Intelligent drainage pipe network pollution traceability alarm system based on invariant analysis

Abstract

The invention discloses a drainage pipe network pollution traceability and alarm system based on invariant analysis, which belongs to the field of data communication of a data center and comprises a data collection module, a cloud platform processing module, an invariant analysis module, an abnormal emission identification and alarm module and a decision support system. The supervision efficiency of the drainage pipe network is improved, environmental pollution is reduced, and sustainable utilization of water resources is guaranteed; according to the system, various sensors are installed at key nodes of a drainage pipe network, data such as water quality, water level, water volume and flow velocity are collected in real time, and the data are transmitted to a cloud platform through a wireless communication technology; by utilizing the data processing capability of the cloud computing platform and combining with an invariant analysis algorithm, the system can quickly position a pollution source, track a pollution path and give an alarm to related personnel in time; the monitoring, management and pollution prevention and control capabilities of the drainage pipe network are effectively improved, and powerful technical support is provided for urban water resource management and environmental protection.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental protection and water resource management, and in particular to a drainage network pollution source tracing and alarm system based on invariant analysis. Background Art

[0002] With the rapid development of industrialization and urbanization, the health of drainage pipe networks, as an important part of urban infrastructure, is directly related to the quality of life of urban residents and the sustainable development of cities. The drainage pipe network is not only responsible for collecting and transporting urban sewage, but also undertakes the task of rainwater discharge. However, due to reasons such as aging of the pipe network, insufficient maintenance, and illegal sewage discharge, the drainage pipe network often faces pollution problems, which not only affects the efficiency of sewage treatment, but may also cause serious pollution to groundwater and the surrounding environment.

[0003] Traditional methods of monitoring and managing drainage network pollution mainly rely on manual inspections and regular sampling. These methods have many limitations, such as low monitoring efficiency, high cost, poor timeliness, and difficulty in achieving rapid response to pollution incidents. In addition, since drainage networks are usually buried underground, their structures are complex and hidden, making it more difficult to locate and trace the source of pollution.

[0004] In order to effectively solve the above problems, in recent years, with the development of Internet of Things technology, cloud computing, big data analysis and artificial intelligence algorithms, intelligent drainage network management systems have emerged. These systems deploy various sensors in the drainage network to collect data such as water quality, water volume, flow rate, etc. in real time, and transmit the data to the central processing platform through wireless communication technology. However, how to accurately identify and locate pollution sources from massive monitoring data, and how to quickly respond to abnormal emission events, are still technical challenges facing this field. Summary of the invention

[0005] The technical problem to be solved by the present invention is to address the deficiencies of the background technology. The present invention provides a drainage network pollution source tracing and alarm system based on invariant analysis. Using advanced sensor technology, cloud computing platform and invariant analysis algorithm, the urban drainage network is monitored in real time, the pollution source is located quickly and accurately, and an automatic alarm is issued for abnormal emission events. The technical solution of the present invention aims to improve the supervision efficiency of the drainage network, reduce environmental pollution, and ensure the sustainable use of water resources. It is of great significance to improve the level of urban infrastructure management and environmental protection.

[0006] The present invention adopts the following technical solutions to solve the above technical problems:

[0007] The drainage network pollution tracing and alarm system based on invariant analysis is characterized by: comprising a data collection module, a cloud platform processing module, an invariant analysis module, an abnormal emission identification and alarm module and a decision support system;

[0008] The data collection module is used to install a variety of sensors at key nodes of the drainage network, including but not limited to water quality sensors, water level sensors, water volume sensors and flow rate sensors, so as to monitor the status of the drainage network in real time; the types of data collected include real-time monitoring data, manual irregular sampling monitoring data, factory hydraulic and water quality monitoring data, and water meter reading data, to ensure the comprehensiveness and diversity of the data; the real-time synchronization of sensor data is achieved through the Internet of Things technology to ensure the timeliness and accuracy of the data;

[0009] The cloud platform processing module is used to use the MindSphere cloud platform to provide powerful data processing and storage capabilities. The cloud platform transmits, stores and analyzes the collected data in real time, and uses the powerful computing power of cloud computing to quickly process massive data. It ensures the security of data during transmission and storage, and uses encryption technology and access control mechanisms to protect data from unauthorized access.

[0010] The invariant analysis module is used for intelligent algorithms based on invariant analysis. It can extract key features from complex drainage network data to form invariants. It can quickly locate pollution sources and improve the accuracy of tracing by analyzing the differences between real-time data and normal operation models. It can also use invariant analysis technology to track pollution paths and provide a scientific basis for pollution control.

[0011] Abnormal emission identification and alarm module, using MindSphere alarm analysis module, sets abnormal emission identification rules based on monitoring indicators, including time series jump, exceeding threshold, 3σ deviation; can automatically identify abnormal emission events, reduce manual intervention, and improve response speed; when abnormal emission is detected, the system immediately sends an alarm to relevant personnel through SMS, email or APP push, etc., to ensure timely response;

[0012] The decision support system provides pollution control strategies and resource optimization allocation suggestions based on the results of source tracing analysis to help managers make scientific decisions. It can provide decision support to managers based on real-time data and historical data analysis, including emergency response to pollution incidents and resource scheduling.

[0013] As a further preferred solution of the drainage network pollution tracing and alarm system based on invariant analysis of the present invention, the principle of the data collection module specifically includes:

[0014] Sensor selection and deployment: Select and deploy a range of high-precision sensors, including water quality sensors, water level sensors, water volume sensors, and flow rate sensors, and integrate the sensors into a monitoring station to achieve comprehensive monitoring of key nodes in the drainage network; use machine learning algorithms to analyze historical sensor data, predict and automatically trigger calibration cycles to maintain long-term data stability;

[0015] Adaptive sampling: Adaptive sampling algorithms dynamically adjust sampling frequency and sampling points based on real-time monitoring data trends and historical data patterns. The algorithms can identify abnormal patterns in data and automatically increase sampling frequency to capture key information. Intelligent algorithms optimize sampling resource allocation, reduce unnecessary sampling, and lower operating costs, while ensuring high coverage and timeliness of key data.

[0016] Wireless Sensor Network (WSN): Building a wireless sensor network enables sensor nodes to self-organize into a network without relying on a central node, thus enhancing the robustness of the system; using a low-power communication protocol to extend the battery life of the sensor, reduce maintenance frequency, and ensure real-time data transmission;

[0017] Edge computing integration: Integrate edge computing modules in sensor nodes or gateways to pre-process raw data, including data cleaning, feature extraction and preliminary analysis, to reduce the amount of data that needs to be transmitted to the cloud; use the fast processing capabilities of edge computing to respond to key events immediately, such as immediately launching emergency procedures when abnormal water quality is detected;

[0018] Data fusion and calibration mechanism: Through the data fusion algorithm, data from different sensors and monitoring points are integrated to improve the consistency and accuracy of the data; using machine learning technology, the data fusion model is continuously optimized and the calibration parameters are automatically adjusted to adapt to the impact of environmental changes and sensor aging.

[0019] As a further preferred solution of the drainage network pollution tracing and alarm system based on invariant analysis of the present invention, the principle of the cloud platform processing module specifically includes:

[0020] Multi-tenant architecture: adopts advanced multi-tenant cloud platform architecture to provide independent data storage and processing space for each user or organization, ensuring data isolation and privacy protection; realizes dynamic allocation and elastic expansion of resources, automatically adjusts computing and storage resources according to actual needs and usage of users, and improves resource utilization and cost-effectiveness;

[0021] Distributed data processing: Deploy efficient parallel computing frameworks, such as Apache Spark or Hadoop, to achieve distributed data processing and analysis, and improve the speed and efficiency of data processing; adopt intelligent load balancing strategies to automatically allocate computing tasks to different computing nodes according to the computing complexity and data volume of the tasks, to ensure the efficient and stable operation of the cloud platform;

[0022] Data lake technology: Build a unified data lake architecture to support the storage of multiple data formats and types, including structured data, semi-structured data, and unstructured data; integrate machine learning and artificial intelligence algorithms to conduct in-depth analysis and mining of data stored in the data lake, discover potential value and patterns in the data, and provide support for decision-making;

[0023] Intelligent data quality management: Use automated data cleaning tools to pre-process the collected data, remove duplicate, erroneous or incomplete data, ensure data quality and consistency, continuously monitor data integrity, accuracy and timeliness, and promptly detect and handle data anomalies to ensure data reliability;

[0024] End-to-end encryption and access control: End-to-end encryption technology, TLS / SSL, is used to ensure the security of data during transmission and prevent data from being intercepted or tampered with during transmission; it is used to implement the role-based access control RBAC mechanism, which finely controls access to data and resources based on the user's role and permissions to prevent unauthorized access and data abuse.

