Sewage pipe network sudden emission analysis method based on online data

Abstract

The invention relates to the technical field of environmental protection, and discloses a sewage pipe network sudden discharge analysis method based on online data, which comprises the following steps: step 1, arranging sensors at a plurality of key nodes of a sewage pipe network, and collecting sewage flow, water quality parameters and pressure data; step 2, carrying out data preprocessing on the collected online data, removing noise and abnormal values, and carrying out data format conversion; 3, identifying an abnormal emission event; step 4, classifying the sudden emission events through an event classification and evaluation module; 5, generating an alarm and notifying related personnel; and step 6, carrying out recording and subsequent analysis on emergencies. By arranging multiple sensors at key nodes of the sewage pipe network, real-time online monitoring of multiple parameters such as sewage flow, water quality, pressure and temperature is achieved, the running state of the pipe network can be monitored in an all-weather mode, it is ensured that sudden discharge events are detected in the first time, and the speed and accuracy of emergency response are greatly improved.

Description

Technical Field

[0001] The invention relates to the technical field of environmental protection, and in particular to a sewage network sudden emission analysis method based on online data. Background Art

[0002] As a core component of the urban drainage system, the operation status of the sewage network is directly related to environmental protection and the stability of the urban drainage system. However, the existing sewage network monitoring and emission analysis technology has many shortcomings and cannot effectively respond to sudden emission events, which can easily lead to serious environmental pollution and system failures. Traditional sewage network monitoring systems mostly rely on regular manual inspections and some offline data monitoring.

[0003] In the existing technology, sewage pipe network monitoring mostly adopts regular sampling, and the frequency of manual inspection and sampling is limited, making it difficult to achieve all-weather real-time monitoring of sewage pipe networks. This method often can only obtain data after a sudden discharge event occurs, resulting in delayed emergency response, missing the best disposal time, and failing to control the pollution source in time. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a sewage pipe network sudden discharge analysis method based on online data to solve the problem that the traditional sewage pipe network monitoring system is difficult to achieve all-weather real-time monitoring of the sewage pipe network.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for analyzing sudden discharge of sewage pipe network based on online data, comprising the following steps: Step 1: Place sensors at multiple key nodes of the sewage network to collect sewage flow, water quality parameters and pressure data; Step 2: Preprocess the collected online data to remove noise and outliers and convert the data format; Step 3: Perform sudden emission detection based on historical data and baseline models to identify abnormal emission events; Step 4: Classify the sudden emission events through the event classification and assessment module and determine their urgency based on the event characteristics; Step 5: Generate an alarm and notify relevant personnel, providing real-time incident handling suggestions; Step 6: Record and conduct subsequent analysis of emergencies.

[0006] Preferably, the data preprocessing includes: Use median filtering or Kalman filtering to denoise the collected data and smooth the noise in the sensor data; Use interpolation algorithms to fill in missing parts of the data; Use the timestamp correction algorithm to time-align the sampling data of different sensors and unify the time dimension of the data; The data formats of different sensors are converted into a unified standard through the data format conversion module.

[0007] Preferably, the sudden emission detection includes the following specific operations: A long short-term memory network model trained based on time series data to predict future sewage flow and water quality changes; Use unsupervised learning algorithms, such as Isolation Forest or K-Means, to detect unseen abnormal patterns in the data; By setting safety thresholds for water quality and flow, rule-based anomaly detection is implemented. When water quality or flow parameters are detected to exceed the threshold, they are marked as abnormal events.

[0008] Preferably, the sensors include a water flow sensor, a water quality sensor, a pressure sensor and a temperature sensor.

[0009] Preferably, the event classification and assessment module performs event priority assessment based on the frequency, emission volume and duration of the event.

[0010] Preferably, the alarm and notification module supports a multi-level alarm mechanism and notifies relevant personnel via SMS, email or APP; The multi-level alarm includes: Low-level alarm: When the data slightly exceeds the set threshold range, the system issues a low-level alarm and recommends manual monitoring; Intermediate alarm: When the data exceeds the threshold moderately and lasts for a long time, the system triggers an intermediate alarm and notifies the management personnel to take intervention measures; Emergency alarm: When data seriously exceeds the threshold and causes significant impact on the environment or equipment, the system immediately triggers an emergency alarm and recommends emergency response measures, including closing valves or starting backup water pumps.

