Intelligent sewage treatment system for extra-long tunnel

Abstract

The invention relates to the technical field of tunnel sewage treatment, in particular to an intelligent sewage treatment system for an extra-long tunnel. Comprising a sewage monitoring module, a water quality analysis module, an automatic processing module, an energy consumption management module and an anomaly detection module. The sewage monitoring module monitors sewage parameters in real time through a distributed sensor array and transmits data. And the water quality analysis module constructs a water quality map through dynamic water quality balance calculation by using multispectral imaging and electrochemical sensing technologies, and generates reports and suggestions. And the automatic processing module adjusts processing parameters according to the report. And the energy consumption management module monitors and optimizes energy consumption, identifies a high-energy-consumption link through an algorithm and takes energy-saving measures. The abnormity detection module monitors the system in real time, identifies early warning abnormal conditions and sends early warning information and a fault report. According to the invention, efficient, accurate and intelligent treatment of sewage in the extra-long tunnel is realized, the sewage treatment efficiency is remarkably improved, and the energy consumption and the operation cost are remarkably reduced.

Description

Technical Field

[0001] The invention relates to the technical field of tunnel sewage treatment, and in particular to an intelligent sewage treatment system for a very long tunnel. Background Art

[0002] During the construction and operation of extra-long tunnels, a large amount of sewage will inevitably be generated. If these sewage is not effectively treated, direct discharge will not only cause serious pollution to the environment, but also have a negative impact on the structure and traffic safety of extra-long tunnels. Therefore, the treatment of sewage from extra-long tunnels has become one of the important topics of urban infrastructure maintenance and environmental protection. At present, there are still the following problems: the existing technology relies on a single sensing technology for water quality analysis, the data accuracy is low, and it is difficult to effectively identify the trend of water quality changes and potential pollution sources, resulting in insufficient basis for treatment decisions and unsatisfactory treatment effects; the process parameter adjustment of the existing system is mostly fixed value or manual adjustment, lacking intelligent adjustment means, and it is difficult to dynamically optimize the treatment process according to real-time water quality changes, resulting in low treatment efficiency and serious waste of reagents; the existing system lacks comprehensive monitoring and optimization of energy consumption, and cannot identify and solve high energy consumption links, resulting in high system operating costs and low energy efficiency. Summary of the invention

[0003] To solve the above problems, the present invention provides an intelligent sewage treatment system for extra-long tunnels. Through tunnel sewage monitoring, analysis and automated treatment, the treatment process is automatically adjusted according to real-time data, the system operation is optimized, and the sewage treatment efficiency is significantly improved. This not only improves the treatment effect, but also significantly reduces energy consumption and operating costs.

[0004] To achieve the above object, the technical solution adopted by the present invention is:

[0005] An intelligent sewage treatment system for a super-long tunnel comprises a sewage monitoring module, a water quality analysis module, an automatic treatment module, an energy consumption management module and an abnormality detection module which are sequentially connected in communication;

[0006] The sewage monitoring module is used to monitor sewage parameters in the tunnel in real time through a distributed sensor array and transmit the monitoring data to the water quality analysis module; the sewage parameters include flow, pH value, turbidity, temperature, chemical oxygen demand, biological oxygen demand, heavy metal concentration and microbial activity;

[0007] The water quality analysis module is used to use multispectral imaging technology and electrochemical sensing technology to improve data accuracy through cross-validation, and in combination with a dynamic water quality balance calculation method, to construct a spatiotemporal water quality change map, to conduct in-depth analysis of the data transmitted by the sewage monitoring module, to identify water quality change trends and potential pollution sources, and to generate dynamic water quality reports and treatment suggestions, which are then transmitted to the automated processing module;

[0008] The automated processing module is used to intelligently adjust the processing parameters, including precise control of reagent addition, aeration volume and sedimentation time, based on the dynamic water quality report and processing suggestions provided by the water quality analysis module, using an adaptive control algorithm and a fuzzy logic control strategy;

[0009] The energy consumption management module is used to monitor and optimize the energy consumption of the entire sewage treatment process, identify high energy consumption links through energy consumption evaluation algorithms, and take energy-saving measures;

[0010] The anomaly detection module is used to monitor each link of the system in real time, identify and warn of possible abnormal conditions through anomaly detection algorithms, and send multi-level warning information and detailed fault diagnosis reports through the Internet of Things platform.