[0025] As a further preferred solution of the drainage network pollution tracing and alarm system based on invariant analysis of the present invention, the principle of the invariant analysis module specifically includes

[0026] Adaptive invariant identification algorithm: Through the adaptive invariant identification algorithm, the feature extraction and analysis strategy can be automatically adjusted according to the characteristics of real-time data and the dynamic changes of the drainage network; integrating machine learning models, neural networks or support vector machines to continuously learn and optimize the invariant identification process, improve the generalization ability and accuracy of the algorithm;

[0027] Among them, feature extraction: Assume that the test data set is, X = {x1, x2, ... x n}, where x n is the data collected at time point n; the feature extraction function f is used to extract invariant features from the data: f:X→Ff: where F is the feature space;

[0028] Adaptive adjustment: Let T be the parameter set of the analysis strategy, and adaptive adjustment can be expressed as: T = g(X, T prev ), where g is the adjustment function, T prev is the previous parameter set;

[0029] Machine learning model: Taking a neural network as an example, the learning process can be expressed as an update of weights and biases:

[0030] Where W and b represent weight and bias respectively, η is the learning rate, and L is the loss function; the optimization goal of the support vector machine SVM is to maximize the interval: At the same time, satisfying y i (w·φ(x i )+b)≥1,for all i; where w and b are model parameters, φ is the kernel function that maps data to a high-dimensional space, and y i is the label of the data;

[0031] Generalization ability evaluation: Use cross-validation to evaluate the generalization ability of the model, such as k-fold cross-validation: Among them, L i is the loss function of the i-th fold;

[0032] Dynamic feature adjustment: According to the feature changes of real-time data, the feature extraction strategy is dynamically adjusted, which can be expressed as: new =h(f old ,X); where h is the adjustment function, f old is the old feature extraction function;

[0033] Characteristics and dynamic changes of real-time data: Assume that the characteristic vector of real-time data is x = [x1, x2, ..., x d ], dynamic changes can be captured by time series analysis, using an autoregressive model: t =c+φ1x t-1 +φ2x t-2 +…+φ p x t-p +∈ t ; where c is a constant term, φ i are model parameters, p is the time delay, ∈ t is the error term;

[0034] Comparative analysis of real-time data and models: Through efficient data processing processes, ensure that real-time data can be quickly processed and compared with the preset normal operation model; build an anomaly detection system that can use statistical methods or machine learning algorithms to quickly identify data patterns that are significantly different from the normal model, thereby discovering potential pollution events; where the data processing process: suppose the real-time data set is X = {x1, x2, …, x n}, where x i is the data collected at time point i;

[0035] The data processing flow can be represented as a series of operations P:X processed =P(X), where X processed is the processed data set;

[0036] Model comparison: Let the normal operation model be M, and the model comparison can be expressed as the difference metric D: D = d(X processed,M), where ,d is the metric function;

[0037] Anomaly detection: Statistical methods, including control charts, expressed as: C = {x∈X processed :|x-μ|>kσ}, where μ is the mean, σ is the standard deviation, and k is the control chart constant;

[0038] Machine learning algorithms, such as Isolation Forest: IsolationForses(X processed )→Scores; where Scores represents the sample anomaly score;

[0039] Support vector machine (SVM) is used to classify normal and abnormal data: f(x) = w·φ(x) + b; where φ is the kernel function that maps the data to a high-dimensional space, and w and b are model parameters;

[0040] Autoencoder for unsupervised learning anomaly detection: Among them, f θ and g θ are the encoding and decoding functions of the autoencoder, respectively, and θ is the model parameter;

[0041] Anomaly determination: Set a threshold r. If the difference measure D exceeds r, an anomaly is considered to be detected:

[0042] if D>τ,then anomaly detected;

[0043] Real-time feedback: According to the abnormal detection results, the data processing flow or model parameters are adjusted in real time to improve the detection accuracy;

[0044] Multi-source data fusion analysis: Establish a data integration mechanism that can effectively integrate data from different sensors and monitoring points to form a unified data view; use data consistency verification tools to compare information from different data sources, eliminate inconsistent or erroneous data, and ensure the accuracy of analysis results:

[0045] Data integration mechanism: Let the data sets of different sensors and monitoring points be X1, X2, …, X n ; The data integration function I is used to merge these data sets into a unified data set X combined :X combined =I(X1,X2,…,X n );

[0046] Data preprocessing: Preprocess each data set, including denoising, normalization, etc.: X i,processed =P(x i ), where P is the preprocessing function;

[0047] Data consistency verification: Let the data consistency verification function be V, which is used to compare information from different data sources and exclude inconsistent data: X verified =V(X combined );

[0048] Outlier detection: Use statistical methods, standard deviation or interquartile range to detect and exclude outliers: Q1 = quantile (X verified ,0.25),Q3=quantile(X verified ,0.75)IQR=Q3-Q1; Outliers={x∈X verified :x<Q1-k·IQRorx>Q3+k·IQR}; where k is a constant, usually 1.5;

[0049] Data fusion: Use weighted average and other methods to fuse data: Among them, w i is the dataset X i The weight of the data is determined based on data quality or other criteria;

[0050] Error correction: If there are systematic errors between data sources, correct them through linear regression or other methods: in, is the corrected data, a and b are parameters determined by the least squares method and other methods;

[0051] Update mechanism: Set up an update mechanism to update the fusion results when new data arrives: X updated =I(X final ,X new ), where X new is the newly arrived data;

[0052] Intelligent pollution path tracking: Through intelligent algorithms, the propagation process of pollutants in the drainage network is simulated, the pollution path is tracked, and a scientific basis is provided for pollution control; a reverse tracing strategy is implemented, and the algorithm is used to start from the downstream of the pollution event and trace back to the pollution source, thereby improving the speed and accuracy of tracing;

[0053] Path tracing algorithm: Let the pollutant concentration data be C(t), a function of time t, and the tracing algorithm be expressed as P(t) = Trace(C(t), V(t)), where P(t) is the pollution path at time t, V(t) is the fluid velocity, and Trace is the tracing algorithm;

[0054] Fluid Dynamics Model: Use the fluid dynamics equations, including the Navier-Stokes equations, to simulate the propagation of pollutants: Where v is the fluid velocity, p is the pressure, μ is the dynamic viscosity, f is the external force, and p is the fluid density;

[0055] Reverse tracing strategy: Let S be the set of pollution sources and D be the detection point of the pollution event. The reverse tracing algorithm can be expressed as: source =ReverseTrace(D,C(D)) where S source It is the inferred pollution source, and ReverseTrace is the reverse tracing algorithm;

[0056] Probability model: Use probability model to evaluate the relative likelihood of different pollution sources: P(S i |D)∝P(C(D)|S i )·P(S i ) Among them, P(S i |D) is the posterior probability that the pollution source comes from S when pollution is observed at detection point D, P(C(D)|S i ) is at the pollution source S i Under the condition of , the likelihood probability of observing contamination C(D), P(S i ) is the pollution source S i The prior probability of

[0057] Data assimilation: Using data assimilation techniques such as Kalman filtering, combined with model predictions and actual observation data, to improve the accuracy of pollution path tracking: in, is the analysis state, is the predicted state, K k is the Kalman gain, z k is the observed value, H k is the observation operator;

[0058] Optimization algorithm: Use optimization algorithm, such as genetic algorithm or particle swarm optimization, to find the optimal location of pollution sources: Among them, Cost is the cost function used to evaluate the pollution source S i The cost of the contamination propagation path to the detection point D;

[0059] (5) Predictive maintenance and optimization: Build a prediction model based on historical data and current trends to predict possible pollution events in the future and provide decision support for preventive measures; based on the prediction results, optimize the maintenance and cleaning strategies of the drainage network to reduce the occurrence of pollution events and improve the operating efficiency and reliability of the drainage network;

[0060] Prediction model construction: Assume that the historical data set is H = {h1,h2,…,h m}, the current trend is T = {t1, t2, …, t n}, the prediction model is expressed as: F = Model(H, T), where F is the prediction of future events;

[0061] Time Series Analysis: Time Series Forecasting Using the Autoregressive Integrated Moving Average ARIMA Model: x t =c+φ1x t-1 +φ2x t-2 +…+φ p x t-p +θ1∈ t-1 +…+θ q ∈ t-q +∈ t ; where x t is the value at time t, c is a constant term, φ i and θ i are model parameters, p and q are the orders of the autoregressive and moving average terms, ∈ t is the error term;

[0062] Machine learning prediction: Use machine learning algorithms, including random forests or neural networks, to build a prediction model: F(X) = f(w, b, X); where F is the prediction function, w and b are model parameters, and X is the input feature;

[0063] Maintenance strategy optimization: Let the maintenance strategy be S, and the optimization goal is to minimize the frequency of pollution events and maintenance costs: S * =ArgMin S (Cost(S)+λ·R(S)); where S * is the optimal maintenance strategy, Cost(S) is the maintenance cost, R(S) is the frequency of pollution events, and λ is the trade-off factor;

[0064] Genetic Algorithm Optimization: Use genetic algorithms to find the optimal maintenance strategy:

[0065] Snew=GeneticAlgorithm(Spopulation,Fitness,MutationRate,SelectionPressure);

[0066] Among them, Spopulation is the population of the maintenance strategy, Fitness is the fitness function, MutationRate is the mutation rate, and SelectionPressure is the selection pressure;

[0067] Simulated annealing algorithm optimization: Use simulated annealing algorithm to optimize the maintenance strategy to avoid local optimal solution: S new =SimulatedAnnealing(S current ,T,CoolingRate) where Scurrent is the current maintenance strategy, T is the initial temperature, and CoolingRate is the cooling rate;

[0068] Real-time feedback adjustment: Dynamically adjust maintenance strategies based on real-time data and prediction results:

[0069] Supdated = UpdateStrategy(S * ,F,X real-time ) where Supdated is the updated maintenance policy, X real-time It is real-time data.

