Drainage pipe network hydraulic water quality monitoring equipment and control system

Abstract

The invention discloses a hydraulic water quality monitoring control system for a drainage pipe network, which belongs to the field of data communication of a data center and comprises a rain gauge, a flow meter, a liquid level meter and a water quality monitor. The system also comprises an intelligent data analysis and diagnosis module, a remote control and automatic management module, an environment adaptability and safety protection design, a communication and data transmission optimization module, and an intelligent fault self-diagnosis and maintenance prompt module. A data analysis algorithm is adopted, data are transmitted to a central monitoring platform in real time through a wireless communication module, and centralized processing and analysis of the data are achieved. Meanwhile, the system has a remote control function and supports remote operation of drainage pipe network equipment through the intelligent tail end power supply controller. The environment adaptability design ensures that the equipment can stably operate in a severe environment, and the communication and data transmission optimization ensures the real-time performance and accuracy of data.

Description

Technical Field

[0001] The invention belongs to the technical field of intelligent monitoring and fault diagnosis, and in particular relates to a drainage network hydraulic water quality monitoring and control system. Background Art

[0002] The field of intelligent fault self-diagnosis and maintenance prompt technology involves the use of advanced data analysis algorithms and artificial intelligence technology to analyze monitoring data in real time to identify abnormal patterns and potential problems in the drainage system. The core of this technology is to improve the reliability and maintenance efficiency of drainage network monitoring equipment through automation and intelligent means.

[0003] Among existing technologies, predictive maintenance (PdM) collects equipment operation data and uses data analysis and modeling techniques to identify potential equipment failures or performance degradation trends, thereby performing maintenance operations in advance to avoid production interruptions or safety accidents caused by equipment failures. This method usually focuses on a specific part or system of the equipment and performs targeted monitoring and diagnosis on it, focusing on equipment failure prediction and preventive maintenance, and achieving optimal operation and maintenance costs.

[0004] Intelligent fault diagnosis technology combines the essence of artificial intelligence and fault diagnosis. By using artificial intelligence technology and methods, it simulates the knowledge and experience of experts in the field to achieve intelligent monitoring of equipment health status and fault prediction. This method can integrate equipment operation data, historical fault records, expert experience and other information, use a variety of diagnostic methods, and form a flexible diagnostic strategy.

[0005] In terms of intelligent operation and maintenance, machine learning algorithms are integrated to predict and automatically repair system failures, significantly improving the efficiency and accuracy of operation and maintenance. The intelligent operation and maintenance platform can analyze the operating status of the system in real time, predict potential failure points, and even automatically perform repair operations before problems occur.

[0006] However, the difficulty in implementing predictive maintenance technology is not only due to the complexity and difficulty of the technology itself, but also due to the lack of a systematic implementation methodology in enterprises. Technical complexity, unclear implementation goals and investment return cycles, and data quality issues are all reasons why predictive maintenance is difficult to implement.

[0007] With the continuous development of technology, especially the application of artificial intelligence technology in the industrial field, the field of intelligent fault self-diagnosis and maintenance prompt technology is developing in a more accurate, efficient and real-time direction, providing new possibilities and challenges for the intelligent management of drainage network monitoring equipment. Summary of the invention

[0008] The technical problem to be solved by the present invention is to provide a drainage network hydraulic and water quality monitoring and control system in response to the shortcomings of the background technology. By integrating multiple sensors and intelligent algorithms, real-time monitoring, data analysis, fault diagnosis and maintenance prompts of key parameters in the drainage network can be realized to improve the safety and efficiency of the drainage system operation.

[0009] The present invention adopts the following technical solutions to solve the above technical problems:

[0010] A drainage network hydraulic water quality monitoring and control system, comprising an integrated monitoring module, an intelligent data analysis and diagnosis module, a remote control and automation management module, an environmental adaptability and safety protection module, a communication and data transmission optimization module, and an intelligent fault self-diagnosis and maintenance prompt module;

[0011] Among them, the integrated monitoring module is used to monitor the water quantity, water quality, liquid level and other parameters in the drainage network in real time through a variety of monitoring equipment, including rain gauge, flow meter, liquid level meter, electronic water gauge, buried liquid level meter, COD online water quality monitor, ammonia nitrogen online water quality monitor, suspended solids online water quality monitor;

[0012] Intelligent data analysis and diagnosis module, which is used to collect and process monitoring data in real time, and use statistics, pattern recognition, and machine learning methods to analyze monitoring data in real time, discover patterns and trends from large amounts of data, and then identify abnormal values, trend changes, and periodic patterns in the data, thereby providing decision support for drainage system management;

[0013] Remote control and automation management module, used to achieve remote control and automation management of drainage network equipment through intelligent terminal power supply controller and remote control system;

[0014] Environmental adaptability and safety protection module, used to meet IP68 waterproof level and intrinsic safety explosion-proof level requirements, integrated leakage detection, overload protection, lightning protection and signal isolation;

[0015] Communication and data transmission optimization module, which is used to optimize data transmission using 4G communication and OPC interface, ensuring that data is transmitted to the monitoring system in real time and accurately;

[0016] Intelligent fault self-diagnosis and maintenance reminder module is used for automatic fault diagnosis and maintenance reminder, thus reducing maintenance costs and extending equipment life.

[0017] As a further preferred solution of the drainage network hydraulic water quality monitoring and control system of the present invention, the rain gauge adopts a tipping bucket design, rainwater enters the water receiver through the rain receiving port and flows into the tipping bucket; when the amount of accumulated water reaches a predetermined value, the tipping bucket flips, and an electronic pulse signal is sent through the reed switch to achieve accurate measurement;

[0018] The flowmeter includes an ultrasonic Doppler flowmeter and an electromagnetic flowmeter; the ultrasonic Doppler flowmeter adopts a dual-frequency

[0019] Doppler technology DFD, by generating two sets of ultrasonic signals with different frequencies, effectively removes noise signals and square-amplifies the accurately identified Doppler signals to improve signal recognition and tracking capabilities;

[0020] The electromagnetic flowmeter includes a capacitance electromagnetic flowmeter and a multi-electrode non-full-tube electromagnetic flowmeter;

[0021] The capacitive electromagnetic flowmeter adopts the capacitive measurement principle. The electrode and the measured fluid form a detection capacitor through an insulating tube, and the flow signal is coupled through the change of the capacitance.

[0022] Multi-electrode non-full pipe electromagnetic flowmeter: Under non-full pipe conditions, multiple pairs of electrodes are used to measure fluid flow rate and liquid level height, and the measured values ​​are corrected through algorithms to achieve accurate measurement of non-full pipe flow;

[0023] The liquid level meter includes an ultrasonic liquid level meter, a radar liquid level meter, and a capacitive liquid level meter;

[0024] Among them, the ultrasonic level meter measures the liquid level by emitting ultrasonic pulses and receiving echoes reflected by the liquid surface; the ultrasonic waves emitted by the sensor are reflected when they encounter the liquid surface, and the sensor receives the reflected waves and calculates the time difference between emission and reception to determine the liquid level height;

[0025] Radar level meter: uses radio waves, i.e. radar waves, to measure liquid level; the radar waves emitted by the sensor are reflected on the liquid surface, the reflected waves are received and the time difference is calculated to determine the liquid level;

[0026] Capacitive level gauge: determines the liquid level by measuring the change in capacitance between the liquid and the container wall; changes in the liquid level cause changes in the capacitance value, thus reflecting the height of the liquid level;

[0027] Electronic water gauge: Utilizes the weak conductivity of water to collect water depth information through a string of electrodes arranged at equal intervals; the electrodes present different potentials in water with different conductivity, thereby determining whether the electrodes are submerged in water and then calculating the water level;

[0028] The water level value W is calculated by the formula W = ∑(E i -E i-1 )×S calculation, where E i is the potential of the ith electrode, S is the electrode spacing;

[0029] Underground liquid level meter: Use high-precision pressure sensors to measure the pressure of the liquid on the bottom of the container to determine the liquid level. When the liquid level changes, the pressure of the liquid on the bottom will also change. By measuring this pressure change, the liquid level information can be obtained.