[0011] Furthermore, the operation process of the water quality analysis module includes the following steps:

[0012] Collect sewage parameter data through a distributed sensor array for preliminary filtering and calibration;

[0013] Use multispectral imaging technology to detect the spectral characteristics of various pollutants in sewage, and use electrochemical sensing technology to determine the ion concentration and electrochemical characteristics of sewage, and combine the two technologies for data cross-validation;

[0014] Based on real-time data, the dynamic water quality balance calculation method is applied to calculate the spatiotemporal distribution of various water quality parameters and generate a water quality change map;

[0015] Using graph convolutional networks and sparse coding algorithms, we conduct in-depth analysis of the spatiotemporal map of water quality changes, identify long-term and short-term water quality trends, predict future water quality conditions, and use geographic information system technology to trace pollution sources and locate potential pollution sources;

[0016] Based on the results of intelligent trend analysis and pollution source tracing data, the differential evolution algorithm is used to generate dynamically adjusted treatment strategies and optimization parameters, and generate a water quality management report including water quality analysis results, treatment recommendations and emergency plans.

[0017] Furthermore, the formula of the dynamic water quality balance calculation method is as follows:

[0018]

[0019] Where WQt represents the water quality parameter value at time t; WQ0 represents the water quality parameter value at the initial time; ΔP i represents the change in pollutant emissions from the ith pollution source; F i represents the flow of the i-th pollution source; A i represents the affected area of ​​the i-th pollution source; R jrepresents the removal efficiency of the jth treatment unit; E j represents the energy consumption of the jth processing unit; V j represents the processing volume of the jth processing unit; n represents the total number of pollution sources; m represents the total number of processing units.

[0020] Furthermore, the formula of the graph convolutional network is as follows:

[0021]

[0022] in, represents the characteristic value of the jth water quality parameter at the i-th monitoring point in the I+1 layer; A represents the characteristic value of the jth water quality parameter at the ith monitoring point in the ith layer; k，im represents the connection relationship between monitoring point i and monitoring point m; N(i) represents the set of all nodes adjacent to node i; d i and d m represents the degree of node i and node m, that is, the number of monitoring points connected to each; Θ k，j represents the jth weight matrix parameter of the kth convolution kernel; B j Represents the bias term of the jth feature.

[0023] Furthermore, the multispectral imaging technology in the water quality analysis module includes imaging of ultraviolet spectrum, visible spectrum and near-infrared spectrum, and identifies different chemical components in sewage and their concentration changes through spectral analysis; the electrochemical sensing technology includes real-time electrochemical monitoring using a microelectrode array, and the microelectrode array includes multiple independent microelectrodes, each microelectrode is used to detect the electrochemical reaction signal of a specific pollutant.

[0024] Furthermore, the operation process of the automation processing module includes the following steps:

[0025] Receive and analyze the water quality management report transmitted by the water quality analysis module, identify the sewage parameters to be treated and their changing trends, and determine the treatment strategy;

[0026] Apply fractional calculus algorithm to accurately model the sewage treatment process based on fractional differential equations, and flexibly control the treatment process by adjusting fractional parameters, including reagent dosage, aeration volume and sedimentation time;

[0027] Use chaos theory control strategies to analyze the nonlinear dynamic behavior of the system and use chaos control technology to optimize the sewage treatment process in real time;

[0028] Monitor key process parameters in real time and generate process logs and reports.

[0029] Furthermore, the fractional order differential equation is as follows:

[0030]

[0031] Among them, D α represents the fractional differential operator; α represents the fractional order, ranging from 0 to 1; y( t ) represents the treatment variables at time t, including the concentration of a pollutant in the sewage; f(t, y(t)) represents the dynamic function, describing the natural variation law in the sewage treatment process; u i (t) represents the i-th controlled variable, including the dosage of the reagent, the aeration volume and the sedimentation time; n represents the total number of controlled variables.

[0032] Furthermore, the energy consumption assessment algorithm includes one or more of a Bayesian network algorithm, a self-organizing mapping algorithm, a reinforcement learning algorithm, and a fractal geometry analysis algorithm.

[0033] Furthermore, it also includes a three-dimensional visualization module, which is used to present the operating status of the system in three dimensions.

[0034] The beneficial effects of the present invention are as follows: the sewage monitoring module of the present invention can monitor a variety of sewage parameters in real time through a distributed sensor array, ensure the timeliness and comprehensiveness of the data, and provide an accurate monitoring basis. The water quality analysis module uses multi-spectral imaging technology and electrochemical sensing technology to improve the accuracy of data analysis through a cross-validation method. At the same time, the construction of a dynamic water quality balance calculation method and a spatiotemporal water quality change map helps to identify water quality change trends and potential pollution sources, and provide detailed dynamic water quality reports and treatment suggestions. The automated processing module uses an adaptive control algorithm and a fuzzy logic control strategy based on the dynamic water quality report and treatment suggestions generated by the water quality analysis module to intelligently adjust the treatment process parameters, achieve precise control of the dosage of the reagent, the aeration volume, and the sedimentation time, and improve the sewage treatment effect while reducing the waste of reagents and energy consumption. The energy consumption management module monitors and optimizes the energy consumption in the sewage treatment process, identifies high-energy consumption links through an energy consumption evaluation algorithm, and takes corresponding energy-saving measures to effectively reduce the system operating cost and improve energy efficiency. The anomaly detection module monitors all aspects of the system in real time, uses anomaly detection algorithms to identify and warn of potential abnormal conditions, and sends multi-level warning information and detailed fault diagnosis reports through the Internet of Things platform to ensure the stable operation of the system and reduce the impact of sudden failures on the sewage treatment process. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a module schematic diagram of an intelligent sewage treatment system for extra-long tunnels according to the present invention.