[0011] Preferably, a sewage network sudden discharge analysis system based on online data comprises: A data acquisition module, used to collect sewage flow, water quality parameters and pressure data from multiple sensors arranged in the sewage pipe network; Data preprocessing module, used to filter noise, fill missing values ​​and convert data formats for collected data; A burst discharge detection module is used to detect burst discharge events in the sewage network through a baseline model based on historical data; An event classification and assessment module is used to classify the detected sudden emission events and assess the urgency of the events based on the event characteristics; Alarm and notification module, used to generate alarms and notify relevant management personnel; The event recording and subsequent analysis module is used to record sudden emission events in detail and generate reports.

[0012] Preferably, the sudden emission detection module includes the following detection method: Rule-based detection based on historical data, using safety threshold settings to detect abnormal fluctuations in water quality, flow and pressure; Use the LSTM time series prediction model based on deep learning to predict changes in water quality and flow in the future, and detect anomalies by comparing the predicted values ​​with the actual values; Use unsupervised learning models to detect never-before-seen anomalous patterns and sudden emission events.

[0013] Preferably, the data storage module includes: Time series database: used to store high-frequency data generated by sensors; Object storage system: used to store large files and automatically generate historical reports; Distributed file system: used to store large-scale sensor data and event records.

[0014] Preferably, the event classification and assessment module performs emergency event classification and response recommendations based on a historical event database and an expert system.

[0015] The present invention provides a method for analyzing sudden discharge of sewage pipe network based on online data. It has the following beneficial effects: 1. The present invention arranges a variety of sensors at key nodes of the sewage pipe network to achieve real-time online monitoring of multiple parameters such as sewage flow, water quality, pressure, temperature, etc. It can monitor the operation status of the pipe network around the clock to ensure that sudden discharge events are detected at the first time, greatly improving the speed and accuracy of emergency response.

[0016] 2. The present invention integrates multiple types of sensors, which not only monitor water quality parameters, but also collect data such as flow, pressure and temperature in real time, realizing all-round monitoring of the sewage pipe network and providing more comprehensive information on the operation status of the pipe network, so as to more accurately detect discharge anomalies and quickly identify the source of the problem.

[0017] 3. The present invention adopts a time series-based deep learning model and unsupervised learning algorithm, which can adaptively learn and predict the operation data of the sewage network, and has high-precision emergency detection capabilities. The system can dynamically adjust the detection model according to historical data, and can predict abnormal emissions, give early warnings, and avoid serious pollution accidents.

[0018] 4. The present invention designs a multi-level alarm mechanism according to the severity of the emergency, including low-level, medium-level and emergency alarms. The system can intelligently judge the priority of the event based on real-time detection data and provide corresponding processing suggestions. At the same time, relevant management personnel are notified in a timely manner through multiple channels (SMS, email, mobile APP, etc.) to ensure the timely transmission of alarm information, effectively improving the efficiency of event response. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A flow chart of a sewage pipe network sudden discharge analysis method based on online data of the present invention; Figure 2 This is an architecture diagram of a sewage network sudden discharge analysis system based on online data according to the present invention. DETAILED DESCRIPTION

[0020] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings 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 of 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.

[0021] Embodiment 1: Please refer to the attached Figure 1 The embodiment of the present invention provides a method for analyzing sudden discharge of sewage pipe network based on online data, comprising the following steps: Step 1: Data Collection Various types of sensors are installed at multiple key nodes of the sewage pipe network to collect the operation data of the sewage pipe network. The sensors used include: Water flow sensor: used to monitor sewage flow and flow rate.

[0022] Water quality sensor: used to measure the concentration of pollutants in sewage, including key indicators such as pH value, chemical oxygen demand (COD), and ammonia nitrogen content.