[0030] The liquid level h is determined by the formula: Calculate, where P is the total pressure of the liquid at the liquid level, P0 is the reference pressure, i.e., atmospheric pressure, ρ is the density of the liquid, and g is the acceleration due to gravity;

[0031] COD online water quality monitor: It uses ultraviolet catalytic oxidation technology to accelerate the decomposition of organic matter, improve digestion efficiency and COD measurement accuracy; COD value is usually calculated based on oxidant consumption, and is obtained by comparing the absorbance of the digested solution with the standard curve. The calculation formula is COD = A × S, where A is the absorbance of the solution and S is the slope of the standard curve;

[0032] Ammonia nitrogen online water quality monitor: uses the ion selective electrode method to determine the concentration by measuring the potential change of ammonia nitrogen ions in the water sample on a specific electrode; in an environment where the pH value is greater than 11, ammonium ions transform into ammonia, and ammonia transfers through the hydrophobic membrane of the ammonia sensitive electrode, causing the electromotive force of the ammonia sensitive electrode to change. The instrument measures the concentration of ammonia nitrogen based on the change in electromotive force; the ammonia nitrogen concentration N can be calculated from the relationship between the electrode potential E and the standard curve, using a linear or nonlinear regression model;

[0033] SS suspended solids online water quality monitor: It uses the scattered light measurement principle to determine the concentration of suspended solids by measuring the scattering intensity of suspended solid particles in water to light of a specific wavelength; the suspended solids concentration C can be calculated through the relationship between the scattered light intensity I and the known calibration curve, that is, C = f(I), where f is a calibration function established based on experimental data.

[0034] As a further preferred solution of the drainage network hydraulic water quality monitoring and control system of the present invention, the principle of the intelligent data analysis and diagnosis module specifically includes the following steps:

[0035] Use big data analysis technologies, including Hadoop and Spark distributed computing frameworks, to process and analyze large-scale data sets;

[0036] Use data mining techniques, including clustering, classification, and regression analysis, to extract valuable information from data;

[0037] Apply machine learning algorithms, including decision trees, random forests, neural networks, etc., to build prediction models to predict and diagnose system status;

[0038] The specific calculation is as follows: Calculate the mean of a data set to reflect the central tendency of the data;

[0039] in, is the sample mean, i.e. the average value of the data set; n is the sample size, i.e. the number of observations in the data set; xi is the i-th observation;

[0040] By formula Measures the degree of dispersion of data; where σ 2 is the sample variance, which indicates the degree of dispersion of the data set, that is, the average of the squares of the differences between each data and its mean;

[0041] The linear regression model y = β0 + β1x + ∈ predicts and evaluates the relationship between variables; y is the dependent variable, that is, the response variable, which is the variable that the model attempts to predict or explain; β0 is the intercept term, which represents the expected value of the dependent variable y when the independent variable x is zero; β1 is the slope, which represents how the dependent variable y is expected to change for every unit change in the independent variable x; x is the independent variable, that is, the explanatory variable, which is the predictor variable in the model; ∈ is the error term, which represents the random variation that the model cannot explain;

[0042] Cluster analysis: K-means clustering optimization objective formula: It is used to divide data points into multiple clusters; J is the sum of squares of intra-cluster differences, which is used to evaluate the optimization target of clustering effect, that is, the sum of squares of distances between all data points and their corresponding cluster centers; k is the number of clusters, that is, the number of clusters to which the data is to be divided; n i is the number of observations in the i-th cluster; x ij is the jth observation in the i-th cluster; μ i is the cluster center of the ith cluster.

[0043] As a further preferred solution of the drainage network hydraulic water quality monitoring and control system of the present invention, the remote control and automation management module includes an intelligent terminal power supply controller, a remote control system and an automation management strategy;

[0044] Among them, the intelligent terminal power supply controller: uses network technology to allow users to remotely power on, off and query electrical equipment through the intelligent terminal power supply controller; at the same time, the controller can monitor parameters such as load current, leakage current, voltage and temperature in real time, and issue an alarm through the management platform when the data is abnormal;

[0045] Remote control system: used to control remote devices through TCP / IP protocol network data communication; based on different communication protocols, including UDP or TCP, to ensure the accuracy and continuity of data transmission; UDP protocol is suitable for scenarios with high real-time requirements but relatively low reliability requirements, while TCP protocol provides more stable connection and data transmission;

[0046] Automation management strategy: used to automatically perform specific tasks through preset rules and conditions, including timing switching equipment and adjusting power distribution according to load conditions;

[0047] As a further preferred solution of the drainage network hydraulic water quality monitoring and control system of the present invention, the principle of the environmental adaptability and safety protection module specifically includes:

[0048] IP68 waterproof rating: IP68 is the highest rating in the international protection level standard, indicating that the device is completely dustproof and can be immersed in water for a long time without damage; the use of sealing technology, including rubber gaskets, waterproof coatings and sealed cabins, ensures that all circuits and interfaces are fully protected;

[0049] Intrinsic safety explosion-proof level: ensures that the equipment will not cause an explosion when used in an inflammable and explosive environment by limiting the electric sparks or heat energy that may be generated on the surface and inside of the equipment; using special circuit design and materials to ensure that under specified conditions, the electric sparks or heat energy generated by the equipment are not enough to ignite the surrounding flammable gas or dust;

[0050] Leakage detection and overload protection: They work by monitoring the current between the device housing and the ground. Once an abnormal current is detected, the system automatically cuts off the power supply to prevent electric shock accidents. Overload protection prevents device damage by monitoring whether the current exceeds the device rating. High-precision current sensors and control circuits are integrated to monitor current changes in real time and compare them with preset safety thresholds.

[0051] Lightning protection: Reduce the impact of high voltage and current generated by lightning on equipment by installing lightning arresters and surge protectors; install surge protectors in the power lines and signal lines of equipment, using the nonlinear resistance material inside to conduct electricity under high voltage and direct excess current to the ground.