[0070] As a further preferred solution of the drainage network pollution tracing and alarm system based on invariant analysis of the present invention, the principle of the abnormal emission identification and alarm module includes:

[0071] Using MindSphere's alarm analysis module, set up abnormal emission identification rules based on monitoring indicators, including time series jumps, exceeding thresholds, and 3σ deviations; the system can automatically identify abnormal emission events, reduce manual intervention, and improve response speed; once abnormal emissions are detected, the system immediately sends an alarm to relevant personnel via SMS, email, or APP push to ensure timely response;

[0072] Self-learning alarm thresholds: Use dynamic threshold algorithms to automatically adjust alarm thresholds based on real-time monitoring data and historical trends to adapt to environmental changes and operating conditions; integrate machine learning models, including random forests or neural networks, to analyze historical alarm events, learn and predict abnormal emission patterns, and thus optimize alarm rules;

[0073] Multi-parameter comprehensive analysis: Implement multi-parameter correlation analysis to identify the relationship between different monitoring parameters and improve the accuracy of abnormal emission identification; adopt a weight allocation mechanism to assign different weights according to the importance of parameters to abnormal emission identification, and optimize the sensitivity and accuracy of alarm rules;

[0074] Real-time data analysis engine: Build a high-speed data processing engine that can analyze a large amount of monitoring data in real time and quickly identify abnormal emission events; implement a complex event processing mechanism that can analyze simultaneous abnormalities of multiple monitoring parameters and improve the system's comprehensive response capabilities;

[0075] Intelligent alarm suppression and merging: Adopt intelligent alarm suppression strategies to identify and suppress repeated or unimportant alarms, reduce unnecessary alarm notifications, and avoid alarm fatigue; adopt alarm merging algorithms to merge related alarm events into a comprehensive alarm, providing clearer event descriptions and handling suggestions; alarm merging algorithms are designed to merge related alarm events into a comprehensive alarm, providing clearer event descriptions and handling suggestions; merging algorithms are usually based on alarm similarity, timestamps, and spatial location factors;

[0076] Similarity metric: Define the similarity metric S(A i ,A j ), using Euclidean distance and cosine similarity methods; setting merging conditions, including similarity threshold ∈ and time window T; if two alarms meet the merging conditions, they are merged into a comprehensive alarm;

[0077] Similarity measure: Where f(A) is the feature vector of alarm A;

[0078] Alarm merging judgment: Among them, t i and t j It is an alarm A i and A j Timestamp of; merge operation: A merged =Combine(A i ,A j ), where A merged It is a combined alarm, which contains the key information and handling suggestions of the combined alarm.

[0079] Multi-channel alarm notification system: Implement a customized notification system that allows users to set the content and method of alarm notifications based on their roles and preferences, improving the accessibility and effectiveness of information; through an interactive alarm response mechanism, allow users to directly confirm receipt of alarms and provide feedback via SMS, email or APP, enhancing the interactivity of the system and user participation.

[0080] As a further preferred solution of the drainage network pollution tracing and alarm system based on invariant analysis of the present invention, the principle of the decision support system specifically includes:

[0081] Based on the results of source tracing analysis, it provides pollution control strategies and resource optimization allocation suggestions to help managers make scientific decisions; it can provide decision-making support for managers based on real-time data and historical data analysis, such as emergency response to pollution incidents and resource scheduling;

[0082] Predictive decision-making model: Use scenario analysis tools, historical data and real-time monitoring data to simulate possible future pollution events and environmental changes, and provide managers with a variety of possible future development scenarios; build a dynamic strategy generation system to automatically propose pollution control strategies and resource allocation plans that adapt to different scenarios based on the results of scenario analysis, making decision-making more flexible and forward-looking;

[0083] Intelligent resource scheduling algorithm: Use intelligent resource scheduling algorithm to evaluate the urgency and treatment requirements of different pollution events, optimize resource allocation and scheduling, and ensure that resources are effectively used where they are most needed; implement a real-time scheduling system that can dynamically adjust resource scheduling plans based on the latest event progress and resource usage, and improve the real-time and flexibility of resource scheduling;

[0084] Interactive decision support interface: Create a user-friendly interactive decision support interface that enables managers to intuitively view data, analysis results and strategy recommendations, thereby improving the efficiency and accuracy of decision-making; integrate a variety of decision-making support tools into the interface, including risk assessment tools, cost-benefit analysis tools, etc., to provide managers with comprehensive decision support and help them evaluate the pros and cons of different decision plans from multiple perspectives.

[0085] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects:

[0086] The present invention discloses a drainage network pollution tracing and warning system based on invariant analysis. By installing multiple sensors at key nodes of the drainage network, the system collects data such as water quality, water level, water volume, flow rate, etc. in real time, and transmits the data to a cloud platform through wireless communication technology. By utilizing the data processing capability of the cloud computing platform and combining the invariant analysis algorithm, the system can quickly locate the pollution source, track the pollution path, and issue warnings to relevant personnel in a timely manner.

[0087] Real-time monitoring and rapid response: Real-time collection and analysis of drainage network data, rapid identification of abnormal discharge events and timely alarms, improving the response speed to pollution events;

[0088] Accurate tracing: Using advanced invariant analysis technology, combined with cloud computing and big data analysis, accurately locate pollution sources and emission paths;

[0089] Intelligent alarm management: Reduce repeated or unimportant alarms through alarm suppression strategies to avoid alarm fatigue, and provide clearer event descriptions through alarm merging algorithms;

[0090] Data fusion and analysis: effectively integrate data from different sensors and monitoring points to improve data consistency and analysis accuracy;

[0091] Predictive maintenance: Build predictive models based on historical data and current trends to predict possible future contamination events and provide decision support for maintenance and cleaning strategies;

[0092] User-friendly interface: Provides intuitive data visualization tools and user-friendly operation interface to simplify user operation processes and improve user experience;

[0093] Reduce environmental pollution: through timely warning and pollution source location, reduce the impact of illegal pollution discharge on the environment and protect water resources;

[0094] Improve supervision efficiency: Use intelligent algorithms and big data analysis to improve the supervision efficiency of drainage networks and reduce the cost of manual inspections;

[0095] Decision support: Provide strategic recommendations based on data analysis to assist managers in making scientific decisions;

[0096] System reliability: Adopt multi-tenant architecture, distributed processing and data security technology to ensure system stability and data security;

[0097] Flexibility and scalability: The system is designed with flexibility and scalability in mind, and can adapt to the needs of drainage networks of different sizes and complexities;

[0098] Integrated monitoring: An integrated monitoring system has been built, which improves the integration and intelligence level of monitoring through a unified data platform and intelligent analysis tools. BRIEF DESCRIPTION OF THE DRAWINGS

[0099] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without paying any creative work.

[0100] Figure 1 It is a schematic diagram of the architecture of the drainage network monitoring system of the present invention;

[0101] Figure 2 is the invariant analysis flow chart of the present invention;

[0102] Figure 3 This is an example interface design of abnormal emission warning of the present invention;

[0103] Figure 4 It is a schematic diagram of the data collection module of the present invention;

[0104] Figure 5 It is a schematic diagram of the cloud platform processing module of the present invention;

[0105] Figure 6 is a schematic diagram of the invariant analysis module of the present invention;

[0106] Figure 7 It is a schematic diagram of the abnormal emission identification and alarm module of the present invention;

[0107] Figure 8 It is a schematic diagram of the decision support system of the present invention. DETAILED DESCRIPTION

[0108] The technical solution of the present invention is further described in detail below in conjunction with the accompanying drawings:

[0109] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. The present invention is described in detail below based on the drawings and preferred embodiments, and the purpose and effect of the present invention will become clearer. It should be understood that the specific embodiments described here are only used to explain the present invention and are not used to limit the present invention.

[0110] The present invention discloses an intelligent drainage network pollution source tracing alarm system and method based on invariant analysis, aiming to improve the supervision efficiency of drainage networks, reduce environmental pollution, and ensure the sustainable use of water resources. The system collects data such as water quality, water level, water volume, flow rate, etc. in real time by installing multiple sensors at key nodes of the drainage network, and transmits the data to the cloud platform through wireless communication technology. By utilizing the data processing capabilities of the cloud computing platform and combining the invariant analysis algorithm, the system can quickly locate the pollution source, track the pollution path, and issue warnings to relevant personnel in a timely manner.

[0111] The main innovations of the system include:

[0112] 1. Invariant analysis technology: quickly locate pollution sources by analyzing the differences between real-time data and normal operating models.

[0113] 2. Real-time data and model comparison analysis: Use statistical methods or machine learning algorithms to quickly identify data patterns that are significantly different from normal models.

[0114] 3. Multi-source data fusion analysis: effectively integrate data from different sensors and monitoring points to form a unified data view.

[0115] 4. Intelligent pollution path tracking: Develop intelligent algorithms to simulate the pollutant propagation process and effectively track the pollution path.

[0116] 5. Predictive maintenance and optimization: Build predictive models based on historical data and current trends to optimize maintenance and cleaning strategies.

[0117] 6. Alarm suppression and merging: Develop intelligent alarm suppression strategies to identify and suppress duplicate or unimportant alarms, while merging related alarm events into a comprehensive alarm.

[0118] The system also provides intuitive data visualization tools and a user-friendly operation interface to simplify the user operation process and improve the user experience. By implementing the technical solution of the present invention, the monitoring, management and pollution prevention and control capabilities of the drainage network can be effectively improved, providing strong technical support for urban water resources management and environmental protection.

[0119] The present invention relates to the technical field of environmental protection and water resource management, and in particular to an intelligent drainage network pollution source tracing alarm system based on invariant analysis and a method thereof. The system utilizes advanced sensor technology, cloud computing platform and invariant analysis algorithm to monitor the urban drainage network in real time, quickly and accurately locate the pollution source, and automatically alarm abnormal emission events. The technical solution of the present invention aims to improve the supervision efficiency of the drainage network, reduce environmental pollution, and ensure the sustainable use of water resources, which is of great significance for improving the level of urban infrastructure management and environmental protection.

[0120] Difficulty in identifying abnormal sewage discharge in drainage networks: Existing technologies mainly rely on alarm rules set by thresholds, which makes it difficult to effectively identify abnormal sewage discharge behaviors in some business scenarios. This invention improves the accuracy and efficiency of abnormal discharge identification by adopting a tracing method based on the topological relationship of the drainage network and a drainage behavior portrait similarity comparison technology.