[0023] Pressure sensor: monitors the pressure in the pipeline to ensure the normal operation of the pipeline system and prevent leakage or pipe burst due to excessive pressure.

[0024] Temperature Sensors: Used to measure changes in wastewater temperature, which can have a significant effect on certain discharge characteristics.

[0025] The collected data is transmitted to the central data processing system in real time via wireless communication technology (such as NB-IoT or LoRa).

[0026] Step 2: Data preprocessing Since the data collected online may contain noise, outliers, or missing values, data preprocessing is required before analysis. The main steps of preprocessing include: Data cleaning: Use mean filtering and interpolation algorithms to remove noise and outliers in the data to ensure data accuracy.

[0027] Missing data completion: The missing parts in data transmission are completed using linear interpolation method to ensure data continuity and integrity.

[0028] Data format conversion: Unify the data formats collected by different sensors for subsequent analysis and processing.

[0029] Step 3: Sudden emission detection The system uses historical data to establish a baseline model of the sewage network to evaluate normal discharge behavior. Sudden discharge detection is performed in the following two ways: Based on threshold detection: The system performs threshold comparison on real-time monitored water flow, water quality, pressure and other data. Any parameter exceeding the set threshold will be marked as abnormal.

[0030] Anomaly detection based on machine learning: A machine learning model (such as ARIMA or LSTM) trained with historical data is used to make real-time predictions on current data. If the detected result deviates too much from the expected value of the model, it is judged as a sudden emission event.

[0031] Step 4: Event Classification and Evaluation When an unexpected emission event is detected, the system automatically classifies the event and evaluates it based on the historical database and event characteristics (such as emission volume, emission duration, pollutant concentration, etc.). Event classification is based on the following criteria: Abnormal water quality event: If the water quality sensor detects a significant increase in COD or ammonia nitrogen concentration, it is judged as abnormal water quality.

[0032] Abnormal flow event: If the flow sensor detects a surge or decrease in sewage flow, it is determined to be abnormal flow.

[0033] Abnormal pressure event: If the pressure in the pipeline network exceeds the set safety range, it is judged as abnormal pressure.

[0034] Based on the characteristics of the detected event, the system further evaluates its urgency. For example, the greater the degree of exceeding the safety threshold and the longer the duration, the higher the urgency.

[0035] Step 5: Alarm and response The system will automatically generate different levels of alarms for different event types and severity. Alarm information is sent to relevant managers via SMS, email or APP push notification. The alarm mechanism is divided into three levels: Low-level alarm: The threshold is slightly exceeded, prompting manual inspection.

[0036] Medium alarm: The threshold is exceeded to a moderate degree, and management is advised to take action as soon as possible.

[0037] Emergency alarm: The threshold is seriously exceeded, which may cause environmental or equipment accidents and requires immediate response measures.

[0038] The system also provides corresponding emergency response suggestions. For example, if the flow rate surges, the system will recommend closing the relevant valves or starting the backup pump; if the water quality is abnormal, the system may recommend starting temporary treatment equipment.

[0039] Step 6: Event Recording and Subsequent Analysis After each sudden emission event occurs, the system will automatically generate a detailed event report, including the following: Event time: the time when the sudden emission occurred and its duration.

[0040] Detection data: Real-time data changes related to emergencies.

[0041] Processing suggestions and measures: The processing suggestions provided by the system and the actual response measures taken.

[0042] Event reports are saved to the historical event database for future analysis. The system can optimize emergency detection and response strategies based on historical data to improve detection accuracy and response efficiency for future events.

[0043] Underlying logic and technical details Data Acquisition and Sensor Integration Each sensor node in the sewage network consists of a sensor, a data collector, a communication module and a power module. The sensor node is connected to the central data processing system via wired or wireless means, and the sensor's data collection cycle can be adjusted according to demand.

[0044] Sensor type: Water quality sensors measure COD, BOD, suspended solids, ammonia nitrogen, etc.; flow sensors monitor flow fluctuations in real time; pressure sensors are used to detect the pressure conditions inside the pipe network.