[0052] As a further preferred solution of the drainage network hydraulic water quality monitoring and control system of the present invention, the principle of the communication and data transmission optimization module specifically includes

[0053] 4G communication: Utilize the high-speed data transmission capability of 4G network to realize remote wireless transmission of monitoring data; 4G network provides higher data transmission rate and wider frequency band, which is suitable for real-time data transmission; integrate 4G module into monitoring equipment, and transmit data through the network infrastructure of the operator; data is transmitted in the mobile network through wireless signals, and forwarded to the monitoring system through base stations and network equipment;

[0054] OPC interface: OPC is an industrial standard communication protocol used to achieve data exchange between different devices and systems; OPC interface allows seamless data transmission between monitoring equipment and monitoring system; OPC server function is implemented in monitoring equipment to provide real-time data access interface; monitoring system acts as OPC client and reads device data through OPC protocol;

[0055] Data transmission optimization algorithm: Use advanced data compression and transmission control algorithms to reduce the size and number of data packets, improve transmission efficiency, and reduce transmission delays; perform compression processing before data transmission to reduce the amount of data; at the same time, dynamically adjust the transmission rate and retransmission strategy according to network conditions to optimize the transmission path;

[0056] Among them, the data transmission efficiency can be expressed by the formula To evaluate, where E is efficiency, D is data volume, and T is transmission time;

[0057] Network quality monitoring and optimization: Real-time monitoring of network quality, including signal strength, bandwidth utilization, delay and packet loss rate indicators, and dynamic adjustment of data transmission strategies; use network monitoring tools and algorithms to analyze network status, and adjust data transmission parameters based on analysis results, including the number of retransmissions, transmission rate and packet size; optimize network parameters to ensure that data transmission delay and packet loss rate meet control indicator requirements.

[0058] As a further preferred solution of the drainage network hydraulic water quality monitoring and control system of the present invention, the intelligent fault self-diagnosis and maintenance prompt module specifically includes:

[0059] Fault prediction model based on machine learning: Use machine learning algorithms, including neural networks and support vector machines, to analyze the historical operation data and maintenance records of the equipment and establish a fault prediction model. By collecting the key performance indicators (KPIs) of the equipment, using data mining technology to extract features and train the fault prediction model; the model can identify fault modes and trends and predict potential faults.

[0060] Real-time health monitoring and adaptive maintenance strategy: monitor its operating status in real time, including temperature, vibration, and pressure parameters, and adjust the maintenance plan through adaptive algorithms; use sensors to collect real-time data, process it through embedded systems, and update the health status of the equipment in real time; adaptive algorithms dynamically adjust maintenance strategies based on the current status of the equipment and historical maintenance records; the service life RUL can be calculated through predictive models, such as using inverse Gaussian processes or other survival models to estimate;

[0061] Intelligent maintenance reminders and remote support: When the system detects a potential fault or maintenance requirement, it automatically generates a maintenance reminder and can be sent to maintenance personnel via remote communication; integrated communication module, including 4G or Wi-Fi, for data transmission and remote communication; the maintenance reminder system generates maintenance suggestions based on the output of the fault prediction model, and can notify maintenance personnel via mobile applications or text messages; the maintenance response time can be estimated by the formula T = D / S, where D is the urgency of the maintenance requirement and S is the availability of maintenance resources.

[0062] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects:

[0063] 1. The present invention provides a drainage network hydraulic water quality monitoring and control system, which is composed of a variety of monitoring equipment, including a rain gauge, a flow meter, a liquid level meter and a water quality monitor, as well as an intelligent data analysis and diagnosis module, a remote control and automation management module, an environmental adaptability and safety protection design, a communication and data transmission optimization module and an intelligent fault self-diagnosis and maintenance prompt module;

[0064] 2. The present invention adopts advanced data analysis algorithms to transmit data to the central monitoring platform in real time through wireless communication modules, realizing centralized data processing and analysis. At the same time, the system has remote control functions and supports remote operation of drainage network equipment through intelligent terminal power supply controllers. Environmental adaptability design ensures that the equipment can operate stably in harsh environments, while communication and data transmission optimization ensure the real-time and accuracy of data.

[0065] 3. The present invention also has intelligent fault self-diagnosis and maintenance prompt functions, which can automatically detect and diagnose potential equipment failures and provide maintenance suggestions in a timely manner, thereby reducing maintenance costs and extending equipment life; the system design takes energy saving and environmental protection into consideration, and adopts a modular design, which is easy to deploy and expand quickly to meet the monitoring needs of drainage pipe networks of different scales;

[0066] 4. The drainage network hydraulic and water quality monitoring equipment and control system of the present invention significantly improves the monitoring efficiency and data accuracy of the drainage network through its innovative monitoring technology and intelligent management, and provides strong technical support for water resources management and environmental protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] 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.

[0068] Figure 1It is the overall architecture diagram of the drainage network hydraulic water quality monitoring equipment and control system of the present invention;

[0069] Figure 2 It is a functional module diagram of the integrated monitoring system of the present invention;

[0070] Figure 3 It is a flow chart of intelligent data analysis and diagnosis of the present invention;

[0071] Figure 4 It is a control logic diagram of remote control and automatic management of the present invention;

[0072] Figure 5 It is a schematic diagram of the structure of the environmental adaptability and safety protection design of the present invention;

[0073] Figure 6 It is a network topology diagram for communication and data transmission optimization of the present invention;

[0074] Figure 7 It is a flow chart of the intelligent fault self-diagnosis and maintenance prompt of the present invention. DETAILED DESCRIPTION

[0075] The technical solution of the present invention is further described in detail below in conjunction with the accompanying drawings:

[0076] 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.

[0077] The present invention provides a drainage network hydraulic water quality monitoring and control system, which aims to provide an integrated and intelligent monitoring solution to achieve real-time and accurate monitoring and management of water quantity, water quality, liquid level and other parameters in the drainage network. The system is composed of a variety of monitoring equipment, including rain gauges, flow meters, liquid level meters and water quality monitors, as well as intelligent data analysis and diagnosis modules, remote control and automation management modules, environmental adaptability and safety protection design, communication and data transmission optimization modules and intelligent fault self-diagnosis and maintenance prompt modules.

[0078] The system uses advanced data analysis algorithms to transmit data to the central monitoring platform in real time through wireless communication modules, realizing centralized data processing and analysis. At the same time, the system has remote control functions and supports remote operation of drainage network equipment through intelligent terminal power supply controllers. Environmental adaptability design ensures that the equipment can operate stably in harsh environments, while communication and data transmission optimization ensure the real-time and accuracy of data.

[0079] The system also has intelligent fault self-diagnosis and maintenance reminder functions, which can automatically detect and diagnose potential equipment failures and provide timely maintenance suggestions, thereby reducing maintenance costs and extending equipment life. The system is designed with energy saving and environmental protection in mind, and adopts a modular design that facilitates rapid deployment and expansion to meet the monitoring needs of drainage networks of different sizes.

[0080] The drainage network hydraulic water quality monitoring equipment and control system of the present invention significantly improves the monitoring efficiency and data accuracy of the drainage network through its innovative monitoring technology and intelligent management, and provides strong technical support for water resources management and environmental protection.

[0081] The present invention relates to intelligent monitoring and fault diagnosis technology, and in particular to hydraulic and water quality monitoring equipment and control systems for drainage networks. The system integrates multiple sensors and intelligent algorithms to achieve real-time monitoring, data analysis, fault diagnosis and maintenance prompts of key parameters in the drainage network, so as to improve the safety and efficiency of drainage system operation.