[0036] Figure 2 It is a schematic flow chart of the operation process of the water quality analysis module in the present invention.

[0037] Figure 3 It is a flow chart of the operation process of the automation processing module in the present invention. DETAILED DESCRIPTION

[0038] See also Figure 1-3 As shown, the present invention relates to an intelligent sewage treatment system for an extra-long tunnel.

[0039] Example

[0040] An intelligent sewage treatment system for a super-long tunnel comprises a sewage monitoring module, a water quality analysis module, an automatic treatment module, an energy consumption management module and an abnormality detection module which are sequentially connected in communication;

[0041] The sewage monitoring module is used to monitor sewage parameters in the tunnel in real time through a distributed sensor array and transmit the monitoring data to the water quality analysis module; the sewage parameters include flow, pH value, turbidity, temperature, chemical oxygen demand, biological oxygen demand, heavy metal concentration and microbial activity;

[0042] It should be noted that the distributed sensor array of the sewage monitoring module is as follows:

[0043] Flow sensor: Ultrasonic flow sensors are installed at the entrance, middle section and exit of the tunnel to ensure that flow changes at different locations can be monitored. Each sensor is installed at an appropriate position in the sewage pipe, with the sensor head located in the center of the pipe to ensure the accuracy of flow measurement. The ultrasonic sensor transmits and receives ultrasonic signals, measures the propagation time difference of the signal in the sewage, and calculates the flow rate and flow of the sewage. The sensor transmits the flow data to the central control system in real time for flow monitoring and data analysis.

[0044] pH sensor: pH sensors are installed at different locations along the tunnel, including the tunnel entrance, middle section, key discharge ports and exits. The sensor is installed inside the sewage pipe with a fixed bracket to ensure that the electrode can be fully immersed in the sewage. The electrode-type pH sensor calculates and displays the pH value by measuring the concentration of hydrogen ions in the sewage. The pH value changes are monitored in real time and the data is transmitted to the central monitoring system for water quality analysis and treatment decisions.

[0045] Turbidity sensor: Turbidity sensors are installed at key locations in the tunnel and in areas where sewage flow changes greatly. The sensor is installed in an insert-type manner, with the sensor head located in the middle of the sewage pipe to ensure accurate measurement. Using light scattering technology, the sensor emits a light beam and measures the scattering intensity of the light beam by suspended particles in the sewage to calculate the turbidity value. Turbidity data is transmitted in real time to the central monitoring system for water quality monitoring and analysis.

[0046] Temperature sensor: Thermocouple temperature sensors are installed in a distributed manner to monitor the temperature changes of sewage in real time. Thermocouple sensors measure the temperature changes of sewage, generate corresponding electrical signals, and convert them into temperature data. The temperature changes are monitored in real time and the data is transmitted to the central monitoring system for water quality analysis and treatment control.

[0047] Chemical oxygen demand (COD) sensor: uses an electrochemical sensor to measure the concentration of organic matter in sewage. The sensor is coated with anti-corrosion materials to ensure long-term stable operation. The concentration of organic matter in sewage is measured through electrochemical reactions, and the chemical oxygen demand (COD) value is calculated. The COD data is transmitted to the central monitoring system in real time for water quality analysis and treatment optimization.

[0048] Biological oxygen demand (BOD) sensor: The biosensor array measures the amount of oxygen required for microorganisms to decompose organic matter in sewage. The sensor detects the amount of oxygen required for the decomposition of organic matter in sewage through microbial sensors and calculates the biological oxygen demand (BOD) value. The BOD data is transmitted to the central monitoring system in real time for water quality monitoring and treatment decision-making.

[0049] Heavy metal concentration sensor: Use electrochemical sensors and spectroscopy technology to detect the concentration of heavy metal ions in sewage. Use electrochemical sensors and spectroscopy technology to detect the concentration of heavy metal ions in sewage, such as lead, mercury, cadmium, etc. Transmit heavy metal concentration data to the central monitoring system in real time for water quality analysis and pollution control.

[0050] Microbial activity sensor: Biosensors are used to monitor the activity level of microorganisms in sewage. Biosensors calculate the activity level of microorganisms by detecting the respiratory activity and metabolites of microorganisms in sewage. The microbial activity data is transmitted to the central monitoring system in real time for water quality analysis and treatment process optimization.

