proposes a solution to the problems of spatiotemporal analysis errors and ambiguous judgment of pollutant diffusion direction caused by the asynchrony of multi-source sensor data existing in the prior art.

[0040] This solution proposes a dynamic data analysis method based on time series alignment and spatial concentration gradient calculation. By unifying the time base of multi-source data and quantifying the spatial diffusion characteristics of pollutants, it provides high-precision spatiotemporal correlation data for intelligent decision-making, fundamentally improving the accuracy of pollution source tracing and diffusion prediction.

[0041] In one embodiment of the present invention, the dynamic data analysis process includes the following core steps: S21: time series alignment processing of multi-source sensor raw data; S22: extraction of spatial correlation characteristics of pollutant concentration fluctuations; S23: cleaning and repairing abnormal data, and outputting structured monitoring data to the intelligent analysis and decision-making module.

[0042] In one embodiment of the present invention, a dynamic data analysis method is proposed to solve the problems of asynchrony and spatiotemporal analysis errors of multi-source sensor data: the timestamp deviation is calibrated by a linear interpolation algorithm ( Figure 4, S22), to achieve multi-source data time series alignment; using the concentration gradient calculation formula (2) to quantify the diffusion direction and rate of pollutants ( Figure 10 ,S231), combined with the network topology structure to extract spatial correlation features; using the Kalman filter algorithm to fill missing values and the isolation forest algorithm to filter noise ( Figure 9 ,S244), and finally outputs structured data to the intelligent analysis module.,The dynamic analysis engine (U2) includes the time series alignment module (U21),,the spatiotemporal correlation analysis submodule (U22), and the data cleaning submodule (U23).,Through spatiotemporal alignment and feature extraction, it provides,high-precision spatiotemporal correlation data for pollution assessment,,improving the accuracy of pollution source tracing and,diffusion prediction.

[0043] The main operating body is the time series alignment module (U21) of the dynamic analysis engine (U2), and the object is the asynchronous data collected by multi-source sensors. The linear interpolation algorithm is used to calibrate the time stamp deviation of different sensors to ensure that the multi-source data is aligned under a unified time base, providing a time consistency basis for subsequent spatiotemporal correlation analysis ( Figure 4 ,S22).

[0044] The main operation is the spatiotemporal correlation analysis submodule (U22), and the object is the pollutant concentration data of the pipe network node. and topological parameters (distance between adjacent nodes 、 , from Figure 7 Pipeline network topology library). Calculated using the concentration gradient calculation formula (2).

[0045] To quantify the spatial variation trend, the direction of the gradient vector indicates the diffusion direction of the pollutant, and the modulus reflects the concentration change rate. For example, Figure 10 The calculation results of the ammonia nitrogen concentration gradient at a certain node in the process can indicate the diffusion direction and rate of pollutants, providing key features for the construction of diffusion models ( Figure 10 ,S231).

[0046] The raw data after time and space alignment enters the data cleaning submodule (U23), uses Kalman filter interpolation to fill missing values, uses the isolation forest algorithm to filter noise points in the equipment vibration data, and outputs the cleaned structured data to the intelligent analysis and decision module (U3) ( Figure 9 ,S244).

[0047] The dynamic parsing engine (U2) consists of three sub-modules: Time series alignment module (U21): receives the original data, generates interpolation data with a unified time base through the interpolation algorithm, and outputs it to the spatiotemporal correlation analysis submodule (U22).

[0048] Spatiotemporal correlation analysis submodule (U22): Combined with the pipeline network topology library, it calculates the concentration gradient of each node, establishes a dynamic mapping relationship between concentration and pipeline network transportation capacity, and outputs the spatial feature vector to the data cleaning submodule (U23).

[0049] Data cleaning submodule (U23): Fill in missing values and filter noise on the spatiotemporally aligned data, and finally output structured data to the intelligent analysis and decision-making module (U3).

[0050] See also Figure 4 , time series alignment processing (S22) and spatial correlation feature extraction (S23) are the core steps, corresponding to the "dynamic data analysis" technical features. Figure 10 As shown, the spatiotemporal correlation analysis submodule (U22) establishes a dynamic mapping relationship (S232) through concentration gradient calculation (S231), supports the construction of the pollutant diffusion model (S234), and the characteristics of each marking technology are fully matched.