[0082] A drainage network hydraulic water quality monitoring and control system, such as Figure 1 As shown, it includes an integrated monitoring module, an intelligent data analysis and diagnosis module, a remote control and automation management module, an environmental adaptability and safety protection module, a communication and data transmission optimization module, and an intelligent fault self-diagnosis and maintenance prompt module;

[0083] Among them, the integrated monitoring module is used to monitor the water quantity, water quality, liquid level and other parameters in the drainage network in real time through a variety of monitoring equipment, including rain gauge, flow meter, liquid level meter, electronic water gauge, buried liquid level meter, COD online water quality monitor, ammonia nitrogen online water quality monitor, suspended solids online water quality monitor;

[0084] Intelligent data analysis and diagnosis module, which is used to collect and process monitoring data in real time, and use statistics, pattern recognition, and machine learning methods to analyze monitoring data in real time, discover patterns and trends from large amounts of data, and then identify abnormal values, trend changes, and periodic patterns in the data, thereby providing decision support for drainage system management;

[0085] Remote control and automation management module, used to achieve remote control and automation management of drainage network equipment through intelligent terminal power supply controller and remote control system;

[0086] Environmental adaptability and safety protection module, used to meet IP68 waterproof level and intrinsic safety explosion-proof level requirements, integrated leakage detection, overload protection, lightning protection and signal isolation;

[0087] Communication and data transmission optimization module, which is used to optimize data transmission using 4G communication and OPC interface, ensuring that data is transmitted to the monitoring system in real time and accurately;

[0088] Intelligent fault self-diagnosis and maintenance reminder module is used for automatic fault diagnosis and maintenance reminder, thus reducing maintenance costs and extending equipment life.

[0089] like Figure 2 As shown, the functional module diagram of the integrated monitoring system.

[0090] Sensor units: including rain gauges, flow meters, level gauges and water quality monitors, responsible for collecting hydraulic and water quality data.

[0091] Data acquisition unit: includes analog-to-digital converter and data buffer, responsible for converting analog signals collected by sensors into digital signals and caching them.

[0092] Signal processing unit: includes signal amplification, filtering and analog-to-digital conversion, responsible for processing the collected digital signals.

[0093] Communication unit: includes wireless communication module and wired communication interface, responsible for transmitting processed signals to the control unit.

[0094] Control unit: includes automation control logic and remote control instructions, and is responsible for executing control strategies based on data transmitted by the communication unit.

[0095] User interface: includes real-time data display, historical data query, alarm setting and notification, and provides a user interaction interface.

[0096] like Figure 3 Shown: Intelligent data analysis and diagnosis flow chart;

[0097] Start: Start the data analysis and diagnosis process. Data collection: Collect raw data from monitoring equipment. Data preprocessing: Clean and preprocess the collected data, such as denoising, filling missing values, etc. Feature extraction: Extract useful features from the preprocessed data. Data standardization: Standardize the feature data to eliminate the dimensionality effect. Select analysis model: Select a suitable analysis model based on the data characteristics. Train the diagnosis model: Use historical data to train the diagnosis model. Model validation: Verify the accuracy and generalization ability of the model through methods such as cross-validation. Model validity: Determine whether the model meets the expected validity standards. Apply the model for fault diagnosis: Use the trained model to perform fault diagnosis on real-time data. Generate fault diagnosis report: Generate a detailed fault diagnosis report based on the model output. Report review: Have professionals review the diagnosis report. Review passed: Determine whether the report has passed the review. Publish the diagnosis report: Publish the reviewed report to relevant personnel. Generate maintenance reminder: Generate maintenance reminders based on the diagnosis results. Remote maintenance notification: Send maintenance notifications to maintenance personnel through the remote communication system. End: Complete the data analysis and diagnosis process.

[0098] like Figure 4 Shown: Control logic diagram of remote control and automation management.

[0099] Start: Start the remote control and automation management process. User request: The user initiates a remote control or automation management request through the interface. Request type: Determine the type of user request. Remote control unit: Process remote control requests. Automation management unit: Process automation management requests. Data query unit: Process data query requests. Send control command: Send control command to the device. Execute automation script: Execute the preset automation management script. Retrieve historical data: Retrieve historical data from the database. Device response: The response of the device after receiving the control command. System status update: Update the system status according to the results of the automation script execution. Display query results: Display the queried data to the user. Success: Check whether the operation is successfully executed. Record operation log: Record all successful operation logs. Error handling: Handle any errors in execution. Send operation feedback: Send feedback on the operation results to the user. End: Complete the remote control and automation management process.

[0100] like Figure 5 Shown: Structural diagram of environmental adaptability and safety protection design.

[0101] Waterproof and dustproof design: ensures that the device can work properly in a humid or dusty environment. Explosion-proof design: prevents the device from causing an explosion in an inflammable and explosive environment. Lightning protection design: protects the device from damage caused by lightning surges. Signal isolation: isolates sensitive circuits to improve the system's anti-interference ability. Overload protection: prevents the device from being damaged by excessive current. Leakage protection: ensures personnel safety and prevents electric shock accidents.

[0102] like Figure 6 Shown: Network topology diagram for communication and data transmission optimization.

[0103] Monitoring equipment: the source of analog or digital signals, such as sensors. Data acquisition unit: responsible for converting analog signals generated by monitoring equipment into digital signals. Signal processing unit: further processes digital signals, such as amplification, filtering and analog-to-digital conversion. Communication unit: responsible for transmitting the processed signals wirelessly or wired. Wireless transmission: data transmission through wireless methods such as 4G / 5G networks. Wired transmission: data transmission through wired methods such as Ethernet or optical fiber. Central monitoring platform: receives data from the communication unit and further processes and stores it. Database: stores data from the central monitoring platform. Analysis server: analyzes and processes the data in the database. User interface: provides an interface for user access and operation, such as real-time monitoring, historical data query, etc. Remote user device: allows remote users to access the central monitoring platform through the Internet. Third-party system: integrates with third-party systems to achieve data sharing and service expansion. Network operator: provides wireless data transmission services. Enterprise network: provides internal network for wired data transmission.

[0104] like Figure 7 As shown, the flowchart of intelligent fault self-diagnosis and maintenance prompts;

[0105] Start: Start the intelligent fault self-diagnosis and maintenance reminder process. Real-time data monitoring: The system monitors the equipment operation data in real time. Data anomaly detection: Detect whether there are anomalies in the monitoring data. Fault self-diagnosis: When data anomalies are detected, the system performs fault self-diagnosis. Diagnosis result analysis: Analyze the results of fault self-diagnosis. Maintenance required: Determine whether maintenance is required. Generate maintenance reminder: If maintenance is required, the system generates a maintenance reminder. Maintenance reminder approval: The maintenance reminder needs to go through the approval process. Approval passed: Determine whether the maintenance reminder has been approved. Send maintenance notification: After approval, the system sends a maintenance notification to relevant personnel. Re-evaluation: If the maintenance reminder is not approved, the system will re-evaluate the fault situation. Maintenance execution: After receiving the maintenance notification, perform maintenance work. Update system status: After maintenance is completed, update the system status to reflect the latest equipment status. End: Complete the intelligent fault self-diagnosis and maintenance reminder process.

[0106] The rain gauge adopts a tipping bucket design. Rainwater enters the water receiver through the rain receiving port and flows into the tipping bucket. When the amount of accumulated water reaches a predetermined value, the tipping bucket flips and sends an electronic pulse signal through the reed switch to achieve accurate measurement.

[0107] The flowmeter includes an ultrasonic Doppler flowmeter and an electromagnetic flowmeter; the ultrasonic Doppler flowmeter adopts a dual-frequency

[0108] Doppler technology DFD, by generating two sets of ultrasonic signals with different frequencies, effectively removes noise signals and square-amplifies the accurately identified Doppler signals to improve signal recognition and tracking capabilities;

[0109] The electromagnetic flowmeter includes a capacitance electromagnetic flowmeter and a multi-electrode non-full-tube electromagnetic flowmeter;

[0110] The capacitive electromagnetic flowmeter adopts the capacitive measurement principle. The electrodes and the measured fluid form a detection capacitor through an insulating tube, and the flow signal is coupled through the change of the capacitance.