[0051] The water quality analysis module is used to use multispectral imaging technology and electrochemical sensing technology to improve data accuracy through cross-validation, and in combination with a dynamic water quality balance calculation method, to construct a spatiotemporal water quality change map, to conduct in-depth analysis of the data transmitted by the sewage monitoring module, to identify water quality change trends and potential pollution sources, and to generate dynamic water quality reports and treatment suggestions, which are then transmitted to the automated processing module;

[0052] The operation process of the water quality analysis module includes the following steps:

[0053] Collect sewage parameter data through a distributed sensor array for preliminary filtering and calibration;

[0054] Multispectral imaging technology is used to detect the spectral characteristics of various pollutants in sewage, and electrochemical sensing technology is used to determine the ion concentration and electrochemical characteristics in sewage, and the two technologies are combined to perform data cross-validation; the multispectral imaging technology includes imaging of ultraviolet spectrum, visible spectrum and near-infrared spectrum, and spectral analysis is used to identify different chemical components in sewage and their concentration changes; the electrochemical sensing technology includes real-time electrochemical monitoring using a microelectrode array, the microelectrode array includes multiple independent microelectrodes, and each microelectrode is used to detect the electrochemical reaction signal of a specific pollutant.

[0055] It should be noted that in multispectral imaging technology, ultraviolet spectrum is used to detect organic pollutants, ammonia nitrogen, etc. in sewage. Visible spectrum is used to detect suspended matter, turbidity, etc. Near-infrared spectrum is used to detect dissolved organic matter and heavy metal ions in water.

[0056] The spectral image of sewage is obtained by spectral imaging, and the spectral characteristics of different pollutants are extracted by spectral analysis software. The spectral image is analyzed to identify the different chemical components in the sewage and their concentration changes, and the analysis results are transmitted to the data processing unit.

[0057] In electrochemical sensing technology, the array includes multiple independent microelectrodes, each of which is used to detect the electrochemical reaction signals of specific pollutants, such as heavy metal ions, ammonia nitrogen, phosphate, etc. The microelectrode array is used to monitor the ion concentration and electrochemical characteristics in sewage in real time. The electrochemical signals of each ion in sewage, such as potential, current, conductivity, etc., are measured by electrochemical sensors. The electrochemical sensing data is cross-validated with the multispectral imaging data to improve the accuracy of the data.

[0058] Based on real-time data, the dynamic water quality balance calculation method is applied to calculate the spatiotemporal distribution of various water quality parameters and generate a water quality change map;

[0059] Specifically, the construction of the dynamic water quality balance model sets the initial water quality parameter values ​​based on historical data and experimental data. The flow and discharge changes of each pollution source are calculated, and the diffusion and impact range of pollutants are determined in combination with the affected area. The changes in water quality after treatment are calculated in combination with the removal efficiency and treatment volume of each treatment unit. The effects of each pollution source and treatment unit are dynamically simulated, and water quality parameters are updated in real time. A real-time water quality change map is generated to show the temporal and spatial distribution of each parameter.

[0060] Using graph convolutional networks and sparse coding algorithms, we conduct in-depth analysis of the spatiotemporal map of water quality changes, identify long-term and short-term water quality trends, predict future water quality conditions, and use geographic information system technology to trace pollution sources and locate potential pollution sources;

[0061] Specifically, we use graph convolutional networks to analyze the temporal and spatial trends of water quality parameters and identify long-term and short-term change patterns. We use sparse coding algorithms to extract key features and predict future water quality conditions. We use GIS technology to perform spatial analysis of pollution sources and locate potential pollution sources. We use machine learning algorithms to analyze historical data and emission patterns of pollution sources and identify major and minor pollution sources.

[0062] Based on the results of intelligent trend analysis and pollution source tracing data, the differential evolution algorithm is used to generate dynamically adjusted treatment strategies and optimization parameters, and generate a water quality management report including water quality analysis results, treatment suggestions and emergency plans;

[0063] Furthermore, the formula of the dynamic water quality balance calculation method is as follows:

[0064]

[0065] Among them, WQ t represents the water quality parameter value at time t; WQ0 represents the water quality parameter value at the initial time; ΔP i represents the change in pollutant emissions from the ith pollution source; F i represents the flow of the i-th pollution source; A i represents the affected area of ​​the i-th pollution source; R j represents the removal efficiency of the jth treatment unit; E j represents the energy consumption of the jth processing unit; V j represents the processing volume of the jth processing unit; n represents the total number of pollution sources; m represents the total number of processing units.

[0066] It should be noted that the functions of the processing units in the formula are as follows:

[0067] Pollutant removal: The treatment unit removes pollutants from sewage through various treatment processes such as physical, chemical and biological treatment. Its removal efficiency is R j It is a key indicator for evaluating the performance of a processing unit.

[0068] Energy consumption management: Each processing unit consumes a certain amount of energy during operation. j It is an important factor affecting the overall system operating cost. The energy consumption management module improves the system's energy efficiency by evaluating and optimizing energy consumption.

[0069] Processing capacity: the processing volume V of the processing unit j It determines the amount of sewage it can treat in a specific period of time. This directly affects the system's processing capacity and efficiency.