[0045] Communication module: For remote sensor nodes, the system uses low-power wide area network (LPWAN) technology, such as NB-IoT. For wired communication, Ethernet or industrial bus (Modbus, RS-485, etc.) can be selected.

[0046] Data preprocessing logic Data preprocessing is a key part of the system and determines the accuracy of subsequent analysis. The processing logic includes: Denoising algorithm: Median filtering and Kalman filtering are used to smooth sensor noise. For example, a pressure sensor may produce instantaneous spikes in a short period of time, and these outliers can be eliminated through filtering.

[0047] Data alignment and aggregation: The sampling frequency of sensors may be inconsistent, so the data needs to be aligned to a unified timestamp and aggregated through window functions. The system uses Apache Flink for real-time streaming and aggregates data into minute, hour, or daily snapshots through sliding windows.

[0048] Anomaly Detection Model Event detection is the core task of the system, which combines rule-based and machine learning-based detection mechanisms.

[0049] Rule detection: Set threshold rules to detect anomalies. For example, when the COD in sewage exceeds a certain preset value, the system marks the event as a possible discharge anomaly.

[0050] Time series prediction model: Use the LSTM model based on deep learning to process the time series data of the sewage network. By training the model, the system can predict future flow and water quality parameters. If the actual data deviates from the predicted value, the system considers it to be abnormal.

[0051] Unsupervised learning: Through Isolation Forest or K-Means clustering algorithms, the system can identify abnormal patterns that have never been seen before. These unsupervised learning algorithms do not rely on predefined rules, so they can automatically discover new types of emergencies.

[0052] Real-time alarm and response The system uses an event-driven mechanism to achieve real-time alarm push through WebSocket or message queues (such as Kafka). Whenever an emergency emission event is detected, the system will automatically generate an alarm and notify the management personnel through SMS, email, application push, etc.

[0053] Multi-level alarm: Alarms are divided into three levels: low, medium and emergency. Low-level alarms recommend monitoring, while emergency alarms trigger the system to automatically perform predefined emergency operations (such as closing valves or starting backup water pumps).

[0054] Historical data storage and deep learning model optimization The system stores historical data using a distributed database architecture (such as Cassandra or HDFS). The stored data can be used for subsequent model training and optimization. Through deep learning frameworks (such as TensorFlow or PyTorch), the system can periodically retrain LSTM or other time series models in the background to optimize the accuracy of emergency detection.

[0055] Embodiment 2: Please refer to the attached Figure 2 , a sewage network sudden discharge analysis system based on online data, including: Data acquisition module: responsible for collecting data from sensors arranged in the sewage pipe network, including water flow sensors, water quality sensors, pressure sensors and temperature sensors. The data is transmitted to the central processing system through the wireless network.

[0056] Data preprocessing module: used to clean, convert formats and complete the collected sewage monitoring data to ensure data quality and continuity.

[0057] Sudden emission detection module: Establish a normal operation model based on historical data, use anomaly detection algorithms (such as threshold detection and machine learning models) to analyze monitoring data in real time and detect sudden emission events.

[0058] Event classification and assessment module: By classifying and assessing emergencies, the severity of the events and their scope of impact are determined, and corresponding handling suggestions are automatically generated.

[0059] Alarm and notification module: Generates alarms based on the urgency of the incident and notifies relevant personnel through a variety of methods, including SMS, email, and mobile APP push.

[0060] Event recording and subsequent analysis module: A report will be generated for each sudden emission event for subsequent optimization of pipeline network management strategies.

[0061] System operation process Data Collection: The system collects data from each sensor and transmits it to the central processor for analysis in real time.

[0062] Data processing: The data preprocessing module filters, completes and formats the collected data to ensure the accuracy of the data during analysis.

[0063] Sudden emission detection: The detection module analyzes the pipeline network data in real time to identify whether there is a sudden emission.

[0064] Alarm response: After detecting an emergency, the system immediately generates an alarm and provides corresponding emergency handling suggestions.

[0065] Subsequent analysis: All events are recorded and stored by the system for future analysis and optimization.