[0051] Example 3: Pollution Assessment Model Construction and Anomaly Detection Example. In one embodiment of the present invention, schematically, to address the problem of high misjudgment rate of concentration cumulative effect pollutants in traditional fixed threshold warning, a pollution assessment model construction method based on LSTM neural network is proposed: by using the historical pollution event feature database ( Figure 5 ,S31) stores more than 100,000 samples (positive samples account for 20%), and after data cleaning, normalization and labeling, inputs into the LSTM network training with 2 hidden layers (128 neurons per layer) (Adam optimizer, learning rate 0.001, 500 iterations, Figure 5 ,S32). The model filters the time series features through the forget gate, updates the cell state based on the spatial concentration gradient, and outputs the pollution level index. (0-1 interval), and dynamically adjust the cumulative effect threshold according to the pipe network topology ( Figure 1 , U3→U4). The feature database unit (U31), model training unit (U32) and real-time analysis and judgment unit (U33) of the intelligent analysis and decision module (U3) work together to achieve a closed loop from data training to real-time generation of pollution levels, improving the sensitivity and robustness of anomaly detection under complex working conditions.

[0052] See also Figure 5 , which shows the flow chart of pollution assessment model construction and analysis, “Establishing a historical pollution event feature database” (S31) and “Machine learning algorithm training” (S32) corresponding technical features, the input and output of the LSTM model and Figure 5 The S31-S35 steps are exactly the same ( Figure 5 , S31-S32). Figure 1As shown, the intelligent analysis and decision module (U3), dynamic analysis engine (U2), and graded response execution module (U4) form a closed loop through data flow (A2), ensuring the real-time transmission of pollution degree indicators and response triggering ( Figure 1 ,U3).

[0053] In one embodiment of the present invention, the pollution assessment model construction and analysis process includes the following core steps: S31: establishing a feature database based on historical pollution events; S32: using the LSTM algorithm to train the anomaly detection module; S33: inputting structured data after spatiotemporal correlation analysis to generate a pollution degree index.

[0054] 1. Establishment of a database of historical pollution event characteristics (S31) The operating subject is the database unit of the intelligent analysis and decision-making module (U3), and the objects are historical monitoring data (water quality parameters, equipment status, pipe network parameters) and manually annotated pollution events (such as illegal discharge events and equipment failure events). Data preprocessing includes: Clean invalid data (sensor node data with missing rate > 30%); Normalization processing (scaling parameters such as pH and dissolved oxygen to the range [0,1]); Event label marking (normal operating conditions are marked as 0, level one pollution events are marked as 0.3-0.7, level two are marked as 0.7-0.9, and level three are marked as 0.9-1).

[0055] Finally, a feature database containing more than 100,000 samples is formed, of which positive samples (pollution events) account for 20% and negative samples (normal working conditions) account for 80%. Figure 5 In the “Historical Pollution Event Characteristics Database” shown ( Figure 5 ,S31).

[0056] 2. LSTM anomaly detection module training (S32) The operation subject is the model training unit, and the input is the multi-source feature vector , including water quality parameters (such as ammonia nitrogen and total phosphorus concentrations), equipment status (such as vibration amplitude and current effective value), and pipe network parameters (such as node flow and pressure). The LSTM network structure contains two hidden layers (128 neurons per layer), and the state update equation is calculated using Formula (1). The training process uses the Adam optimizer with a learning rate of 0.001, a batch size of 32, 500 iterations, and a loss function of mean square error (MSE). The validation set accounts for 20% ( Figure 5 ,S32).

[0057] 3. Generation of pollution degree index (S33) The trained LSTM model is deployed in the real-time analysis unit, which inputs the structured data (spatiotemporal correlation features with unified timestamps) output by the dynamic analysis engine (U2) and outputs the pollution level. , where 0 represents normal and 1 represents the highest pollution level.

[0058] Intelligent analysis and decision module (U3) is the core analysis and judgment unit of the system ( Figure 1 ,U3), consists of three sub-modules: Feature database unit (U31): stores historical pollution event data, supports incremental updates (such as automatic import of the previous 24 hours of data every day), and provides a data query interface for the model training unit to call ( Figure 5 ,S31). Model training unit (U32): Integrates LSTM neural network framework, supports hyperparameter configuration (such as the number of hidden layers, number of iterations), through Figure 3 The sensor array and Figure 7 The topology library obtains multi-source training data and outputs the trained anomaly detection model ( Figure 5 , S32). Real-time analysis unit (U33): loads the trained LSTM model, processes the spatiotemporal feature data input by the dynamic analysis engine (U2) in real time, and generates pollution degree indicators , and output to the hierarchical response execution module (U4) ( Figure 1 ,U3→U4).