[0070] System optimization: By real-time monitoring and adjusting the parameters of the treatment unit, the system can optimize the operating status, improve the sewage treatment effect, and reduce energy consumption and operating costs.

[0071] Responding to abnormal situations: With the cooperation of the system abnormality detection module, the processing unit can respond to emergencies in a timely manner, adjust the processing strategy, and ensure the stability and reliability of the sewage treatment process.

[0072] The formula of the graph convolutional network is as follows:

[0073]

[0074] in, represents the characteristic value of the jth water quality parameter at the i-th monitoring point in the I+1 layer; A represents the characteristic value of the jth water quality parameter at the ith monitoring point in the ith layer; k，im represents the connection relationship between monitoring point i and monitoring point m; N(i) represents the set of all nodes adjacent to node i; d i and d m represents the degree of node i and node m, that is, the number of monitoring points connected to each; Θ k，j represents the jth weight matrix parameter of the kth convolution kernel; B j Represents the bias term of the jth feature.

[0075] The automated processing module is used to intelligently adjust the processing parameters, including precise control of reagent addition, aeration volume and sedimentation time, based on the dynamic water quality report and processing suggestions provided by the water quality analysis module, using an adaptive control algorithm and a fuzzy logic control strategy;

[0076] The operation process of the automation processing module includes the following steps:

[0077] Receive and analyze the water quality management report transmitted by the water quality analysis module, identify the sewage parameters to be treated and their changing trends, and determine the treatment strategy;

[0078] Apply fractional calculus algorithm to accurately model the sewage treatment process based on fractional differential equations, and flexibly control the treatment process by adjusting fractional parameters, including reagent dosage, aeration volume and sedimentation time;

[0079] Specifically, the specific steps of the fractional order differential algorithm modeling include establishing an initial fractional order model of the sewage treatment process based on experimental data and historical operation data. Through experimental calibration and optimization algorithm, the optimal fractional order parameters are determined to ensure the accuracy of the model. The required dosage of the reagent is calculated using the fractional order model, and the operating parameters of the dosing pump are adjusted in real time to ensure the accuracy and timeliness of the reagent addition. According to the fractional order model, the operating parameters of the aeration system (such as fan speed and aeration time) are dynamically adjusted to optimize the microbial treatment effect. The fractional order model is used to predict the settling velocity and concentration of suspended matter, adjust the operating time and sludge discharge frequency of the sedimentation tank, and ensure the solid-liquid separation effect.

[0080] Use chaos theory control strategies to analyze the nonlinear dynamic behavior of the system and use chaos control technology to optimize the sewage treatment process in real time;

[0081] Specifically, by collecting the system's operating data, the phase space is reconstructed to identify the chaotic characteristics of the system. The Lyapunov exponent of the system is calculated to evaluate the degree of chaos in the system. Chaos synchronization technology is used to make the system's state converge at different time points, thereby controlling the chaotic behavior of the system. By making small disturbances to the system's input parameters, the system's operating state is regulated to avoid falling into the chaotic area. Using chaos control strategies, the dosage of reagents, aeration volume and sedimentation time are dynamically adjusted to optimize the treatment process. The system response is monitored in real time, and the control parameters are adjusted through feedback control loops to ensure stable operation of the system.

[0082] Monitor key process parameters in real time and generate process logs and reports.

[0083] Furthermore, the fractional order differential equation is as follows:

[0084]

[0085] Among them, D α represents the fractional differential operator; α represents the fractional order, ranging from 0 to 1; y(t) represents the treatment variable at time t, including the concentration of a pollutant in the sewage; f(t, y(t)) represents a dynamic function, describing the natural variation law in the sewage treatment process; u i (t) represents the i-th controlled variable, including the dosage of the reagent, the aeration volume and the sedimentation time; n represents the total number of controlled variables.

[0086] The energy consumption management module is used to monitor and optimize the energy consumption in the entire sewage treatment process, identify high-energy consumption links through an energy consumption evaluation algorithm, and take energy-saving measures; the energy consumption evaluation algorithm includes one or more of a Bayesian network algorithm, a self-organizing mapping algorithm, a reinforcement learning algorithm, and a fractal geometry analysis algorithm.

[0087] It should be noted that the energy consumption evaluation algorithm includes one or more of the following:

[0088] Bayesian network algorithm: Based on the historical energy consumption data of the system, a Bayesian network model is constructed to identify the key factors affecting energy consumption. The Bayesian network is used to predict energy consumption and identify potential high energy consumption risks.

[0089] Self-organizing map algorithm: Through the self-organizing map algorithm (SOM), energy consumption data is clustered and analyzed to identify energy consumption patterns and abnormal points. Based on the clustering results, equipment operating parameters are optimized to reduce energy consumption.