[0066] The system greatly improves the efficiency of detecting sudden discharges from sewage networks and can respond in a timely manner to prevent sewage discharges from causing further impacts on the environment and facilities.

[0067] System architecture logic Sensor network: Distributed and deployed at different nodes of the sewage network to collect real-time data such as sewage flow, pressure, and water quality.

[0068] Data transmission network: A hybrid wireless and wired network is used to send sensor data to a central server. NB-IoT and LoRa are used for low-power devices, while Ethernet and fiber are used for sensor nodes with higher bandwidth.

[0069] Data processing system: Use stream processing frameworks (such as Apache Kafka or Apache Flink) to process data in real time. After preprocessing, the data will be sent to the event detection system.

[0070] Alarm and response system: Detected events are pushed to the alarm system through a message queue system (such as RabbitMQ or Kafka). The alarm system can be integrated with the existing urban sewage management system to achieve automatic control.

[0071] Data analysis platform: Based on distributed storage technology (such as Hadoop, HBase), it supports large-scale data storage and analysis. Historical data can be used to train deep learning models and continuously optimize detection models.

[0072] Data Flow and Processing Data flow design The data flow design of the system includes the path for data collected by sensors to flow to the central server, as well as the data processing path within the central server.

[0073] Data collection: The sensor sends data to the gateway through protocols such as NB-IoT or LoRa, and the gateway uploads the data to the cloud server through the 4G / 5G network.

[0074] Data processing: After data is uploaded, it first enters the message queue. The system uses Apache Flink's real-time processing engine to distribute the data to multiple computing nodes for real-time processing. The processed data is stored in a NoSQL database (such as Cassandra) or passed to the emergency detection system.

[0075] Real-time event detection Model parallelization: To improve detection speed, the system supports multiple models running in parallel. Anomaly detection and rule detection algorithms based on machine learning can simultaneously analyze flow, pressure, water quality and other data.

[0076] Detection algorithm optimization: Real-time event detection relies on the optimization of the underlying model. Through continuous training and optimization, the model can be automatically updated to adapt to changes in the sewage network.

[0077] Data storage and historical analysis Data storage uses a distributed storage system to store all historical data for analysis and model training. The storage architecture includes: Time Series Database (TSDB): used to store time series data, suitable for storing high-frequency data collected by sensors.

[0078] Object storage: Stores large files and historical reports.

[0079] Analysis Engine: The analysis engine is responsible for extracting useful information from the stored historical data for model optimization, system performance improvement, and prediction of future events. The analysis engine is usually composed of the following sub-modules: Data cleaning module: Before analyzing historical data, the analysis engine will perform a secondary cleaning of the data, including removing redundant information, processing outliers, and standardizing data to ensure that the data quality meets the analysis requirements.

[0080] Statistical analysis module: Through statistical analysis of historical data, the system can summarize the operation rules of the pipe network, such as peak discharge time, seasonal changes in sewage flow, etc. This information can help operation managers make decisions.

[0081] Machine Learning Training Module: Based on the stored historical data, the machine learning training module continuously optimizes the emergency detection model. This module can use time series data to train LSTM models, classification-based anomaly detection models (such as decision trees, support vector machines SVM), and unsupervised learning models (such as K-Means clustering and Isolation Forest).

[0082] Prediction module: By reviewing historical data and combining it with prediction algorithms, the system can infer possible abnormal situations or operation trends of the pipeline network in the future. This prediction module can adjust monitoring strategies or provide early warnings to operators based on long-term trends.

[0083] Distributed database architecture In order to store massive amounts of data and ensure system scalability, the system uses a distributed database architecture. This architecture is based on the following database and storage technologies: Time Series Database (TSDB): Time series databases are well suited for high-frequency data storage, especially data generated by sensors. Common TSDB technologies include InfluxDB and OpenTSDB, which support efficient time series queries and aggregations.

[0084] NoSQL databases: For example, MongoDB, Cassandra, and other NoSQL databases are used to store unstructured or semi-structured data, such as sensor configuration files, historical event records, and system logs.