[0059] Example 4: Graded warning and emergency response example. In one embodiment of the present invention, schematically, in response to the problems of single warning level and lack of targeted emergency response in the existing technology, this embodiment proposes a graded warning mechanism based on pollution degree indicators, which dynamically matches differentiated response strategies through preset thresholds to solve the problems of resource waste and disposal delays in the traditional "one-size-fits-all" warning model.

[0060] See also Figure 5 , Figure 10 ,The hierarchical warning signal triggering (S42), work order generation (S43) and emergency response operation execution (S44) correspond to technical features in the ,hierarchical warning logic diagram. Figure 7 The work order generation process directly reflects the "associated pipe network topology structure" and "automatic matching of emergency resources, Figure 8 The figure is a schematic diagram of the process of generating an emergency response work order in one embodiment of the present invention.

[0061] The hierarchical early warning and emergency response process includes the following core steps: 1. Pollution degree index comparison (S41): Pollution level output by the intelligent analysis and decision module (U3) With three levels of preset thresholds (Level 1: , Level 2 , Level 3: ) for real-time comparison ( Figure 6 ,S42).

[0062] 2. Triggering of graded warning signals (S42): Activate the corresponding warning level according to the comparison results. Level 1 warnings are notified to the operation and maintenance personnel through the APP. Level 2 warnings trigger the sound and light alarms simultaneously. Level 3 warnings activate the plant-wide emergency broadcast. Figure 6 ,S43).

[0063] 3. Abnormal pollution source location and work order generation (S43): When the third-level warning is triggered, the pollution source coordinates (i, j) are locked through the pipeline node positioning algorithm and associated Figure 7 GIS maps and topological structure libraries are used to generate emergency response work orders with geographical tags, including pollution levels, impact areas and disposal recommendations.

[0064] 4. Emergency response operation execution (S44): The hierarchical response execution module (U4) starts the backup purification equipment (such as activated carbon adsorption device) within 10 seconds according to the work order instructions, adjusts the valve opening through the Modbus protocol to control the spread of pollution, and sends multi-channel alarm notifications (SMS, WeChat, phone) including the location of the pollution source to the management personnel, and generates a visual diffusion report ( Figure 10 ,A41-A43).

[0065] The hierarchical response execution module (U4) serves as the system's terminal execution unit ( Figure 1 ,U4), consists of warning threshold configuration unit, geographic information processing unit, and equipment linkage control unit: Warning threshold configuration unit: support operation and maintenance personnel to Figure 6 The warning logic interface customizes the three-level threshold. The default threshold is based on the statistical analysis of historical pollution events (for example, the three-level threshold corresponds to the top 5% quantile of historical severe pollution events).

[0066] Geographic Information Processing Unit: Figure 7 The pipeline network topology library in the system integrates the pipeline network GIS map and real-time monitoring data, and generates pollution diffusion heat maps through spatial interpolation algorithms with a positioning accuracy of ±10 meters.

[0067] Example 5: Example of intelligent generation of emergency response work orders. In one embodiment of the present invention, schematically, to address the problems of low efficiency and unreasonable resource allocation of traditional manual scheduling, this embodiment proposes an intelligent generation method for emergency work orders based on pipeline network topology and equipment status, and realizes accurate scheduling of emergency resources through data-driven optimization algorithms.

[0068] The process of generating emergency response work orders is as follows: Pollution source coordinate positioning: using Figure 4 The concentration gradient direction analyzed in (Construction of Pollution Assessment Model) is different from Figure 7The pipeline flow direction data in the (dynamic analysis engine architecture) is used to determine the coordinates (i, j) of the pollution source through triangulation with an error of less than 5 meters.

[0069] Topology association and resource matching: Retrieval Figure 7 The pipe network topology library in the dynamic analysis engine U2 identifies the location of upstream and downstream equipment of pollution sources and combines Figure 3 The real-time status of the equipment health monitoring unit (U12) in the (dynamic data analysis process) is obtained through the Dijkstra algorithm ( Figure 8 ,A6) Screen the spare equipment that can be reached within 3 minutes.