[0090] Reinforcement learning algorithm: Through reinforcement learning algorithms (such as Q-learning), the equipment operation strategy is optimized in real time and energy consumption parameters are adjusted dynamically. Based on energy consumption feedback, the control strategy is continuously improved and optimized to achieve energy-saving effects.

[0091] Fractal geometry analysis algorithm: Use fractal geometry to analyze the complexity and change patterns of energy consumption data, identify energy consumption anomalies and fluctuations. Through fractal dimension and fractal spectrum, diagnose the operating status of energy consumption systems and propose energy-saving measures.

[0092] Energy-saving measures are as follows:

[0093] Optimize equipment operation: Adjust operating hours: According to the energy consumption evaluation results, arrange the operation time of high-energy-consuming equipment during the period with lower electricity costs to reduce operating costs. Optimize operation mode: Adjust the equipment operation mode, such as intermittent operation of pumps and on-demand operation of aeration equipment, to reduce energy consumption.

[0094] Improve energy efficiency: Use high-efficiency motors, low-energy aeration equipment and energy-saving pumps to improve overall energy efficiency. Regularly inspect and maintain equipment to ensure efficient operation and reduce energy waste.

[0095] Energy recovery: Install heat exchangers in high-temperature treatment units (such as anaerobic digesters) to recover waste heat for heating other treatment units. Use biogas produced by anaerobic digestion of sludge to generate electricity and reduce dependence on external electricity.

[0096] In one embodiment, the operating parameters of the pump, such as pump speed and flow rate, are dynamically adjusted based on real-time flow and water quality data to avoid excessive operation of the pump and waste of energy. During off-peak hours, the operating speed of the pump is appropriately reduced to reduce energy consumption. A dynamic water quality balance model is used to accurately control aeration time and aeration volume to ensure the effect of microbial treatment while reducing energy consumption. During periods of low oxygen demand, aeration time is reduced to reduce energy consumption. Based on real-time water quality analysis results, the dosage and addition time of the agent are optimized to reduce excessive use of chemical agents and the corresponding energy consumption. An intelligent dosing system is used to accurately control the dosage of the agent to ensure effective use of the agent.

[0097] The anomaly detection module is used to monitor each link of the system in real time, identify and warn of possible abnormal conditions through anomaly detection algorithms, and send multi-level warning information and detailed fault diagnosis reports through the Internet of Things platform.

[0098] It also includes a three-dimensional visualization module, which is used to present the operating status of the system in three dimensions.

[0099] Among them, the three-dimensional visualization module includes a data processing unit, a graphics rendering unit and a display unit; the data processing unit is used to receive and process the data transmitted by each module, including sewage monitoring data, water quality analysis data, automation processing data, energy consumption management data and abnormal detection data; the graphics rendering unit is used to generate a three-dimensional model based on the data provided by the data processing unit to display the dynamic changes in the sewage treatment process, including the temporal and spatial distribution of sewage parameters, water quality change trends, energy consumption and abnormal conditions; the display unit is used to display the generated three-dimensional model to the operator in real time to facilitate monitoring and decision-making.

[0100] It should be noted that the data processing unit: receives data from the sewage monitoring module, water quality analysis module, automation processing module, energy consumption management module and anomaly detection module. Cleans the received data to remove noise and outliers to ensure the accuracy and consistency of the data. Integrates data from different modules to form a unified database for subsequent analysis and processing. Converts data into a format that can be recognized by the visualization system, including time series data, spatial distribution data, etc.

[0101] Specifically, sewage monitoring data include parameters such as flow rate, pH value, turbidity, temperature, chemical oxygen demand, biological oxygen demand, heavy metal concentration and microbial activity.

[0102] Water quality analysis data includes multispectral imaging results, electrochemical sensing data, dynamic water quality balance calculation results, etc.

[0103] The automated processing data includes processing parameters such as dosage of reagents, aeration volume, and sedimentation time.

[0104] Energy consumption management data includes equipment energy consumption data, energy consumption assessment results, energy-saving measures, etc.

[0105] Anomaly detection data includes real-time monitoring data, anomaly detection results, warning information, and fault diagnosis reports.

[0106] Graphics rendering unit: Build a three-dimensional spatial model of the sewage treatment system, including tunnel structure, sewage pipes, treatment equipment, monitoring points, etc. Based on real-time data, dynamically generate a change model in the sewage treatment process, including sewage flow, water quality parameter changes, equipment operation status, etc. Map sewage monitoring data and water quality analysis data into the three-dimensional spatial model to display the temporal and spatial distribution of sewage parameters. Through animation effects, display the changing trends of water quality parameters, energy consumption data and equipment status, and intuitively reflect the operation of the system. Use high-performance graphics rendering technology to achieve real-time rendering to ensure smooth display of the three-dimensional model. Generate high-resolution three-dimensional images to ensure clear details for easy observation and analysis by operators. Display real-time changes in sewage parameters such as flow, pH value, turbidity, etc. through visual elements such as color, shape and animation. Use dynamic charts and animations to display the changing trends and distribution of water quality parameters. Display the energy consumption and optimization results of each device through energy consumption heat maps and dynamic curves. Display abnormal detection results and early warning information in real time, and prompt operators of potential problems through color changes and warning icons. Users can freely scale and rotate the three-dimensional model to observe different angles and details. By clicking or hovering, you can query detailed information of specific data points, such as water quality parameters at a certain location, operating status of a certain device, etc. Users can play back historical data through timeline control to observe past operating conditions and changing trends.