[0085] Object storage: Object storage systems (such as Amazon S3 and Ceph) are used to store large files and historical reports, such as automatically generated reports after each sudden emission event, or intermediate data generated during model training.

[0086] Performance optimization of data storage In order to improve the performance of data query and analysis, the system optimizes the storage architecture in many aspects: Partitioned storage: Historical data is partitioned and stored based on time and location, which reduces the system I / O load when querying data for a specific time period or a specific area. The combination of time partitioning and geographic partitioning makes query and analysis more efficient.

[0087] Data compression: For historical data that is not frequently accessed for a long time, the system will compress the data to reduce storage space usage. Common compression technologies include Zstandard and Snappy, which can significantly reduce the amount of data while ensuring a faster decompression speed.

[0088] Cache and index optimization: To speed up queries, the system caches commonly used query results and creates indexes for key fields (such as timestamps and sensor IDs), which significantly improves data retrieval speed.

[0089] Historical analysis and optimization The historical data stored in the system is not only used to review events, but also to optimize models and system performance. The specific analysis and optimization process is as follows: Emergency review analysis: The relevant data of each emergency will be saved in the database and compared with the model prediction results. By reviewing the occurrence of each event, the system can find the source of model errors and adjust the model parameters based on these errors.

[0090] Adaptive model training: Based on the data of historical events, the machine learning model can be adaptively trained. The system automatically selects appropriate training data and optimizes the model based on this data, so that the detection accuracy is gradually improved in actual applications.

[0091] Decision optimization: By analyzing past emergency response records, the system can optimize the response strategy of the decision-making module. For example, if a certain type of emergency occurs multiple times, the system can provide early warning and more detailed emergency response suggestions.

[0092] Embodiment three: Intelligent prediction and optimization based on data analysis Based on the historical data stored in the system and the historical analysis of emergencies, the system can not only review past data, but also use machine learning models to make intelligent predictions and proactively optimize pipeline network operations and management strategies.

[0093] Intelligent prediction model By analyzing a large amount of historical data, the system can identify the operating rules of the sewage network and predict possible abnormal situations in the future based on these rules. The intelligent prediction model includes: Time series prediction: By training historical sensor data with deep learning models such as LSTM, the system can predict parameters such as sewage flow and water quality in the future. These predicted values ​​can help managers take preventive measures before an incident occurs.

[0094] Pattern recognition: Through clustering algorithms (such as K-Means or DBSCAN), the system can identify specific patterns in the operation of the sewage network, which may be associated with certain specific events. For example, abnormal fluctuations in flow within a specific time period may indicate a certain type of pipe blockage or leakage.

[0095] Dynamically adjust monitoring strategies Based on the prediction results, the system can dynamically adjust the monitoring strategy of the sewage network: Sensor Scheduling: Based on the prediction results, the system can dynamically adjust the sampling frequency of the sensor or adjust the working mode of the sensor. For example, when the prediction results of a certain area show that there may be sudden emission events in the future, the system can increase the sampling frequency of the sensors in that area to obtain more data.

[0096] Dynamic adjustment of thresholds: The system can dynamically adjust monitoring thresholds based on historical event data. For example, if sewage flow in a certain area usually increases during a certain season, the system will automatically raise the threshold to avoid unnecessary low-level alarms.

[0097] Automatically generate optimization reports The system will automatically generate an optimization report based on historical data at regular intervals, including: Pipeline network operation status: Review the pipeline network operation data over the past period of time to identify potential risk areas.

[0098] Event handling suggestions: Based on historical emergency response records, the system provides more optimized emergency handling suggestions to help operators respond faster and more effectively in future events.

[0099] Model optimization report: The system will generate a model optimization report based on the model training status. The report details the model improvements, the improvement of detection accuracy, and future optimization directions.

[0100] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for analyzing sudden discharge of sewage pipe network based on online data, characterized in that: The following steps are involved: Step 1: Place sensors at multiple key nodes of the sewage network to collect sewage flow, water quality parameters and pressure data; Step 2: Preprocess the collected online data to remove noise and outliers and convert the data format; Step 3: Perform sudden emission detection based on historical data and baseline models to identify abnormal emission events; Step 4: Classify the sudden emission events through the event classification and assessment module and determine their urgency based on the event characteristics; Step 5: Generate an alarm and notify relevant personnel, providing real-time incident handling suggestions; Step 6: Record and conduct subsequent analysis of emergencies.