[0121] The problem of tracing the source of pollution in drainage pipe networks: Traditional manual offline tracing is inefficient and difficult to trace the source of domestic sewage. The system proposed in this invention can quickly locate the pollution source and discharge path through intelligent algorithms and big data analysis, thus improving the efficiency and accuracy of tracing the source.

[0122] Supervision and law enforcement difficulties: In the actual law enforcement process, due to the lack of a clear understanding of the sewage characteristics of the drainage households, law enforcement personnel often need to squat for a long time to collect evidence of sewage discharge. The present invention establishes a smart supervision platform to achieve online linkage between abnormal sewage discharge identification and alarm, online pollution tracing and auxiliary law enforcement evidence collection, effectively improving the efficiency of law enforcement evidence collection.

[0123] Real-time and accuracy issues of drainage network monitoring data: Traditional monitoring methods may have problems such as untimely data updates and insufficient accuracy. The present invention realizes real-time monitoring and data analysis of the drainage network by installing intelligent sensors at key nodes of the drainage network and combining Internet of Things technology and cloud computing platform.

[0124] Improved integration and intelligence of drainage network monitoring system: Existing monitoring systems may have information island problems, making data sharing and business collaboration difficult. This invention builds an integrated monitoring system, which improves the integration and intelligence of drainage network monitoring through a unified data platform and intelligent analysis tools.

[0125] Through the above technical scheme, the present invention can effectively improve the monitoring, management and pollution prevention and control capabilities of the drainage network, and provide strong technical support for urban water resources management and environmental protection.

[0126] The present invention is particularly suitable for urban drainage pipe networks, industrial zone drainage systems and other occasions where water quality monitoring and management are required.

[0127] like Figure 1 Shown: Schematic diagram of the drainage network monitoring system architecture;

[0128] (1) Data collection layer: includes various types of sensors and field data collectors to collect data on different parameters.

[0129] (2) Data transmission layer: involves different data transmission protocols and network security measures to ensure data security and integrity.

[0130] (3) Cloud platform processing layer: includes data lake, real-time processing engine, batch processing engine and machine learning platform, which are used to store and analyze the collected data.

[0131] (4) Application service layer: includes key services such as decision support system, resource management, user management, and alarm and event management.

[0132] (5) Display layer: includes data visualization dashboard, mobile app and alarm system, providing information display and interactive interface for end users.

[0133] like Figure 2 Shown: Invariant analysis flow chart;

[0134] (1) Data preprocessing: Preprocess the collected raw data, such as noise reduction and standardization.

[0135] (2) Data quality check: Check the completeness, consistency and accuracy of the data.

[0136] (3) Data cleaning: If the data quality check fails, data cleaning is performed, such as removing outliers and filling missing values.

[0137] (4) Feature extraction: Extract features that are helpful for analysis from the cleaned data.

[0138] (5) Feature selection: Select the most representative features from the extracted features.

[0139] (6) Feature Engineering: If the features are not sufficient to build a model, feature engineering is performed, such as feature construction and conversion.

[0140] (7) Construct an invariant model: Construct an invariant analysis model using the selected features.

[0141] (8) Training the invariant model: Use historical data to train the model.

[0142] (9) Verify model accuracy: Verify the accuracy of the model through methods such as cross-validation.

[0143] (10) Adjust model parameters: If the model accuracy is insufficient, adjust the model parameters.

[0144] (11) Apply the invariant model: Apply the trained model to real-time data monitoring.

[0145] (12) Real-time data monitoring: Use models to monitor real-time data.

[0146] (13) Pollution source location: Once an anomaly is detected, locate the pollution source.

[0147] (14) Pollution path tracing: tracing the path of pollution spread.

[0148] (15) Generate alarm: Generate alarm based on the pollution path tracking results.

[0149] (16) Notify relevant personnel: Notify relevant personnel through various communication means (such as SMS, email, APP push, etc.).

[0150] like Figure 3 Shown: Abnormal emission alarm example interface:

[0151] (1) Menu bar: includes menu options such as File, View and Help.

[0152] (2) Real-time data monitoring: Displays the real-time data of the current drainage network.

[0153] (3) Alarm list: Lists all abnormal emission alarms, including alarm level, timestamp, location, details, and operations.

[0154] (4) Map view: Displays a map of the drainage network and marks pollution sources and pollution paths on the map.

[0155] (5) Statistical analysis: Provides charts of historical alarm statistics and pollution source distribution.

[0156] (6) Settings: Allows users to manage users, set alarm thresholds, and set notification preferences.

[0157] (7) Operation: Users can confirm, ignore, or view details of the alarm.

[0158] (8) Map operations: Users can zoom in, zoom out, locate pollution sources, and track paths by clicking on the map.

[0159] (9) Chart type selection: Users can select different chart types to view historical alarm statistics.

[0160] (10) Distribution map type: Users can select different distribution map types to view the distribution of pollution sources.

[0161] Specific embodiment: A drainage network pollution tracing and alarm system based on invariant analysis, comprising a data collection module, a cloud platform processing module, an invariant analysis module, an abnormal emission identification and alarm module and a decision support system;

[0162] Among them, the data collection module is used to install a variety of sensors at key nodes of the drainage network, including but not limited to water quality sensors, water level sensors, water volume sensors and flow rate sensors, to monitor the status of the drainage network in real time; the data types collected include real-time monitoring data, manual irregular sampling monitoring data, factory hydraulic and water quality monitoring data, and water meter reading data to ensure the comprehensiveness and diversity of the data; real-time synchronization of sensor data is achieved through the Internet of Things technology to ensure the timeliness and accuracy of the data.

[0163] The cloud platform processing module is used to use the MindSphere cloud platform to provide powerful data processing and storage capabilities. The cloud platform transmits, stores and analyzes the collected data in real time, and uses the powerful computing power of cloud computing to quickly process massive data. It ensures the security of data during transmission and storage, and uses encryption technology and access control mechanisms to protect data from unauthorized access.

[0164] The invariant analysis module is used for intelligent algorithms based on invariant analysis. It can extract key features from complex drainage network data to form invariants. It can quickly locate pollution sources and improve the accuracy of tracing by analyzing the differences between real-time data and normal operation models. It can also use invariant analysis technology to track pollution paths and provide a scientific basis for pollution control.

[0165] Abnormal emission identification and alarm module, using MindSphere alarm analysis module, sets abnormal emission identification rules based on monitoring indicators, including time series jump, exceeding threshold, 3σ deviation; can automatically identify abnormal emission events, reduce manual intervention, and improve response speed; when abnormal emission is detected, the system immediately sends an alarm to relevant personnel through SMS, email or APP push, etc., to ensure timely response;

[0166] The decision support system provides pollution control strategies and resource optimization allocation suggestions based on the results of source tracing analysis to help managers make scientific decisions. It can provide decision support to managers based on real-time data and historical data analysis, including emergency response to pollution incidents and resource scheduling.

[0167] like Figure 4 As shown, the principle of the data collection module specifically includes:

[0168] Sensor selection and deployment: Select and deploy a range of high-precision sensors, including water quality sensors, water level sensors, water volume sensors, and flow rate sensors, and integrate the sensors into a monitoring station to achieve comprehensive monitoring of key nodes in the drainage network; use machine learning algorithms to analyze historical sensor data, predict and automatically trigger calibration cycles to maintain long-term data stability;

[0169] Adaptive sampling: Adaptive sampling algorithms dynamically adjust sampling frequency and sampling points based on real-time monitoring data trends and historical data patterns. The algorithms can identify abnormal patterns in data and automatically increase sampling frequency to capture key information. Intelligent algorithms optimize sampling resource allocation, reduce unnecessary sampling, and lower operating costs, while ensuring high coverage and timeliness of key data.

[0170] Wireless Sensor Network (WSN): Building a wireless sensor network enables sensor nodes to self-organize into a network without relying on a central node, thus enhancing the robustness of the system; using a low-power communication protocol to extend the battery life of the sensor, reduce maintenance frequency, and ensure real-time data transmission;

[0171] Edge computing integration: Integrate edge computing modules in sensor nodes or gateways to pre-process raw data, including data cleaning, feature extraction and preliminary analysis, to reduce the amount of data that needs to be transmitted to the cloud; use the fast processing capabilities of edge computing to respond to key events immediately, such as immediately launching emergency procedures when abnormal water quality is detected;

[0172] Data fusion and calibration mechanism: Through the data fusion algorithm, data from different sensors and monitoring points are integrated to improve the consistency and accuracy of the data; using machine learning technology, the data fusion model is continuously optimized and the calibration parameters are automatically adjusted to adapt to the impact of environmental changes and sensor aging.

[0173] like Figure 5 As shown, the principle of the cloud platform processing module specifically includes:

[0174] Multi-tenant architecture: adopts advanced multi-tenant cloud platform architecture to provide independent data storage and processing space for each user or organization, ensuring data isolation and privacy protection; realizes dynamic allocation and elastic expansion of resources, automatically adjusts computing and storage resources according to actual needs and usage of users, and improves resource utilization and cost-effectiveness;

[0175] Distributed data processing: Deploy efficient parallel computing frameworks, such as Apache Spark or Hadoop, to achieve distributed data processing and analysis, and improve the speed and efficiency of data processing; adopt intelligent load balancing strategies to automatically allocate computing tasks to different computing nodes according to the computing complexity and data volume of the tasks, to ensure the efficient and stable operation of the cloud platform;

[0176] Data lake technology: Build a unified data lake architecture to support the storage of multiple data formats and types, including structured data, semi-structured data, and unstructured data; integrate machine learning and artificial intelligence algorithms to conduct in-depth analysis and mining of data stored in the data lake, discover potential value and patterns in the data, and provide support for decision-making;

[0177] Intelligent data quality management: Through automated data cleaning tools, the collected data is pre-processed to remove duplicate, erroneous or incomplete data to ensure data quality and consistency. The integrity, accuracy and timeliness of the data are continuously monitored to detect and handle data anomalies in a timely manner to ensure data reliability.