[0107] Display unit: Displays a 3D model of the entire sewage treatment system, including tunnel structures, treatment equipment, and monitoring points. Displays real-time data and key parameters, such as flow, water quality parameters, energy consumption data, etc. Provides interactive tools such as zoom, rotation, timeline control, data query, etc. The interface updates monitoring data and analysis results in real time to ensure that operators are always up to date. Displays warning information of abnormal detection on the interface, and prompts operators of potential problems through color changes and warning icons. Provides a one-click report generation function to facilitate operators to export key data and analysis results as reports. Operators can monitor the operation of the sewage treatment system in real time through a 3D visualization interface, and promptly discover and handle abnormal conditions. Using the visualization interface, operators can intuitively analyze water quality changes, energy consumption, and treatment effects, and formulate optimization strategies. Through 3D visualization, management can fully understand the system operation status and provide data support and visualization analysis results for decision-making.

[0108] In summary, the present invention uses a distributed sensor array to monitor the flow rate, pH value, turbidity, temperature, chemical oxygen demand, biological oxygen demand, heavy metal concentration and microbial activity of sewage in real time, ensuring the comprehensiveness and real-time nature of the data. The automated processing module can adaptively adjust the processing parameters based on dynamic water quality reports and processing suggestions to ensure the efficiency and accuracy of the processing process.

[0109] The present invention uses multispectral imaging technology and electrochemical sensing technology to perform data cross-validation, thereby improving the accuracy of water quality analysis data. Through the spatiotemporal water quality change map, it is possible to identify water quality change trends and potential pollution sources, generate dynamic water quality reports and treatment suggestions, and enhance the system's analytical capabilities. Based on real-time data, a dynamic water quality balance model is constructed, which can generate a water quality change map and display the spatiotemporal distribution of water quality parameters. Using graph convolutional networks and sparse coding algorithms, in-depth analysis of water quality change trends is performed, future water quality conditions are predicted, and accurate pollution source tracing analysis is provided.

[0110] The automated processing module in the present invention adopts adaptive control algorithms and fuzzy logic control strategies to intelligently adjust processing parameters, optimize processing effects, and reduce energy consumption. The energy consumption management module identifies high-energy consumption links through energy consumption evaluation algorithms, takes energy-saving measures, and improves the energy efficiency of the system. The anomaly detection module monitors each link of the system in real time, identifies and warns of possible abnormal conditions through anomaly detection algorithms, and sends warning information and detailed fault diagnosis reports in a timely manner to ensure the stable operation of the system.

[0111] The three-dimensional visualization module in the present invention can present the operating status of the system in three dimensions, showing the dynamic changes in the sewage treatment process, including the temporal and spatial distribution of sewage parameters, water quality change trends, energy consumption and abnormal conditions. It provides an interactive monitoring interface, so that operators can intuitively observe and analyze the system operation, discover and handle abnormal conditions in a timely manner, and improve management efficiency.

[0112] The above implementation modes are merely descriptions of the preferred implementation modes of the present invention, and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary engineering and technical personnel in the field shall fall within the protection scope determined by the claims of the present invention.

Claims

1. An intelligent sewage treatment system for extra-long tunnels, characterized in that: It includes a sewage monitoring module, a water quality analysis module, an automatic processing module, an energy consumption management module and an abnormality detection module which are sequentially connected in communication; The sewage monitoring module is used to monitor sewage parameters in the tunnel in real time through a distributed sensor array and transmit the monitoring data to the water quality analysis module; the sewage parameters include flow, pH value, turbidity, temperature, chemical oxygen demand, biological oxygen demand, heavy metal concentration and microbial activity; The water quality analysis module is used to use multispectral imaging technology and electrochemical sensing technology to improve data accuracy through cross-validation, and in combination with a dynamic water quality balance calculation method, to construct a spatiotemporal water quality change map, to conduct in-depth analysis of the data transmitted by the sewage monitoring module, to identify water quality change trends and potential pollution sources, and to generate dynamic water quality reports and treatment suggestions, which are then transmitted to the automated processing module; The automated processing module is used to intelligently adjust the processing parameters, including precise control of reagent addition, aeration volume and sedimentation time, based on the dynamic water quality report and processing suggestions provided by the water quality analysis module, using an adaptive control algorithm and a fuzzy logic control strategy; The energy consumption management module is used to monitor and optimize the energy consumption of the entire sewage treatment process, identify high energy consumption links through energy consumption evaluation algorithms, and take energy-saving measures; The anomaly detection module is used to monitor each link of the system in real time, identify and warn of possible abnormal conditions through anomaly detection algorithms, and send multi-level warning information and detailed fault diagnosis reports through the Internet of Things platform.