[0111] Multi-electrode non-full pipe electromagnetic flowmeter: Under non-full pipe conditions, multiple pairs of electrodes are used to measure fluid flow rate and liquid level height, and the measured values ​​are corrected through algorithms to achieve accurate measurement of non-full pipe flow.

[0112] The liquid level meter includes an ultrasonic liquid level meter, a radar liquid level meter, and a capacitive liquid level meter;

[0113] Among them, the ultrasonic level meter measures the liquid level by emitting ultrasonic pulses and receiving echoes reflected by the liquid surface; the ultrasonic waves emitted by the sensor are reflected when they encounter the liquid surface, and the sensor receives the reflected waves and calculates the time difference between emission and reception to determine the liquid level height;

[0114] Radar level meter: uses radio waves, i.e. radar waves, to measure liquid level. The radar waves emitted by the sensor are reflected on the liquid surface, the reflected waves are received and the time difference is calculated to determine the liquid level;

[0115] Capacitive level gauge: determines the liquid level by measuring the change in capacitance between the liquid and the container wall. The change in liquid level causes the change in capacitance value, thus reflecting the height of the liquid level;

[0116] Electronic water gauge: Utilizes the weak conductivity of water to collect water depth information through a string of electrodes arranged at equal intervals; the electrodes present different potentials in water with different conductivity, thereby determining whether the electrodes are submerged in water and then calculating the water level;

[0117] The water level value W is calculated by the formula W = ∑(E i -E i-1 )×S calculation, where E i is the potential of the ith electrode, S is the electrode spacing;

[0118] Underground liquid level meter: Use high-precision pressure sensors to measure the pressure of the liquid on the bottom of the container to determine the liquid level. When the liquid level changes, the pressure of the liquid on the bottom will also change. By measuring this pressure change, the liquid level information can be obtained.

[0119] The liquid level h is determined by the formula: Calculate, where P is the total pressure of the liquid at the liquid level, P0 is the reference pressure, i.e., atmospheric pressure, ρ is the density of the liquid, and g is the acceleration due to gravity;

[0120] COD online water quality monitor: It uses ultraviolet catalytic oxidation technology to accelerate the decomposition of organic matter, improve digestion efficiency and COD measurement accuracy; COD value is usually calculated based on oxidant consumption, and is obtained by comparing the absorbance of the digested solution with the standard curve. The calculation formula is COD = A × S, where A is the absorbance of the solution and S is the slope of the standard curve;

[0121] Ammonia nitrogen online water quality monitor: uses the ion selective electrode method to determine the concentration by measuring the potential change of ammonia nitrogen ions in the water sample on a specific electrode; in an environment where the pH value is greater than 11, ammonium ions transform into ammonia, and ammonia transfers through the hydrophobic membrane of the ammonia-sensitive electrode, causing a change in the electromotive force of the ammonia-sensitive electrode. The instrument measures the concentration of ammonia nitrogen based on the change in electromotive force; the ammonia nitrogen concentration N can be calculated through the relationship between the electrode potential E and the standard curve, using a linear or nonlinear regression model.

[0122] SS suspended solids online water quality monitor: It uses the scattered light measurement principle to determine the concentration of suspended solids by measuring the scattering intensity of suspended solid particles in water to light of a specific wavelength; the suspended solids concentration C can be calculated through the relationship between the scattered light intensity I and the known calibration curve, that is, C = f(I), where f is a calibration function established based on experimental data.

[0123] The principle of the intelligent data analysis and diagnosis module specifically includes the following steps:

[0124] Use big data analysis technologies, including Hadoop and Spark distributed computing frameworks, to process and analyze large-scale data sets;

[0125] Use data mining techniques, including clustering, classification, and regression analysis, to extract valuable information from data;

[0126] Apply machine learning algorithms, including decision trees, random forests, neural networks, etc., to build prediction models to predict and diagnose system status;

[0127] The specific calculation is as follows: Calculate the mean of a data set to reflect the central tendency of the data;

[0128] in, is the sample mean, i.e., the average value of the data set; n is the sample size, i.e., the number of observations in the data set; x i is the i-th observation value;

[0129] By formula Measures the degree of dispersion of data; where σ 2 is the sample variance, which indicates the degree of dispersion of the data set, that is, the average of the squares of the differences between each data and its mean;

[0130] The linear regression model y = β0 + β1x + ∈ predicts and evaluates the relationship between variables; y is the dependent variable, that is, the response variable, which is the variable that the model attempts to predict or explain; β0 is the intercept term, which represents the expected value of the dependent variable y when the independent variable x is zero; β1 is the slope, which represents how the dependent variable y is expected to change for every unit change in the independent variable x; x is the independent variable, that is, the explanatory variable, which is the predictor variable in the model; ∈ is the error term, which represents the random variation that the model cannot explain;

[0131] Cluster analysis: K-means clustering optimization objective formula: It is used to divide data points into multiple clusters; J is the sum of squares of intra-cluster differences, which is used to evaluate the optimization target of clustering effect, that is, the sum of squares of distances between all data points and their corresponding cluster centers; k is the number of clusters, that is, the number of clusters to which the data is to be divided; n i is the number of observations in the i-th cluster; x ij is the jth observation in the i-th cluster; μ i is the cluster center of the ith cluster.

[0132] The remote control and automation management module includes an intelligent terminal power supply controller, a remote control system and an automation management strategy;

[0133] Among them, the intelligent terminal power supply controller: uses network technology to allow users to remotely power on, off and query electrical equipment through the intelligent terminal power supply controller; at the same time, the controller can monitor parameters such as load current, leakage current, voltage and temperature in real time, and issue an alarm through the management platform when the data is abnormal;

[0134] Remote control system: used to control remote devices through TCP / IP protocol network data communication; based on different communication protocols, including UDP or TCP, to ensure the accuracy and continuity of data transmission; UDP protocol is suitable for scenarios with high real-time requirements but relatively low reliability requirements, while TCP protocol provides more stable connection and data transmission;

[0135] Automation management strategy: used to automatically perform specific tasks through preset rules and conditions, including timing switching equipment and adjusting power distribution according to load conditions;

[0136] The principles of the environmental adaptability and safety protection module specifically include:

[0137] IP68 waterproof rating: IP68 is the highest rating in the international protection level standard, indicating that the device is completely dustproof and can be immersed in water for a long time without damage; the use of sealing technology, including rubber gaskets, waterproof coatings and sealed cabins, ensures that all circuits and interfaces are fully protected;

[0138] Intrinsic safety explosion-proof level: ensures that the equipment will not cause an explosion when used in an inflammable and explosive environment by limiting the electric sparks or heat energy that may be generated on the surface and inside of the equipment; using special circuit design and materials to ensure that under specified conditions, the electric sparks or heat energy generated by the equipment are not enough to ignite the surrounding flammable gas or dust;

[0139] Leakage detection and overload protection: They work by monitoring the current between the device housing and the ground. Once an abnormal current is detected, the system automatically cuts off the power supply to prevent electric shock accidents. Overload protection prevents device damage by monitoring whether the current exceeds the device rating. High-precision current sensors and control circuits are integrated to monitor current changes in real time and compare them with preset safety thresholds.