2. According to the method for analyzing sudden discharge of sewage pipe network based on online data in claim 1, it is characterized in that: The data preprocessing includes: Use median filtering or Kalman filtering to denoise the collected data and smooth the noise in the sensor data; Use interpolation algorithms to fill in missing parts of the data; Use the timestamp correction algorithm to time-align the sampling data of different sensors and unify the time dimension of the data; The data formats of different sensors are converted into a unified standard through the data format conversion module.

3. According to the method for analyzing sudden discharge of sewage pipe network based on online data in claim 1, it is characterized in that: The sudden emission detection includes the following specific operations: A long short-term memory network model trained based on time series data to predict future sewage flow and water quality changes; Use unsupervised learning algorithms, such as Isolation Forest or K-Means, to detect unseen abnormal patterns in the data; By setting safety thresholds for water quality and flow, rule-based anomaly detection is implemented. When water quality or flow parameters are detected to exceed the threshold, they are marked as abnormal events.

4. According to the method for analyzing sudden discharge of sewage pipe network based on online data in claim 1, it is characterized in that: The sensors include a water flow sensor, a water quality sensor, a pressure sensor and a temperature sensor.

5. The method for analyzing sudden discharge of sewage pipe network based on online data according to claim 1 is characterized in that: The event classification and assessment module performs event priority assessment based on the frequency, emission and duration of the event.

6. The method for analyzing sudden discharge of sewage pipe network based on online data according to claim 1 is characterized in that: The alarm and notification module supports a multi-level alarm mechanism and notifies relevant personnel via SMS, email or APP; The multi-level alarm includes: Low-level alarm: When the data slightly exceeds the set threshold range, the system issues a low-level alarm and recommends manual monitoring; Intermediate alarm: When the data exceeds the threshold moderately and lasts for a long time, the system triggers an intermediate alarm and notifies the management personnel to take intervention measures; Emergency alarm: When data seriously exceeds the threshold and causes significant impact on the environment or equipment, the system immediately triggers an emergency alarm and recommends emergency response measures, including closing valves or starting backup water pumps.

7. A sewage network sudden discharge analysis system based on online data, characterized in that: A method for analyzing sudden discharges from a sewage network based on online data as described in any one of claims 1 to 6, comprising: A data acquisition module, used to collect sewage flow, water quality parameters and pressure data from multiple sensors arranged in the sewage pipe network; Data preprocessing module, used to filter noise, fill missing values ​​and convert data formats for collected data; A burst discharge detection module is used to detect burst discharge events in the sewage network through a baseline model based on historical data; An event classification and assessment module is used to classify the detected sudden emission events and assess the urgency of the events based on the event characteristics; Alarm and notification module, used to generate alarms and notify relevant management personnel; The event recording and subsequent analysis module is used to record sudden emission events in detail and generate reports.

8. The sewage network sudden discharge analysis system based on online data according to claim 7 is characterized in that: The sudden emission detection module includes the following detection methods: Rule-based detection based on historical data, using safety threshold settings to detect abnormal fluctuations in water quality, flow and pressure; Use the LSTM time series prediction model based on deep learning to predict changes in water quality and flow in the future, and detect anomalies by comparing the predicted values ​​with the actual values; Use unsupervised learning models to detect never-before-seen anomalous patterns and sudden emission events.

9. The sewage pipe network sudden discharge analysis system based on online data according to claim 7 is characterized in that: The data storage module comprises: Time series database: used to store high-frequency data generated by sensors; Object storage system: used to store large files and automatically generate historical reports; Distributed file system: used to store large-scale sensor data and event records.

10. The sewage network sudden discharge analysis system based on online data according to claim 7 is characterized in that: The event classification and evaluation module classifies emergencies and provides response recommendations based on a historical event database and an expert system.

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