[0070] Work order content generation: through Figure 8 The work order generation module of the (Emergency Disposal Work Order Generation Process) automatically generates a work order containing pollution source information, disposal instructions and geographical tags, and pushes it to the on-site terminal ( Figure 8 ,A9-A10).

[0071] Intelligent work order generation system integrated into hierarchical response execution module Figure 8 The core components of the "Emergency Response Work Order Generation Process" (U4) include: Pipe network topology database: stores parameters such as node coordinates, pipe segment length, pipe diameter, valve position, etc., and supports real-time updates (such as manually importing CAD drawings after pipeline network renovation).

[0072] Example 6: Data cleaning and outlier processing example. In one embodiment of the present invention, schematically, to address the analysis error problem caused by missing sensor data and noise interference, this embodiment proposes a composite data cleaning method based on Kalman filtering and isolation forest to improve data quality to support accurate analysis.

[0073] Traditional data cleaning relies solely on simple interpolation or threshold filtering, which is incapable of handling complex noise (such as periodic interference from equipment vibration signals) and random omissions (such as missed data due to momentary communication interruptions). For example, a pipeline monitoring project experienced a 20% misjudgment rate due to ineffective dissolved oxygen data cleaning. This implementation significantly improves the proportion of valid data and its reliability through algorithm-level cleaning technology.

[0074] The data cleaning process includes: 1. Missing value filling: For continuous data such as dissolved oxygen and current, the Kalman filter algorithm is used to recursively estimate the true value. For example, the 10-second data missing due to LoRa signal interruption is repaired, and the estimated error is less than 2% ( Figure 9 , S241-S242). 2. Noise filtering: For discrete data such as equipment vibration amplitude and pH mutation value, the anomaly score is calculated using the isolation forest algorithm. Data points with a path length less than 50% of the average depth are identified as noise and filtered, with an accuracy rate of over 95% ( Figure 9, S243). 3. Quality labeling: Add quality labels (normal / repaired / filtered) to the cleaned data for differential processing by subsequent analysis modules ( Figure 9 ,S244).

[0075] The data cleaning submodule (U23) is the core component of the dynamic parsing engine (U2) Figure 1 ,U2), integrates two algorithm units: Kalman filter unit: built-in sensor drift model (such as the temperature drift coefficient of the water quality sensor), real-time update of the state transfer matrix, and adaptation to the noise characteristics of different sensors.

[0076] Isolation forest unit: By constructing 50 random binary trees, the average path length of the data points is calculated, and the data points with anomaly scores < 0.3 are marked as noise ( Figure 9 principle description).

[0077] Example 7: System module interaction and closed-loop control example. In one embodiment of the present invention, schematically, to address the problems of insufficient coordination and lack of feedback mechanism in traditional system modules, this embodiment constructs a full-process closed-loop control system of "data collection-analysis-decision-response", and realizes improved system adaptability through data interaction and strategy optimization between modules.

[0078] See also Figure 1 ,The feedback link (A4) in the overall system architecture diagram clearly indicates the feedback link, Figure 9 The data flow diagram (P1) reflects the closed-loop logic of "collection-analysis-decision-response".

[0079] In this embodiment, the process and interaction relationship are: 1. Forward data flow: Multimodal data acquisition module (U1) → dynamic analysis engine (U2) → intelligent analysis and decision module (U3) → hierarchical response execution module (U4), forming the main link of "monitoring-analysis-decision-execution" ( Figure 1 ,A1-A3).

[0080] 2. Reverse strategy feedback: The response execution results (such as the startup status of the backup equipment and the change of the pipe network pressure) are transmitted back to the dynamic analysis engine (U2) and the multimodal data acquisition module (U1) through the feedback link (A4), triggering the following adjustments: If a pollution incident occurs in a certain area three times in a row, the sampling frequency of the sensor in the area will be automatically increased from 1 second to 0.5 seconds ( Figure 9 , S1 parameter adaptation); if no actual fault is found after the equipment vibration abnormality warning, the abnormal score threshold of the isolation forest algorithm is automatically optimized ( Figure 9 , S243 parameter fine-tuning).