[0140] Lightning protection: Reduce the impact of high voltage and current generated by lightning on equipment by installing lightning arresters and surge protectors; install surge protectors in the power lines and signal lines of equipment, using the nonlinear resistance material inside to conduct electricity under high voltage and direct excess current to the ground.

[0141] The principle of the communication and data transmission optimization module specifically includes

[0142] 4G communication: Utilize the high-speed data transmission capability of the 4G network to achieve remote wireless transmission of monitoring data; the 4G network provides a higher data transmission rate and a wider frequency band, which is suitable for real-time data transmission; integrate the 4G module into the monitoring equipment and transmit data through the operator's network infrastructure. Data is transmitted in the mobile network through wireless signals and forwarded to the monitoring system through base stations and network equipment;

[0143] OPC interface: OPC is an industrial standard communication protocol used to achieve data exchange between different devices and systems; OPC interface allows seamless data transmission between monitoring equipment and monitoring system; OPC server function is implemented in monitoring equipment to provide real-time data access interface; monitoring system acts as OPC client and reads device data through OPC protocol;

[0144] Data transmission optimization algorithm: Use advanced data compression and transmission control algorithms to reduce the size and number of data packets, improve transmission efficiency, and reduce transmission delays; perform compression processing before data transmission to reduce the amount of data. At the same time, dynamically adjust the transmission rate and retransmission strategy according to network conditions to optimize the transmission path;

[0145] Among them, the data transmission efficiency can be expressed by the formula To evaluate, where E is efficiency, D is data volume, and T is transmission time;

[0146] Network quality monitoring and optimization: Real-time monitoring of network quality, including signal strength, bandwidth utilization, delay and packet loss rate indicators, and dynamic adjustment of data transmission strategies; use network monitoring tools and algorithms to analyze network status, and adjust data transmission parameters based on analysis results, including the number of retransmissions, transmission rate and packet size; optimize network parameters to ensure that data transmission delay and packet loss rate meet control indicator requirements.

[0147] The intelligent fault self-diagnosis and maintenance prompt module specifically includes:

[0148] Fault prediction model based on machine learning: Use machine learning algorithms, including neural networks and support vector machines, to analyze the historical operation data and maintenance records of the equipment and establish a fault prediction model. By collecting the key performance indicators (KPIs) of the equipment, using data mining technology to extract features and train the fault prediction model; the model can identify fault modes and trends and predict potential faults.

[0149] Real-time health monitoring and adaptive maintenance strategy: Real-time monitoring of its operating status, including temperature, vibration, and pressure parameters, and adjusting the maintenance plan through adaptive algorithms; sensors are used to collect real-time data, which is processed through embedded systems to update the health status of the equipment in real time. The adaptive algorithm dynamically adjusts the maintenance strategy based on the current status of the equipment and historical maintenance records; the service life RUL can be calculated through a predictive model, such as using an inverse Gaussian process or other survival models to estimate;

[0150] Intelligent maintenance reminders and remote support: When the system detects a potential fault or maintenance requirement, it automatically generates a maintenance reminder and can be sent to maintenance personnel via remote communication; integrated communication module, including 4G or Wi-Fi, for data transmission and remote communication; the maintenance reminder system generates maintenance suggestions based on the output of the fault prediction model, and can notify maintenance personnel via mobile applications or text messages; the maintenance response time can be estimated by the formula T = D / S, where D is the urgency of the maintenance requirement and S is the availability of maintenance resources.

[0151] Example 1: Intelligent fault diagnosis of drainage network monitoring system:

[0152] Background: In a large city, the health of the drainage network is crucial to prevent flooding and waterlogging. In order to ensure the effective operation of the drainage system, a monitoring system integrating intelligent fault self-diagnosis and maintenance prompts is deployed.

[0153] Implementation steps:

[0154] Equipment deployment: Various sensors, including water level meters, flow meters, water quality monitors, etc., as well as intelligent data acquisition units, are installed at key nodes of the drainage network. Data collection: Sensors collect data in real time and transmit it to the central monitoring platform through the 4G network. Data analysis: The collected data is statistically analyzed using the mean formula and variance formula to determine the central trend and degree of dispersion of the data. Fault diagnosis: Linear regression models are applied to analyze abnormal patterns in the data and identify possible fault points. Maintenance prompts: When an abnormality is detected, the system automatically generates maintenance prompts and sends them to the mobile devices of the maintenance team. Maintenance execution: The maintenance team conducts on-site inspections and necessary maintenance work based on the prompts. System optimization: Historical fault data is analyzed through cluster analysis, the fault diagnosis model is optimized, and the accuracy and response speed of the system are improved.

[0155] Results: The system successfully predicted and prevented multiple possible drainage system failures, significantly reducing flooding and waterlogging incidents due to equipment failures, and improving the overall reliability of the city's drainage system and the quality of life of residents.

[0156] Example 2: Intelligent maintenance system for industrial wastewater treatment plants:

[0157] Background: An industrial wastewater treatment plant needs to ensure that its treatment equipment continues to operate stably to meet environmental regulations and avoid production interruptions.

[0158] Implementation steps:

[0159] System installation: Smart sensors are installed on key equipment for wastewater treatment, including pumps, agitators, and filters, and smart terminal power supply controllers are deployed. Data monitoring: Sensors monitor equipment status in real time, including parameters such as current, voltage, temperature, and vibration. Anomaly detection: Data is analyzed in real time using mean and variance formulas to identify deviations from normal operating conditions. Fault prediction: Machine learning algorithms, such as random forests, are used to conduct in-depth analysis of data to predict potential equipment failures. Maintenance scheduling: Once a possible failure is predicted, the system automatically generates a maintenance work order and schedules it based on the availability of the maintenance team. Maintenance execution: The maintenance team performs preventive maintenance based on system prompts, such as replacing worn parts or adjusting equipment settings. Performance evaluation: The effectiveness of maintenance activities is evaluated through cluster analysis, and maintenance strategies are continuously optimized.

[0160] Results: The smart maintenance system significantly improved the operational efficiency of the wastewater treatment plant, reduced unplanned downtime, ensured the continuity and compliance of wastewater treatment, and significantly reduced maintenance costs by reducing the need for emergency repairs.

[0161] The drainage network hydraulic water quality monitoring and control system of the present invention realizes technical innovation in many aspects:

[0162] Integrated monitoring equipment: The present invention integrates a variety of monitoring equipment such as rain gauges, flow meters, liquid level meters and water quality monitors, and realizes centralized data collection and processing through a unified platform, solving the problems of scattered equipment and data islands in the prior art.

[0163] Intelligent data analysis and diagnosis: Using advanced data analysis algorithms, such as machine learning and data mining technology, the collected monitoring data is analyzed in real time to effectively identify abnormal patterns and potential problems in the drainage system, improving the system's prediction accuracy and response speed.

[0164] Remote control and automated management: Through the intelligent terminal power supply controller and remote control system, remote control and automated management of drainage network equipment are realized, reducing the need for manual inspections and improving the efficiency of maintenance work.

[0165] Environmental adaptability and safety protection design: The equipment is designed to meet the IP68 waterproof level and intrinsic safety explosion-proof level requirements, and integrates multiple safety protection measures such as leakage detection, overload protection, lightning protection and signal isolation to ensure the stable operation of the system in harsh environments.