[0178] End-to-end encryption and access control: End-to-end encryption technology, TLS / SSL, is used to ensure the security of data during transmission and prevent data from being intercepted or tampered with during transmission; it is used to implement the role-based access control RBAC mechanism, which finely controls access to data and resources based on the user's role and permissions to prevent unauthorized access and data abuse.

[0179] like Figure 6 As shown, the principle of the invariant analysis module specifically includes

[0180] Adaptive invariant identification algorithm: Through the adaptive invariant identification algorithm, the feature extraction and analysis strategy can be automatically adjusted according to the characteristics of real-time data and the dynamic changes of the drainage network; integrating machine learning models, neural networks or support vector machines to continuously learn and optimize the invariant identification process, improve the generalization ability and accuracy of the algorithm;

[0181] Among them, feature extraction: Assume that the test data set is, X = {x1, x2, ... x n}, where x n is the data collected at time point n; the feature extraction function f is used to extract invariant features from the data: f:X→Ff: where F is the feature space;

[0182] Adaptive adjustment: Let T be the parameter set of the analysis strategy, and adaptive adjustment can be expressed as: T = g(X, T prev ), where g is the adjustment function, T prev is the previous parameter set;

[0183] Machine learning model: Taking a neural network as an example, the learning process can be expressed as an update of weights and biases:

[0184] Where W and b represent weight and bias respectively, η is the learning rate, and L is the loss function; the optimization goal of the support vector machine SVM is to maximize the interval: At the same time, satisfying y i (w·φ(x i )+b)≥1,for all i; where w and b are model parameters, φ is the kernel function that maps data to a high-dimensional space, and y i is the label of the data;

[0185] Generalization ability evaluation: Use cross-validation to evaluate the generalization ability of the model, such as k-fold cross-validation: Among them, L i is the loss function of the i-th fold;

[0186] Dynamic feature adjustment: According to the feature changes of real-time data, the feature extraction strategy is dynamically adjusted, which can be expressed as: new =h(f old ,X); where h is the adjustment function, f old is the old feature extraction function;

[0187] Characteristics and dynamic changes of real-time data: Assume that the characteristic vector of real-time data is x = [x1, x2, ..., x d ], dynamic changes can be captured by time series analysis, using an autoregressive model: t =c+φ1x t-1+φ2x t-2 +…+φ p x t-p +∈ t ; where c is a constant term, φ i are model parameters, p is the time delay, ∈ t is the error term;

[0188] Comparative analysis of real-time data and models: Through efficient data processing processes, ensure that real-time data can be quickly processed and compared with the preset normal operation model; build an anomaly detection system that can use statistical methods or machine learning algorithms to quickly identify data patterns that are significantly different from the normal model, thereby discovering potential pollution events; where the data processing process: suppose the real-time data set is X = {x1, x2, …, x n}, where x i is the data collected at time point i;

[0189] The data processing flow can be represented as a series of operations P:X processed =P(X), where X processe d is the processed data set;

[0190] Model comparison: Let the normal operation model be M, and the model comparison can be expressed as the difference metric D: D = d(X processed ,M), where ,d is the metric function;

[0191] Anomaly detection: Statistical methods, including control charts, expressed as: C = {x∈X processed :|x-μ>kσ}, where μ is the mean, σ is the standard deviation, and k is the control chart constant;

[0192] Machine learning algorithms, such as Isolation Forest: IsolationForses(X processed )→Scores; where Scores represents the sample anomaly score;

[0193] Support vector machine (SVM) is used to classify normal and abnormal data: f(x) = w·φ(x) + b; where φ is the kernel function that maps the data to a high-dimensional space, and w and b are model parameters;

[0194] Autoencoder for unsupervised learning anomaly detection: Among them, f θ and g θ are the encoding and decoding functions of the autoencoder, respectively, and θ is the model parameter;

[0195] Anomaly determination: Set a threshold r. If the difference measure D exceeds r, an anomaly is considered to be detected:

[0196] if D>τ,then anomaly detected;

[0197] Real-time feedback: According to the abnormal detection results, the data processing flow or model parameters are adjusted in real time to improve the detection accuracy;

[0198] Multi-source data fusion analysis: Establish a data integration mechanism that can effectively integrate data from different sensors and monitoring points to form a unified data view; use data consistency verification tools to compare information from different data sources, eliminate inconsistent or erroneous data, and ensure the accuracy of analysis results:

[0199] Data integration mechanism: Let the data sets of different sensors and monitoring points be X1, X2, …, X n ; The data integration function I is used to merge these data sets into a unified data set X combined :X combined =I(X1,X2,…,X n );

[0200] Data preprocessing: Preprocess each data set, including denoising, normalization, etc.: X i,processed =P(x i ), where P is the preprocessing function;

[0201] Data consistency verification: Let the data consistency verification function be V, which is used to compare information from different data sources and exclude inconsistent data: X verified =V(X combined );

[0202] Outlier detection: Use statistical methods, standard deviation or interquartile range to detect and exclude outliers: Q1 = quantile (X verified ,0.25),Q3=quantile(X verified ,0.75)IQR=Q3-Q1; Outliers={x∈X verified :x<Q1-k·IQRorx>Q3+k·IQR}; where k is a constant, usually 1.5;

[0203] Data fusion: Use weighted average and other methods to fuse data: Among them, w i is the dataset X i The weight of the data is determined based on data quality or other criteria;

[0204] Error correction: If there are systematic errors between data sources, correct them through linear regression or other methods: in, is the corrected data, a and b are parameters determined by the least squares method and other methods;

[0205] Update mechanism: Set up an update mechanism to update the fusion results when new data arrives: X updated =I(X final ,X new ), where X new is the newly arrived data;

[0206] Intelligent pollution path tracking: Through intelligent algorithms, the propagation process of pollutants in the drainage network is simulated, the pollution path is tracked, and a scientific basis is provided for pollution control; a reverse tracing strategy is implemented, and the algorithm is used to start from the downstream of the pollution event and trace back to the pollution source, thereby improving the speed and accuracy of tracing;

[0207] Path tracing algorithm: Let the pollutant concentration data be C(t), a function of time t, and the tracing algorithm be expressed as P(t) = Trace(C(t), V(t)), where P(t) is the pollution path at time t, V(t) is the fluid velocity, and Trace is the tracing algorithm;

[0208] Fluid Dynamics Model: Use the fluid dynamics equations, including the Navier-Stokes equations, to simulate the propagation of pollutants: Where v is the fluid velocity, p is the pressure, μ is the dynamic viscosity, f is the external force, and p is the fluid density;

[0209] Reverse tracing strategy: Let S be the set of pollution sources and D be the detection point of the pollution event. The reverse tracing algorithm can be expressed as: source =ReverseTrace(D,C(D)) where S source It is the inferred pollution source, and ReverseTrace is the reverse tracing algorithm;

[0210] Probability model: Use probability model to evaluate the relative likelihood of different pollution sources: P(S i D)∝P(C(D)S i )·P(S i ) Among them, P(S i |D) is the posterior probability that the pollution source comes from S when pollution is observed at detection point D, P(C(D)|S i ) is at the pollution source S i Under the condition of , the likelihood probability of observing contamination C(D), P(S i ) is the pollution source S i The prior probability of

[0211] Data assimilation: Using data assimilation techniques such as Kalman filtering, combined with model predictions and actual observation data, to improve the accuracy of pollution path tracking: in, is the analysis state, is the predicted state, K k is the Kalman gain, z k is the observed value, H k is the observation operator;

[0212] Optimization algorithm: Use optimization algorithm, such as genetic algorithm or particle swarm optimization, to find the optimal location of pollution sources: Among them, Cost is the cost function used to evaluate the pollution source S i The pollution to the detection point D

[0213] the costs of infection transmission pathways;

[0214] (5) Predictive maintenance and optimization: Build a prediction model based on historical data and current trends to predict possible pollution events in the future and provide decision support for preventive measures; based on the prediction results, optimize the maintenance and cleaning strategies of the drainage network to reduce the occurrence of pollution events and improve the operating efficiency and reliability of the drainage network;

[0215] Prediction model construction: Assume that the historical data set is H = {h1,h2,…,h m}, the current trend is T = {t1, t2, …, t n}, the prediction model is expressed as: F = Model(H, T), where F is the prediction of future events;

[0216] Time Series Analysis: Time Series Forecasting Using the Autoregressive Integrated Moving Average ARIMA Model: x t =c+φ1x t-1 +φ2x t-2 +…+φ p x t-p +θ1∈ t-1 +…+θ q ∈ t-q +∈ t ; where x t is the value at time t, c is a constant term, φ i and θ i are model parameters, p and q are the orders of the autoregressive and moving average terms, ∈ t is the error term;

[0217] Machine learning prediction: Use machine learning algorithms, including random forests or neural networks, to build a prediction model: F(X) = f(w, b, X); where F is the prediction function, w and b are model parameters, and X is the input feature;

[0218] Maintenance strategy optimization: Let the maintenance strategy be S, and the optimization goal is to minimize the frequency of pollution events and maintenance costs: S * =ArgMin S (Cost(S)+λ·R(S)); where S * is the optimal maintenance strategy, Cost(S) is the maintenance cost, R(S) is the frequency of pollution events, and λ is the trade-off factor;

[0219] Genetic Algorithm Optimization: Use genetic algorithms to find the optimal maintenance strategy:

[0220] Snew=GeneticAlgorithm(Spopulation,Fitness,MutationRate,SelectionPressure); where Spopulation is the population of the maintenance strategy, Fitness is the fitness function, MutationRate is the mutation rate, and SelectionPressure is the selection pressure;

[0221] Simulated annealing algorithm optimization: Use simulated annealing algorithm to optimize the maintenance strategy to avoid local optimal solution: S new =SimulatedAnnealing(S current ,T,CoolingRate) where S current is the current maintenance strategy, T is the initial temperature, and CoolingRate is the cooling rate;

[0222] Real-time feedback adjustment: Dynamically adjust maintenance strategies based on real-time data and prediction results:

[0223] Supdated = UpdateStrategy(S * ,F,X real-time ) where Supdated is the updated maintenance policy, X real-time It is real-time data.