2. The intelligent sewage treatment system for extra-long tunnels according to claim 1 is characterized in that: The operation process of the water quality analysis module includes the following steps: Collect sewage parameter data through a distributed sensor array for preliminary filtering and calibration; Use multispectral imaging technology to detect the spectral characteristics of various pollutants in sewage, and use electrochemical sensing technology to determine the ion concentration and electrochemical characteristics of sewage, and combine the two technologies for data cross-validation; Based on real-time data, the dynamic water quality balance calculation method is applied to calculate the spatiotemporal distribution of various water quality parameters and generate a water quality change map; Using graph convolutional networks and sparse coding algorithms, we conduct in-depth analysis of the spatiotemporal map of water quality changes, identify long-term and short-term water quality trends, predict future water quality conditions, and use geographic information system technology to trace pollution sources and locate potential pollution sources; Based on the results of intelligent trend analysis and pollution source tracing data, the differential evolution algorithm is used to generate dynamically adjusted treatment strategies and optimization parameters, and generate a water quality management report including water quality analysis results, treatment recommendations and emergency plans.

3. The intelligent sewage treatment system for extra-long tunnels according to claim 2 is characterized in that: The formula for the dynamic water quality balance calculation method is as follows: Among them, WQ t represents the water quality parameter value at time t; WQ0 represents the water quality parameter value at the initial time; ΔP i represents the change in pollutant emissions from the ith pollution source; F i represents the flow of the i-th pollution source; A i represents the affected area of ​​the i-th pollution source; R j represents the removal efficiency of the jth treatment unit; E j represents the energy consumption of the jth processing unit; V j represents the processing volume of the jth processing unit; n represents the total number of pollution sources; m represents the total number of processing units.

4. The intelligent sewage treatment system for extra-long tunnels according to claim 2 is characterized in that: The formula of the graph convolutional network is as follows: in, represents the characteristic value of the jth water quality parameter at the i-th monitoring point in the l+1th layer; represents the characteristic value of the jth water quality parameter at the ith monitoring point in the lth layer; represents the connection relationship between monitoring point i and monitoring point m; N(i) represents the set of all nodes adjacent to node i; d i and d m represents the degree of node i and node m, that is, the number of monitoring points connected to each; Θ k，j represents the jth weight matrix parameter of the kth convolution kernel; B j Represents the bias term of the jth feature.

5. The intelligent sewage treatment system for extra-long tunnels according to claim 2 is characterized in that: The multispectral imaging technology in the water quality analysis module includes imaging of ultraviolet spectrum, visible spectrum and near-infrared spectrum, and identifies different chemical components in sewage and their concentration changes through spectral analysis; the electrochemical sensing technology includes real-time electrochemical monitoring using a microelectrode array, and the microelectrode array includes multiple independent microelectrodes, each microelectrode is used to detect the electrochemical reaction signal of a specific pollutant.

6. The intelligent sewage treatment system for extra-long tunnels according to claim 1 is characterized in that: The operation process of the automated processing module includes the following steps: Receive and analyze the water quality management report transmitted by the water quality analysis module, identify the sewage parameters to be treated and their changing trends, and determine the treatment strategy; Apply fractional calculus algorithm to accurately model the sewage treatment process based on fractional differential equations, and flexibly control the treatment process by adjusting fractional parameters, including reagent dosage, aeration volume and sedimentation time; Use chaos theory control strategies to analyze the nonlinear dynamic behavior of the system and use chaos control technology to optimize the sewage treatment process in real time; Monitor key process parameters in real time and generate process logs and reports.

7. The ultra-long tunnel sewage intelligent treatment system according to claim 6 is characterized in that: The fractional differential equation is as follows: Among them, D α represents the fractional differential operator; α represents the fractional order, ranging from 0 to 1; y(t) represents the treatment variable at time t, including the concentration of a pollutant in the sewage; f(t, y(t)) represents a dynamic function, describing the natural variation law in the sewage treatment process; u i (t) represents the i-th controlled variable, including the dosage of the reagent, the aeration volume and the sedimentation time; n represents the total number of controlled variables.

8. The intelligent sewage treatment system for extra-long tunnels according to claim 1 is characterized in that: The energy consumption evaluation algorithm includes one or more of a Bayesian network algorithm, a self-organizing mapping algorithm, a reinforcement learning algorithm, and a fractal geometry analysis algorithm.

9. The intelligent sewage treatment system for extra-long tunnels according to claim 1 is characterized in that: It also includes a three-dimensional visualization module, which is used to present the operating status of the system in three dimensions.

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