[0166] Communication and data transmission optimization: Utilize 4G communication and OPC interface to optimize data transmission, ensure that data is transmitted to the monitoring system in real time and accurately, and improve the real-time and accuracy of data.

[0167] Intelligent fault self-diagnosis and maintenance prompts: The system can automatically detect and diagnose potential equipment failures and provide timely maintenance suggestions, reducing maintenance costs and extending equipment life.

[0168] Through the description of the above innovations, the present invention not only improves the efficiency and data accuracy of drainage network monitoring, but also provides strong technical support for water resources management and environmental protection, and has significant social and economic value. Improve monitoring accuracy: Through high-precision sensors and advanced data processing algorithms, the accuracy of drainage network monitoring data is improved. Enhance system reliability: Integrate multiple safety protection measures, such as waterproof, dustproof, explosion-proof and lightning protection, to ensure the stable operation of the system in harsh environments. Optimize maintenance efficiency: Intelligent fault self-diagnosis and maintenance reminder functions reduce the need for manual inspections and improve the efficiency and response speed of maintenance work. Reduce operating costs: Predictive maintenance strategies reduce unexpected downtime and emergency repairs, and reduce long-term operating costs. Improve data security: Use data encryption technology and secure communication protocols to protect monitoring data from unauthorized access. Enhance system adaptability: Modular design and flexible configuration options enable the system to adapt to drainage networks of different sizes and needs. Realize remote management: Remote control and automated management functions allow operators to manage monitoring equipment from any location. Improve decision support: Real-time data analysis and diagnosis provide scientific decision support for drainage system management. Environmentally friendly: Reduce water waste and environmental pollution through effective monitoring and maintenance. Promote sustainable development: Support the sustainable use of urban water resources and the healthy development of urban ecology through intelligent management.

Claims

1. A drainage network hydraulic water quality monitoring and control system, characterized by: It includes integrated monitoring module, intelligent data analysis and diagnosis module, remote control and automation management module, environmental adaptability and safety protection module, communication and data transmission optimization module, intelligent fault self-diagnosis and maintenance prompt module; Among them, the integrated monitoring module is used to monitor the water quantity, water quality, liquid level and other parameters in the drainage network in real time through a variety of monitoring equipment, including rain gauge, flow meter, liquid level meter, electronic water gauge, buried liquid level meter, COD online water quality monitor, ammonia nitrogen online water quality monitor, suspended solids online water quality monitor; Intelligent data analysis and diagnosis module, which is used to collect and process monitoring data in real time, and use statistics, pattern recognition, and machine learning methods to analyze monitoring data in real time, discover patterns and trends from large amounts of data, and then identify abnormal values, trend changes, and periodic patterns in the data, thereby providing decision support for drainage system management; Remote control and automation management module, used to achieve remote control and automation management of drainage network equipment through intelligent terminal power supply controller and remote control system; Environmental adaptability and safety protection module, used to meet IP68 waterproof level and intrinsic safety explosion-proof level requirements, integrated leakage detection, overload protection, lightning protection and signal isolation; Communication and data transmission optimization module, which is used to optimize data transmission using 4G communication and OPC interface to ensure that data is transmitted to the monitoring system in real time and accurately; Intelligent fault self-diagnosis and maintenance reminder module is used for automatic fault diagnosis and maintenance reminder, reducing maintenance costs and extending equipment life.

2. A drainage network hydraulic and water quality monitoring and control system according to claim 1, characterized in that: The rain gauge adopts a tipping bucket design. Rainwater enters the water receiver through the rain receiving port and flows into the tipping bucket. When the amount of accumulated water reaches a predetermined value, the tipping bucket flips and sends an electronic pulse signal through the reed switch to achieve accurate measurement. The flowmeter includes an ultrasonic Doppler flowmeter and an electromagnetic flowmeter; the ultrasonic Doppler flowmeter adopts dual-frequency Doppler technology DFD, which effectively removes noise signals by generating two sets of ultrasonic signals with different frequencies, and square-amplifies the accurately identified Doppler signals to improve the signal recognition and tracking capabilities; The electromagnetic flowmeter includes a capacitance electromagnetic flowmeter and a multi-electrode non-full-tube electromagnetic flowmeter; The capacitive electromagnetic flowmeter adopts the capacitive measurement principle. The electrode and the measured fluid form a detection capacitor through an insulating tube, and the flow signal is coupled through the change of the capacitance. Multi-electrode non-full pipe electromagnetic flowmeter: Under non-full pipe conditions, multiple pairs of electrodes are used to measure fluid flow rate and liquid level height, and the measured values ​​are corrected through algorithms to achieve accurate measurement of non-full pipe flow; The liquid level meter includes an ultrasonic liquid level meter, a radar liquid level meter, and a capacitive liquid level meter; Among them, the ultrasonic level meter measures the liquid level by emitting ultrasonic pulses and receiving echoes reflected by the liquid surface; the ultrasonic waves emitted by the sensor are reflected when they encounter the liquid surface, and the sensor receives the reflected waves and calculates the time difference between emission and reception to determine the liquid level height; Radar level meter: uses radio waves, namely radar waves, to measure liquid level; the radar waves emitted by the sensor are reflected on the liquid surface, the reflected waves are received and the time difference is calculated to determine the liquid level; Capacitive level gauge: determines the liquid level by measuring the change in capacitance between the liquid and the container wall; changes in the liquid level cause changes in the capacitance value, thus reflecting the height of the liquid level; Electronic water gauge: Utilizes the weak conductivity of water to collect water depth information through a string of electrodes arranged at equal intervals; the electrodes present different potentials in water with different conductivity, thereby determining whether the electrodes are submerged in water and then calculating the water level; The water level W is calculated by the formula W = Σ(E i -E i-1 )×S calculation, where E i is the potential of the ith electrode, S is the electrode spacing; Underground liquid level meter: Use high-precision pressure sensors to measure the pressure of the liquid on the bottom of the container to determine the liquid level. When the liquid level changes, the pressure of the liquid on the bottom will also change. By measuring this pressure change, the liquid level information can be obtained. The liquid level h is determined by the formula: Calculate, where P is the total pressure of the liquid at the liquid level, P0 is the reference pressure, i.e., atmospheric pressure, ρ is the density of the liquid, and g is the acceleration due to gravity; COD online water quality monitor: It uses ultraviolet catalytic oxidation technology to accelerate the decomposition of organic matter, improve digestion efficiency and COD measurement accuracy; COD value is usually calculated based on oxidant consumption, and is obtained by comparing the absorbance of the digested solution with the standard curve. The calculation formula is COD = A × S, where A is the absorbance of the solution and S is the slope of the standard curve; Ammonia nitrogen online water quality monitor: uses the ion selective electrode method to determine the concentration by measuring the potential change of ammonia nitrogen ions in the water sample on a specific electrode; in an environment where the pH value is greater than 11, ammonium ions transform into ammonia, and ammonia transfers through the hydrophobic membrane of the ammonia sensitive electrode, causing the electromotive force of the ammonia sensitive electrode to change. The instrument measures the concentration of ammonia nitrogen based on the change in electromotive force; the ammonia nitrogen concentration N can be calculated from the relationship between the electrode potential E and the standard curve, using a linear or nonlinear regression model; SS suspended solids online water quality monitor: It uses the scattered light measurement principle to determine the concentration of suspended solids by measuring the scattering intensity of suspended solid particles in water to light of a specific wavelength; the suspended solids concentration C can be calculated through the relationship between the scattered light intensity I and the known calibration curve, that is, C = f(I), where f is a calibration function established based on experimental data.