[0224] like Figure 7 As shown, the principle of the abnormal emission identification and alarm module includes:

[0225] Using MindSphere's alarm analysis module, set up abnormal emission identification rules based on monitoring indicators, including time series jumps, exceeding thresholds, and 3σ deviations; the system can automatically identify abnormal emission events, reduce manual intervention, and improve response speed; once abnormal emissions are detected, the system immediately sends an alarm to relevant personnel via SMS, email, or APP push to ensure timely response;

[0226] Self-learning alarm thresholds: Use dynamic threshold algorithms to automatically adjust alarm thresholds based on real-time monitoring data and historical trends to adapt to environmental changes and operating conditions; integrate machine learning models, including random forests or neural networks, to analyze historical alarm events, learn and predict abnormal emission patterns, and thus optimize alarm rules;

[0227] Multi-parameter comprehensive analysis: Implement multi-parameter correlation analysis to identify the relationship between different monitoring parameters and improve the accuracy of abnormal emission identification; adopt a weight allocation mechanism to assign different weights according to the importance of parameters to abnormal emission identification, and optimize the sensitivity and accuracy of alarm rules;

[0228] Real-time data analysis engine: Build a high-speed data processing engine that can analyze a large amount of monitoring data in real time and quickly identify abnormal emission events; implement a complex event processing mechanism that can analyze simultaneous abnormalities of multiple monitoring parameters and improve the system's comprehensive response capabilities;

[0229] Intelligent alarm suppression and merging: Adopt intelligent alarm suppression strategies to identify and suppress repeated or unimportant alarms, reduce unnecessary alarm notifications, and avoid alarm fatigue; adopt alarm merging algorithms to merge related alarm events into a comprehensive alarm, providing clearer event descriptions and handling suggestions; alarm merging algorithms are designed to merge related alarm events into a comprehensive alarm, providing clearer event descriptions and handling suggestions; merging algorithms are usually based on alarm similarity, timestamps, and spatial location factors;

[0230] Similarity metric: Define the similarity metric S(A i ,A j ), using Euclidean distance and cosine similarity methods; setting merging conditions, including similarity threshold ∈ and time window T; if two alarms meet the merging conditions, they are merged into a comprehensive alarm;

[0231] Similarity measure: Where f(A) is the feature vector of alarm A;

[0232] Alarm merging judgment: Among them, t i and t j It is an alarm A i and A j Timestamp of; merge operation: A merged =Combine(A i ,A j ), where A merged It is a combined alarm, which contains the key information and handling suggestions of the combined alarm.

[0233] Multi-channel alarm notification system: Implement a customized notification system that allows users to set the content and method of alarm notifications based on their roles and preferences, improving the accessibility and effectiveness of information; through an interactive alarm response mechanism, allow users to directly confirm receipt of alarms and provide feedback via SMS, email or APP, enhancing the interactivity of the system and user participation.

[0234] like Figure 8 As shown, the principle of the decision support system specifically includes:

[0235] Based on the results of source tracing analysis, it provides pollution control strategies and resource optimization allocation suggestions to help managers make scientific decisions; it can provide decision-making support for managers based on real-time data and historical data analysis, such as emergency response to pollution incidents and resource scheduling;

[0236] Predictive decision-making model: Use scenario analysis tools, historical data and real-time monitoring data to simulate possible future pollution events and environmental changes, and provide managers with a variety of possible future development scenarios; build a dynamic strategy generation system to automatically propose pollution control strategies and resource allocation plans that adapt to different scenarios based on the results of scenario analysis, making decision-making more flexible and forward-looking;

[0237] Intelligent resource scheduling algorithm: Use intelligent resource scheduling algorithm to evaluate the urgency and treatment requirements of different pollution events, optimize resource allocation and scheduling, and ensure that resources are effectively used where they are most needed; implement a real-time scheduling system that can dynamically adjust resource scheduling plans based on the latest event progress and resource usage, and improve the real-time and flexibility of resource scheduling;

[0238] Interactive decision support interface: Create a user-friendly interactive decision support interface that enables managers to intuitively view data, analysis results and strategy recommendations, thereby improving the efficiency and accuracy of decision-making; integrate a variety of decision-making support tools into the interface, including risk assessment tools, cost-benefit analysis tools, etc., to provide managers with comprehensive decision support and help them evaluate the pros and cons of different decision plans from multiple perspectives.

[0239] Example 1: Inflow monitoring of urban sewage treatment plants;

[0240] Background: Urban wastewater treatment plants need to monitor influent water quality in real time to ensure the stability and effectiveness of the treatment process.

[0241] Implementation steps:

[0242] Data collection: Install sensors for pH, dissolved oxygen, chemical oxygen demand (COD), etc. in the inflow pipes of the sewage treatment plant.

[0243] Real-time monitoring: Data is collected in real time through wireless sensor networks and transmitted to the cloud platform.

[0244] Alarm settings: Set alarm rules on the cloud platform, such as pH value exceeding the range of 6-9 and COD exceeding 500mg / L.

[0245] Alarm suppression: Develop algorithms to identify and suppress repeated alarms within a short period of time, such as multiple consecutive pH value violations.

[0246] Alarm merging: Merge multiple related alarms into one comprehensive alarm, such as when multiple sensors detect that COD exceeds the standard at the same time.

[0247] Effect: It realizes real-time monitoring of the inflow of sewage treatment plants. It reduces false alarms and repeated alarms, and improves the work efficiency of operators. It responds to water quality abnormalities in a timely manner, ensuring the quality and efficiency of sewage treatment.

[0248] Example 2: Leakage detection of drainage pipe network in industrial area;

[0249] Background: The drainage network in industrial areas is complex and leak detection is crucial to prevent groundwater contamination.

[0250] Implementation steps:

[0251] Data collection: Install flow rate sensors and water level sensors at key nodes of the drainage network.

[0252] Data transmission: Data is transmitted to the cloud platform in real time via cellular networks.

[0253] Anomaly detection: Analyze data using machine learning models to identify leak patterns.

[0254] Alarm suppression: Implement intelligent alarm suppression strategies to ignore temporary water level changes caused by rainfall.

[0255] Alarm merging: Merge multiple leakage alarms in the same area into one regional leakage alarm.

[0256] Effect: Timely discovery and location of leakage points in the drainage network. Reduced unnecessary on-site inspections due to false alarms. Strengthened supervision of the drainage network in industrial areas and reduced environmental pollution risks.

[0257] Example 3: Pollution monitoring of rainwater outlets in residential areas;

[0258] Background: Pollution monitoring at stormwater outfalls helps control urban non-point source pollution.

[0259] Implementation steps:

[0260] Data collection: Install rainfall sensors and water quality sensors at stormwater outfalls.

[0261] Data synchronization: Synchronize data to the cloud platform via satellite communication.

[0262] Pollution Forecasting: Build predictive models to predict changes in water quality after rainfall events.

[0263] Alarm settings: Set alarm thresholds, such as when the SS (suspended solids) concentration exceeds 150 mg / L after rainfall.

[0264] Alarm merging: Merge pollution alarms from multiple emission outlets caused by the same rainfall event.

[0265] Effect: Improved the ability to control urban non-point source pollution. Early warning through prediction models provided decision-making support for environmental management in residential areas. Reduced problems such as eutrophication of water bodies caused by urban non-point source pollution.

[0266] The present invention realizes real-time monitoring and rapid response: real-time collection and analysis of drainage network data, rapid identification of abnormal emission events and timely alarm, thus improving the response speed to pollution events. Accurate tracing: using advanced invariant analysis technology, combined with cloud computing and big data analysis, accurately locates pollution sources and emission paths. Intelligent alarm management: through alarm suppression strategies, reduce repeated or unimportant alarms, avoid alarm fatigue, and provide clearer event descriptions through alarm merging algorithms. Data fusion and analysis: effectively integrate data from different sensors and monitoring points to improve data consistency and analysis accuracy. Predictive maintenance: based on historical data and current trends, build a prediction model to predict possible future pollution events and provide decision support for maintenance and cleaning strategies. User-friendly interface: provide intuitive data visualization tools and user-friendly operation interface, simplify user operation processes and improve user experience. Reduce environmental pollution: through timely alarms and pollution source location, reduce the impact of illegal discharge on the environment and protect water resources. Improve supervision efficiency: use intelligent algorithms and big data analysis to improve the supervision efficiency of drainage networks and reduce the cost of manual inspections. Decision support: Provide strategic recommendations based on data analysis to assist managers in making scientific decisions. System reliability: Adopt multi-tenant architecture, distributed processing and data security technology to ensure system stability and data security. Flexibility and scalability: The system design takes flexibility and scalability into consideration and can adapt to the needs of drainage networks of different scales and complexities. Integrated monitoring: An integrated monitoring system is built to improve the integration and intelligence level of monitoring through a unified data platform and intelligent analysis tools.