[0081] The core of closed-loop control lies in the design of a two-way data interface: Forward interface: Use JSON format to transmit structured data (timestamp, node coordinates, pollution level) to ensure cross-module compatibility ( Figure 4 ,S25-S31 data format).

[0082] Feedback interface: Real-time transmission of device control results and network status changes through message queues (such as RabbitMQ), with a delay of less than 10 milliseconds.

[0083] Example 8: Multimodal Data Acquisition Module Hardware Deployment Example. In one embodiment of the present invention, illustratively addressing the issue of insufficient sensor reliability in the highly corrosive and high-humidity environments of industrial wastewater pipe networks, a highly adaptable hardware deployment solution for the multimodal data acquisition module (U1) is developed. This solution utilizes a triple optimization of materials, structure, and intelligent compensation technology to ensure data acquisition accuracy under complex operating conditions. The multimodal data acquisition module (U1) comprises a water quality sensor array (U11) and an equipment health monitoring unit (U12). The former utilizes an IP68 protective housing made of polyvinylidene fluoride (PVDF), supports long-term immersion up to 5 meters underwater, and operates stably in acidic and alkaline environments with a pH range of 2-12. A built-in temperature compensation module provides real-time correction for the impact of water temperature on detection results.

[0084] Example 9: To address the problem that traditional diffusion models ignore the dynamic changes in concentration gradients, a spatiotemporal correlation analysis method based on spatial concentration gradients is proposed: The spatiotemporal correlation analysis submodule (U22) of the dynamic analysis engine (U2) calculates the pollutant concentration gradient vector based on the coordinates of the pipe network nodes and concentration data using formula (2). Its direction corresponds to the pollutant diffusion path, and its modulus reflects the rate of concentration change per unit distance (e.g., when the ammonia nitrogen concentration gradient modulus is > 0.5 mg / L·m, it is determined to be rapid diffusion). This gradient feature serves as a key input parameter to support the construction of a dynamic diffusion model ( Figure 10 , S231), achieving minute-level prediction of the migration trajectory of the pollution plume, with a prediction error reduced by 35% compared with the traditional model. During the analysis process, the concentration data is calibrated by the time series alignment module (U21) of the dynamic analysis engine (U2), and then the spatiotemporal correlation analysis submodule (U22) is combined with the pipe network topology library ( Figure 7 ) completes the gradient calculation and finally outputs it to the intelligent analysis and decision module (U3) to generate pollution diffusion warning ( Figure 5 ,S33).

[0085] Example 10: To address the issues of insufficient visualization of emergency response and low efficiency of equipment linkage, a multi-system linkage control and real-time situation visualization solution is constructed: Equipment linkage control: The hierarchical response execution module (U4) completes the startup of backup purification equipment (such as activated carbon adsorption devices) and valve opening adjustment within 10 seconds through industrial protocols such as Modbus / TCP, achieving rapid physical blocking of pollution spread.

[0086] Multi-channel graded alarms: Level 1 warning: real-time data links are pushed via the APP, allowing operation and maintenance personnel to remotely view monitoring parameters; Level 2 warning: SMS notification including the pollution source coordinates (accuracy ±10 meters) and pollution level; Level 3 warning: triggers a telephone alarm and provides disposal suggestions (such as prioritizing closing the upstream valve V-023) to ensure immediate response to high-risk events.

[0087] Real-time Situation Visualization: A visualization module developed based on WebGL technology supports adaptive display on both PC and mobile devices, loading pipeline network BIM models and monitoring data (such as ammonia nitrogen concentration heat maps and valve status icons) in real time, with rendering latency less than 50ms per frame. Managers can click on a map marker to jump to the pollution source details interface, view diffusion prediction curves and compare them with historical data, and quickly formulate treatment plans.

[0088] Through the collaborative design of "protocol standardization + response grading + intuitive display", the solution reduces the equipment linkage delay to 10 seconds, increases the information transmission accuracy to 99%, and improves the efficiency of visual interaction by 40% compared with traditional solutions, effectively solving the pain points of "slow, chaotic and interrupted" emergency response in industrial scenarios.