3. A drainage network hydraulic and water quality monitoring and control system according to claim 1, characterized in that: The principle of the intelligent data analysis and diagnosis module specifically includes the following steps: Use big data analysis technologies, including Hadoop and Spark distributed computing frameworks, to process and analyze large-scale data sets; Use data mining techniques, including clustering, classification, and regression analysis, to extract valuable information from data; Apply machine learning algorithms, including decision trees, random forests, neural networks, etc., to build prediction models to predict and diagnose system status; The specific calculation is as follows: Calculate the mean of a data set to reflect the central tendency of the data; in, is the sample mean, i.e. the average value of the data set; n is the sample size, i.e. the number of observations in the data set; xi is the i-th observation; By formula Measures the degree of dispersion of data; where σ 2 is the sample variance, which indicates the degree of dispersion of the data set, that is, the average of the squares of the differences between each data and its mean; The linear regression model y = β0 + β1x + ∈ predicts and evaluates the relationship between variables; y is the dependent variable, that is, the response variable, which is the variable that the model attempts to predict or explain; β0 is the intercept term, which represents the expected value of the dependent variable y when the independent variable x is zero; β1 is the slope, which represents how the dependent variable y is expected to change for every unit change in the independent variable x; x is the independent variable, that is, the explanatory variable, which is the predictor variable in the model; ∈ is the error term, which represents the random variation that the model cannot explain; Cluster analysis: K-means clustering optimization objective formula: It is used to divide data points into multiple clusters; J is the sum of squares of intra-cluster differences, which is used to evaluate the optimization target of clustering effect, that is, the sum of squares of distances between all data points and their corresponding cluster centers; k is the number of clusters, that is, the number of clusters to which the data is to be divided; n i is the number of observations in the i-th cluster; x ij is the jth observation in the i-th cluster; μ i is the cluster center of the ith cluster.

4. A drainage network hydraulic and water quality monitoring and control system according to claim 1, characterized in that: The remote control and automation management module includes an intelligent terminal power supply controller, a remote control system and an automation management strategy; Among them, the intelligent terminal power supply controller: uses network technology to allow users to remotely power on, off and query electrical equipment through the intelligent terminal power supply controller; at the same time, the controller can monitor parameters such as load current, leakage current, voltage and temperature in real time, and issue an alarm through the management platform when the data is abnormal; Remote control system: used to control remote devices through TCP / IP protocol network data communication; based on different communication protocols, including UDP or TCP, to ensure the accuracy and continuity of data transmission; UDP protocol is suitable for scenarios with high real-time requirements but relatively low reliability requirements, while TCP protocol provides more stable connection and data transmission; Automation management strategy: used to automatically perform specific tasks through preset rules and conditions, including timing switching equipment and adjusting power distribution according to load conditions; 5. A drainage network hydraulic and water quality monitoring and control system according to claim 1, characterized in that: The principles of the environmental adaptability and safety protection module specifically include: IP68 waterproof rating: IP68 is the highest rating in the international protection level standard, indicating that the device is completely dustproof and can be immersed in water for a long time without damage; the use of sealing technology, including rubber gaskets, waterproof coatings and sealed cabins, ensures that all circuits and interfaces are fully protected; Intrinsic safety explosion-proof level: ensures that the equipment will not cause an explosion when used in an inflammable and explosive environment by limiting the electric sparks or heat energy that may be generated on the surface and inside of the equipment; using special circuit design and materials to ensure that under specified conditions, the electric sparks or heat energy generated by the equipment are not enough to ignite the surrounding flammable gas or dust; Leakage detection and overload protection: It works by monitoring the current between the equipment casing and the ground. Once an abnormal current is detected, the system will automatically cut off the power supply to prevent electric shock accidents; Overload protection prevents equipment damage by monitoring whether the current exceeds the rated value of the equipment. It integrates high-precision current sensors and control circuits to monitor current changes in real time and compare them with preset safety thresholds. Lightning protection: Reduce the impact of high voltage and current generated by lightning on equipment by installing lightning arresters and surge protectors; install surge protectors in the power lines and signal lines of equipment, using the nonlinear resistance material inside to conduct electricity under high voltage and direct excess current to the ground.

6. A drainage network hydraulic and water quality monitoring and control system according to claim 1, characterized in that: The principle of the communication and data transmission optimization module specifically includes 4G communication: Utilize the high-speed data transmission capability of 4G network to realize remote wireless transmission of monitoring data; 4G network provides higher data transmission rate and wider frequency band, which is suitable for real-time data transmission; integrate 4G module into monitoring equipment, and transmit data through the network infrastructure of the operator; data is transmitted in the mobile network through wireless signals, and forwarded to the monitoring system through base stations and network equipment; OPC interface: OPC is an industrial standard communication protocol used to achieve data exchange between different devices and systems; The OPC interface allows Enable seamless data transmission between monitoring equipment and monitoring system; implement OPC server function in monitoring equipment and provide real-time data access interface; monitoring system acts as OPC client and reads equipment data through OPC protocol; Data transmission optimization algorithm: Use advanced data compression and transmission control algorithms to reduce the size and number of data packets, improve transmission efficiency, and reduce transmission delays; perform compression processing before data transmission to reduce the amount of data; at the same time, dynamically adjust the transmission rate and retransmission strategy according to network conditions to optimize the transmission path; Among them, the data transmission efficiency can be expressed by the formula To evaluate, where E is efficiency, D is data volume, and T is transmission time; Network quality monitoring and optimization: Real-time monitoring of network quality, including signal strength, bandwidth utilization, delay and packet loss rate indicators, and dynamic adjustment of data transmission strategies; use network monitoring tools and algorithms to analyze network status, and adjust data transmission parameters based on analysis results, including the number of retransmissions, transmission rate and packet size; optimize network parameters to ensure that data transmission delay and packet loss rate meet control indicator requirements.

7. A drainage network hydraulic and water quality monitoring and control system according to claim 1, characterized in that: The intelligent fault self-diagnosis and maintenance prompt module specifically includes: Fault prediction model based on machine learning: Use machine learning algorithms, including neural networks and support vector machines, to analyze the historical operation data and maintenance records of the equipment and establish a fault prediction model. By collecting the key performance indicators (KPIs) of the equipment, using data mining technology to extract features and train the fault prediction model; the model can identify fault modes and trends and predict potential faults. Real-time health monitoring and adaptive maintenance strategy: real-time monitoring of its operating status, including temperature, vibration, and pressure parameters, and adjusting the maintenance plan through adaptive algorithms; using sensors to collect real-time data, processing it through embedded systems, and updating the health status of the equipment in real time; Adaptive algorithms dynamically adjust maintenance strategies based on the current status of the equipment and historical maintenance records; The service life RUL can be calculated by a prediction model, such as using an inverse Gaussian process or other survival models to estimate; Intelligent maintenance reminders and remote support: When the system detects a potential fault or maintenance requirement, it automatically generates a maintenance reminder and can be sent to maintenance personnel via remote communication; integrated communication module, including 4G or Wi-Fi, for data transmission and remote communication; the maintenance reminder system generates maintenance suggestions based on the output of the fault prediction model, and can notify maintenance personnel via mobile applications or text messages; the maintenance response time can be estimated by the formula T = D / S, where D is the urgency of the maintenance requirement and S is the availability of maintenance resources.

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