[0267] Those skilled in the art can understand that the above are only preferred examples of the invention and are not intended to limit the invention. Although the invention is described in detail with reference to the above examples, those skilled in the art can still modify the technical solutions recorded in the above examples or replace some of the technical features with equivalents. Any modification, equivalent replacement, etc. made within the spirit and principle of the invention should be included in the protection scope of the invention. All technical features in this embodiment can be freely combined according to actual needs.

[0268] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A drainage network pollution tracing and warning system based on invariant analysis, characterized by: It includes data collection module, cloud platform processing module, invariant analysis module, abnormal emission identification and alarm module and decision support system; The data collection module is used to install a variety of sensors at key nodes of the drainage network, including but not limited to water quality sensors, water level sensors, water volume sensors and flow rate sensors, so as to monitor the status of the drainage network in real time; the types of data collected include real-time monitoring data, manual irregular sampling monitoring data, factory hydraulic and water quality monitoring data, and water meter reading data, to ensure the comprehensiveness and diversity of the data; the real-time synchronization of sensor data is achieved through the Internet of Things technology to ensure the timeliness and accuracy of the data; The cloud platform processing module is used to use the MindSphere cloud platform to provide powerful data processing and storage capabilities. The cloud platform transmits, stores and analyzes the collected data in real time, and uses the powerful computing power of cloud computing to quickly process massive data. It ensures the security of data during transmission and storage, and uses encryption technology and access control mechanisms to protect data from unauthorized access. The invariant analysis module is used for intelligent algorithms based on invariant analysis. It can extract key features from complex drainage network data to form invariants. It can quickly locate pollution sources and improve the accuracy of tracing by analyzing the differences between real-time data and normal operation models. It can also use invariant analysis technology to track pollution paths and provide a scientific basis for pollution control. Abnormal emission identification and alarm module, using MindSphere alarm analysis module, sets abnormal emission identification rules based on monitoring indicators, including time series jump, exceeding threshold, 3σ deviation; can automatically identify abnormal emission events, reduce manual intervention, and improve response speed; when abnormal emission is detected, the system immediately sends an alarm to relevant personnel through SMS, email or APP push, etc., to ensure timely response; The decision support system provides pollution control strategies and resource optimization allocation suggestions based on the results of source tracing analysis to help managers make scientific decisions. It can provide decision support to managers based on real-time data and historical data analysis, including emergency response to pollution incidents and resource scheduling.

2. The drainage network pollution tracing and alarm system based on invariant analysis according to claim 1 is characterized by: The principles of the data collection module specifically include: Sensor selection and deployment: Select and deploy a series of high-precision sensors, including water quality sensors, water level sensors, water volume sensors and flow rate sensors, and integrate the sensors into a monitoring station to achieve comprehensive monitoring of key nodes in the drainage network; Use machine learning algorithms to analyze historical sensor data to predict and automatically trigger calibration cycles to maintain long-term data stability; Adaptive sampling: Adaptive sampling algorithms dynamically adjust sampling frequency and sampling points based on real-time monitoring data trends and historical data patterns; algorithms can identify abnormal patterns in data and automatically increase sampling frequency to capture key information; intelligent algorithms optimize sampling resource allocation, reduce unnecessary sampling, and reduce operating costs, while ensuring high coverage and timeliness of key data Wireless Sensor Network (WSN): Building a wireless sensor network enables sensor nodes to self-organize into a network without relying on a central node, thus enhancing the robustness of the system; using a low-power communication protocol to extend the battery life of the sensor, reduce maintenance frequency, and ensure real-time data transmission; Edge computing integration: Integrate edge computing modules in sensor nodes or gateways to pre-process raw data, including data cleaning, feature extraction and preliminary analysis, to reduce the amount of data that needs to be transmitted to the cloud; use the fast processing capabilities of edge computing to respond to key events immediately, such as immediately launching emergency procedures when abnormal water quality is detected; Data fusion and calibration mechanism: Through the data fusion algorithm, data from different sensors and monitoring points are integrated to improve the consistency and accuracy of the data; using machine learning technology, the data fusion model is continuously optimized and the calibration parameters are automatically adjusted to adapt to the impact of environmental changes and sensor aging.

3. The drainage network pollution tracing and alarm system based on invariant analysis according to claim 1 is characterized by: The principles of the cloud platform processing module specifically include: Multi-tenant architecture: adopts advanced multi-tenant cloud platform architecture to provide independent data storage and processing space for each user or organization, ensuring data isolation and privacy protection; realizes dynamic allocation and elastic expansion of resources, automatically adjusts computing and storage resources according to actual needs and usage of users, and improves resource utilization and cost-effectiveness; Distributed data processing: Deploy efficient parallel computing frameworks, such as Apache Spark or Hadoop, to achieve distributed data processing and analysis, and improve the speed and efficiency of data processing; adopt intelligent load balancing strategies to automatically allocate computing tasks to different computing nodes according to the computing complexity and data volume of the tasks, to ensure the efficient and stable operation of the cloud platform; Data lake technology: Build a unified data lake architecture to support the storage of multiple data formats and types, including structured data, semi-structured data, and unstructured data; integrate machine learning and artificial intelligence algorithms to conduct in-depth analysis and mining of data stored in the data lake, discover potential value and patterns in the data, and provide support for decision-making; Intelligent data quality management: Use automated data cleaning tools to pre-process the collected data, remove duplicate, erroneous or incomplete data, ensure data quality and consistency, continuously monitor data integrity, accuracy and timeliness, and promptly detect and handle data anomalies to ensure data reliability; End-to-end encryption and access control: End-to-end encryption technology, TLS / SSL, is used to ensure the security of data during transmission and prevent data from being intercepted or tampered with during transmission; it is used to implement the role-based access control RBAC mechanism, which finely controls access to data and resources based on the user's role and permissions to prevent unauthorized access and data abuse.