Claims

1. A sewage monitoring and early warning method, characterized in that: The following steps are involved: S1: Real-time collection of multi-source sewage monitoring data through a distributed sensor network, wherein the multi-source sewage monitoring data includes water quality parameters, equipment operating status, and pipe network environment information; S2: performing dynamic data analysis on the multi-source sewage monitoring data to generate structured monitoring data, wherein the dynamic data analysis includes performing time series alignment processing and spatial correlation feature extraction on the multi-source sewage monitoring data; S3: Input the multi-source feature vectors corresponding to the multi-source sewage monitoring data into the pre-trained pollution assessment model to perform pollution assessment operations, output the target pollution degree index, and judge the cumulative effect of pollutants through dynamic thresholds; S4: Based on the comparison result between the target pollution level index and the preset threshold, trigger a graded warning signal and perform corresponding emergency response operations.

2. The method according to claim 1, characterized in that The dynamic data parsing includes: S21: Linear interpolation algorithm calibrates the timestamp deviation of water quality parameters to ensure that water quality parameters are aligned under a unified time base; S22: Quantify the diffusion direction and rate of pollutants in water quality parameters through the concentration gradient calculation formula, where the concentration gradient direction is generated by the concentration difference between adjacent nodes and the pipe network topology; S23: The Kalman filter algorithm is used to fill the missing values of the water quality parameters, and the isolation forest algorithm is used to filter the noise in the water quality parameters. The cleaned water quality parameters are used as structured monitoring data.

3. The method according to claim 2, characterized in that The pollution assessment model is built based on the LSTM neural network, and its state update includes: S31: Filter the water quality parameter characteristic data after time series alignment through the forget gate to control the retention ratio of historical cell states; S32: updating the current cell state by combining the concentration gradient data obtained by extracting the spatial correlation features; S33: Output the hidden state as a pollution level indicator, and dynamically adjust the pollution cumulative effect threshold according to the pipeline network topology.

4. The method according to claim 1, wherein After the graded warning signal is triggered, the method further includes: S41: transmitting the device control results and pipe network status change data back through the feedback link; S42: Adaptively adjust the sensor sampling frequency or algorithm parameter threshold according to the feedback data.

5. The method according to claim 1, wherein After the graded warning signal is triggered, the method further includes: S51: Based on the concentration gradient change trend and pipe network flow direction data, the coordinates of the abnormal pollution source are determined by triangulation; S52: Generate an emergency response work order with a geographical tag, the work order including the pollution level, impact range, and response suggestions; S53: Combine the network topology and the real-time operating status of the equipment to automatically match the optimal emergency response resource allocation plan.

6. The method according to claim 5, characterized in that The optimal emergency response resource allocation plan is generated in the following way: S61: using Dijkstra algorithm to calculate the shortest path between the pollution source and the backup equipment; S62: Prioritize the use of backup equipment within 50 meters of the pollution source; S63: Dynamically adjust the scheduling strategy with the goal of minimizing processing time and minimizing equipment energy consumption.

7. The method according to claim 1, characterized in that The multi-source sewage monitoring data is collected in the following ways: S71: The water quality sensor array acquires pH value, dissolved oxygen, ammonia nitrogen and total phosphorus concentration in real time, with a sampling frequency of ≥1 Hz; S72: The equipment health monitoring unit collects water pump current, vibration amplitude, and valve opening; S73: The pipe network environment monitoring device collects node flow, pressure and pipe diameter parameters, and the data update cycle is ≤10 seconds.

8. The method according to claim 1, characterized in that The emergency response operations also include: S81: Generate a visual pollution diffusion situation report based on WebGL technology; S82: Dynamically display the concentration heat map and diffusion direction arrows; S83: Supports adaptive display on PC and mobile devices, with rendering delay <50ms / frame.

9. The method according to claim 1, characterized in that After the graded warning signal is triggered, the method further includes: S91: Level 1 warning pushes real-time data link via APP; S92: Level 2 warning adds SMS notification, including pollution source coordinates and levels; S93: Level 3 warning triggers a telephone alarm with disposal suggestions.

10. A sewage monitoring and early warning system, characterized in that: include: Multimodal data acquisition module: used to collect water quality parameters, equipment operating status and pipe network environment information in real time through a distributed sensor network; Dynamic parsing engine: used for time series alignment, spatial correlation feature extraction and data cleaning of multi-source data; Intelligent analysis and decision-making module: A pollution assessment model built based on the LSTM neural network outputs the target pollution level index; Graded response execution module: triggers differentiated early warning signals according to pollution levels and performs emergency response operations.

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