4. The drainage network pollution tracing and alarm system based on invariant analysis according to claim 1 is characterized by: The principle of the invariant analysis module specifically includes Adaptive invariant identification algorithm: Through the adaptive invariant identification algorithm, the feature extraction and analysis strategy can be automatically adjusted according to the characteristics of real-time data and the dynamic changes of the drainage network; integrating machine learning models, neural networks or support vector machines to continuously learn and optimize the invariant identification process, improve the generalization ability and accuracy of the algorithm; Among them, feature extraction: Assume that the test data set is, X = {x1, x2, ... x n }, where x n is the data collected at time point n; the feature extraction function f is used to extract invariant features from the data: f:X→Ff: where F is the feature space; Adaptive adjustment: Let T be the parameter set of the analysis strategy, and adaptive adjustment can be expressed as: T = g(X, T prev ), where g is the adjustment function, T prev is the previous parameter set; Machine learning model: Taking a neural network as an example, the learning process can be expressed as an update of weights and biases: Where W and b represent weight and bias respectively, η is the learning rate, and L is the loss function; the optimization goal of the support vector machine SVM is to maximize the interval: At the same time, satisfying y i (w·φ(x i )+b)≥1,for all i; where w and b are model parameters, φ is the kernel function that maps data to a high-dimensional space, and y i is the label of the data; Generalization ability evaluation: Use cross-validation to evaluate the generalization ability of the model, such as k-fold cross-validation: Among them, L i is the loss function of the i-th fold; Dynamic feature adjustment: According to the feature changes of real-time data, the feature extraction strategy is dynamically adjusted, which can be expressed as: new =h(f old ,X); where h is the adjustment function, f old is the old feature extraction function; Characteristics and dynamic changes of real-time data: Assume that the characteristic vector of real-time data is x = [x1, x2, ..., x d ], dynamic changes can be captured by time series analysis, using an autoregressive model: t =c+φ1x t-1 +φ2x t-2 +…+φ p x t-p +∈ t ; where c is a constant term, φ i are model parameters, p is the time delay, ∈ t is the error term; Comparative analysis of real-time data and models: Through efficient data processing processes, ensure that real-time data can be quickly processed and compared with the preset normal operation model; build an anomaly detection system that can use statistical methods or machine learning algorithms to quickly identify data patterns that are significantly different from the normal model, thereby discovering potential pollution events; where the data processing process: suppose the real-time data set is X = {x1, x2, …, x n }, where x i is the data collected at time point i; The data processing flow can be represented as a series of operations P:X processed =P(X), where X processed is the processed data set; Model comparison: Let the normal operation model be M, and the model comparison can be expressed as the difference metric D: D = d(X processed ,M), where ,d is the metric function; Anomaly detection: Statistical methods, including control charts, expressed as: C = {x∈X processed :|x-μ|>kσ}, where μ is the mean, σ is the standard deviation, and k is the control chart constant; Machine learning algorithms, such as Isolation Forest: IsolationForses(X processed )→Scores; Among them, Scores represents the sample abnormality score; Support vector machine (SVM) is used to classify normal and abnormal data: f(x) = w·φ(x) + b; where φ is the kernel function that maps the data to a high-dimensional space, and w and b are model parameters; Autoencoder for unsupervised learning anomaly detection: Among them, f θ and g θ are the encoding and decoding functions of the autoencoder, respectively, and θ is the model parameter; Anomaly determination: Set a threshold r. If the difference measure D exceeds r, an anomaly is considered to be detected: if D>τ,then anomaly detected; Real-time feedback: According to the abnormal detection results, the data processing flow or model parameters are adjusted in real time to improve the detection accuracy; Multi-source data fusion analysis: Establish a data integration mechanism that can effectively integrate data from different sensors and monitoring points to form a unified data view; use data consistency verification tools to compare information from different data sources, eliminate inconsistent or erroneous data, and ensure the accuracy of analysis results: Data integration mechanism: Let the data sets of different sensors and monitoring points be X1, X2, …, X n ; The data integration function I is used to merge these data sets into a unified data set X combined :X combined =I(X1,X2,…,X n ); Data preprocessing: Preprocess each data set, including denoising, normalization, etc.: X i,processed =P(x i ), where P is the preprocessing function; Data consistency verification: Let the data consistency verification function be V, which is used to compare information from different data sources and exclude inconsistent data: X verified =V(X combined ); Outlier detection: Use statistical methods, standard deviation or interquartile range to detect and exclude outliers: Q1 = quantile (X verified ,0.25),Q3=quantile(X verified ,0.75)IQR=Q3-Q1; Outliers={x∈X verified :x<Q1-k·IQRorx>Q3+k·IQR}; where k is a constant, usually 1.5; Data fusion: Use weighted average and other methods to fuse data: Among them, w i is the dataset X i The weight of the data is determined based on data quality or other criteria; Error correction: If there are systematic errors between data sources, correct them through linear regression or other methods: in, is the corrected data, a and b are parameters determined by the least squares method and other methods; Update mechanism: Set up an update mechanism to update the fusion results when new data arrives: X updated =I(X final ,X new ), where X new is the newly arrived data; Intelligent pollution path tracking: Through intelligent algorithms, the propagation process of pollutants in the drainage network is simulated, the pollution path is tracked, and a scientific basis is provided for pollution control; a reverse tracing strategy is implemented, and the algorithm is used to start from the downstream of the pollution event and trace back to the pollution source, thereby improving the speed and accuracy of tracing; Path tracing algorithm: Let the pollutant concentration data be C(t), a function of time t, and the tracing algorithm be expressed as P(t) = Trace(C(t), V(t)), where P(t) is the pollution path at time t, V(t) is the fluid velocity, and Trace is the tracing algorithm; Fluid Dynamics Model: Use the fluid dynamics equations, including the Navier-Stokes equations, to simulate the propagation of pollutants: Where v is the fluid velocity, p is the pressure, μ is the dynamic viscosity, f is the external force, and p is the fluid density; Reverse tracing strategy: Let S be the set of pollution sources and D be the detection point of the pollution event. The reverse tracing algorithm can be expressed as: source =ReverseTrace(D,C(D)) where S source It is the inferred pollution source, and ReverseTrace is the reverse tracing algorithm; Probability model: Use probability model to evaluate the relative likelihood of different pollution sources: P(S i D)∝P(C(D)S i )·P(S i ) Among them, P(S i |D) is the posterior probability that the pollution source comes from S when pollution is observed at detection point D, P(C(D)|S i ) is at the pollution source S i Under the condition of , the likelihood probability of observing contamination C(D), P(S i ) is the pollution source S i The prior probability of Data assimilation: Using data assimilation techniques such as Kalman filtering, combined with model predictions and actual observation data, to improve the accuracy of pollution path tracking: in, is the analysis state, is the predicted state, K k is the Kalman gain, z k is the observed value, H k is the observation operator; Optimization algorithm: Use optimization algorithm, such as genetic algorithm or particle swarm optimization, to find the optimal location of pollution sources: Among them, Cost is the cost function used to evaluate the pollution source S i The pollution to the detection point D the costs of infection transmission pathways; Predictive maintenance and optimization: Build a prediction model based on historical data and current trends to predict possible pollution events in the future and provide decision support for preventive measures; based on the prediction results, optimize the maintenance and cleaning strategies of the drainage network, reduce the occurrence of pollution events, and improve the operating efficiency and reliability of the drainage network; Prediction model construction: Assume that the historical data set is H = {h1,h2,…,h m }, the current trend is T = {t1, t2, …, t n }, the prediction model is expressed as: F = Model(H, T), where F is the prediction of future events; Time Series Analysis: Time Series Forecasting Using the Autoregressive Integrated Moving Average ARIMA Model: x t =c+φ1x t-1 +φ2x t-2 +…+φ p x t-p +θ1∈ t-1 +…+θ q ∈ t-q +∈ t ; where x t is the value at time t, c is a constant term, φ i and θ i are model parameters, p and q are the orders of the autoregressive and moving average terms, ∈ t is the error term; Machine learning prediction: Use machine learning algorithms, including random forests or neural networks, to build a prediction model: F(X) = f(w, b, X); where F is the prediction function, w and b are model parameters, and X is the input feature; Maintenance strategy optimization: Let the maintenance strategy be S, and the optimization goal is to minimize the frequency of pollution events and maintenance costs: S * =ArgMin S (Cost(S)+λ·R(S)); where S * is the optimal maintenance strategy, Cost(S) is the maintenance cost, R(S) is the frequency of pollution events, and λ is the trade-off factor; Genetic Algorithm Optimization: Use genetic algorithms to find the optimal maintenance strategy: Snew=GeneticAlgorithm(Spopulation,Fitness,MutationRate,SelectionPressure); Among them, Spopulation is the population of the maintenance strategy, Fitness is the fitness function, MutationRate is the mutation rate, and SelectionPressure is the selection pressure; Simulated annealing algorithm optimization: Use the simulated annealing algorithm to optimize the maintenance strategy to avoid local optimal solutions: S new =SimulatedAnnealing(S current ,T,CoolingRate) where S current is the current maintenance strategy, T is the initial temperature, and CoolingRate is the cooling rate; Real-time feedback adjustment: Dynamically adjust maintenance strategies based on real-time data and prediction results: Supdated = UpdateStrategy(S * ,F,X real-time ) where Supdated is the updated maintenance policy, X real-time It is real-time data.

5. The drainage network pollution tracing and alarm system based on invariant analysis according to claim 1 is characterized by: The principles of the abnormal emission identification and alarm module include: Using MindSphere's alarm analysis module, set up abnormal emission identification rules based on monitoring indicators, including time series jumps, exceeding thresholds, and 3σ deviations; the system can automatically identify abnormal emission events, reduce manual intervention, and improve response speed; once abnormal emissions are detected, the system immediately sends an alarm to relevant personnel via SMS, email, or APP push to ensure timely response; Self-learning alarm thresholds: Use dynamic threshold algorithms to automatically adjust alarm thresholds based on real-time monitoring data and historical trends to adapt to environmental changes and operating conditions; integrate machine learning models, including random forests or neural networks, to analyze historical alarm events, learn and predict abnormal emission patterns, and thus optimize alarm rules; Multi-parameter comprehensive analysis: Implement multi-parameter correlation analysis to identify the relationship between different monitoring parameters and improve the accuracy of abnormal emission identification; adopt a weight allocation mechanism to assign different weights according to the importance of parameters to abnormal emission identification, and optimize the sensitivity and accuracy of alarm rules; Real-time data analysis engine: Build a high-speed data processing engine that can analyze a large amount of monitoring data in real time and quickly identify abnormal emission events; implement a complex event processing mechanism that can analyze simultaneous abnormalities of multiple monitoring parameters and improve the system's comprehensive response capabilities; Intelligent alarm suppression and merging: Adopt intelligent alarm suppression strategies to identify and suppress repeated or unimportant alarms, reduce unnecessary alarm notifications, and avoid alarm fatigue; adopt alarm merging algorithms to merge related alarm events into a comprehensive alarm, providing clearer event descriptions and handling suggestions; The alarm merging algorithm aims to merge related alarm events into a comprehensive alarm, providing clearer event descriptions and handling suggestions; The merging algorithm is usually based on the similarity, timestamp and spatial location factors of the alarms; Similarity metric: Define the similarity metric S(A i ,A j ), using Euclidean distance and cosine similarity methods; Set the merging conditions, including the similarity threshold ∈ and the time window T; if two alarms meet the merging conditions, they are merged into a comprehensive alarm; Similarity measure: Where f(A) is the feature vector of alarm A; Alarm merging judgment: Among them, t i and t j It is an alarm A i and A j Timestamp of; merge operation: A merged =Combine(A i ,A j ), where A merged It is a combined alarm, which contains the key information and handling suggestions of the combined alarm. Multi-channel alarm notification system: Implement a customized notification system that allows users to set the content and method of alarm notifications based on their roles and preferences, improving the accessibility and effectiveness of information; through an interactive alarm response mechanism, allow users to directly confirm receipt of alarms and provide feedback via SMS, email or APP, enhancing the interactivity of the system and user participation.

6. The drainage network pollution tracing and alarm system based on invariant analysis according to claim 1 is characterized by: The principles of the decision support system specifically include: Based on the results of source tracing analysis, it provides pollution control strategies and resource optimization allocation suggestions to help managers make scientific decisions; it can provide decision-making support for managers based on real-time data and historical data analysis, such as emergency response to pollution incidents and resource scheduling; Predictive decision-making model: Use scenario analysis tools, historical data and real-time monitoring data to simulate possible future pollution events and environmental changes, and provide managers with a variety of possible future development scenarios; build a dynamic strategy generation system to automatically propose pollution control strategies and resource allocation plans that adapt to different scenarios based on the results of scenario analysis, making decision-making more flexible and forward-looking; Intelligent resource scheduling algorithm: Use intelligent resource scheduling algorithm to evaluate the urgency and treatment requirements of different pollution events, optimize resource allocation and scheduling, and ensure that resources are effectively used where they are most needed; implement a real-time scheduling system that can dynamically adjust resource scheduling plans based on the latest event progress and resource usage, and improve the real-time and flexibility of resource scheduling; Interactive decision support interface: Create a user-friendly interactive decision support interface that enables managers to intuitively view data, analysis results and strategy recommendations, thereby improving the efficiency and accuracy of decision-making; integrate a variety of decision-making support tools into the interface, including risk assessment tools, cost-benefit analysis tools, etc., to provide managers with comprehensive decision support and help them evaluate the pros and cons of different decision plans from multiple perspectives.

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