An intelligent sewage treatment system for extra-long tunnels

Abstract

The present invention relates to the technical field of tunnel sewage treatment, and particularly refers to an intelligent sewage treatment system for extra-long tunnels. It includes a sewage monitoring module, a water quality analysis module, an automated treatment 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. The water quality analysis module uses multi-spectral imaging and electro-chemical sensing technologies to construct a water quality map through dynamic water quality balance calculations, generating reports and suggestions. The automated treatment module adjusts treatment parameters according to the reports. The energy consumption management module monitors and optimizes energy consumption, identifies high-energy-consuming links through algorithms, and takes energy-saving measures. The anomaly detection module monitors the system in real time, identifies and warns of abnormal conditions, and sends warning messages and fault reports. The present invention realizes efficient, precise, and intelligent treatment of sewage in extra-long tunnels, not only significantly improving the sewage treatment efficiency, but also significantly reducing energy consumption and operating costs.

Description

Technical Field

[0001] The present invention relates to the technical field of tunnel sewage treatment, and particularly to an intelligent sewage treatment system for extra-long tunnels. Background Art

[0002] During the construction and operation of extra-long tunnels, a large amount of sewage will inevitably be generated. If this sewage is not effectively treated and directly discharged, it 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 in extra-long tunnels has become one of the important topics in urban infrastructure maintenance and environmental protection. There are still the following problems: existing technologies rely mostly on single sensing technologies for water quality analysis, with low data accuracy, making it difficult to effectively identify water quality change trends and potential pollution sources, resulting in insufficient basis for treatment decisions and unsatisfactory treatment effects; the adjustment of process parameters in existing systems is mostly fixed values or manual adjustment, lacking intelligent adjustment means, making it difficult to dynamically optimize the treatment process according to real-time water quality changes, resulting in low treatment efficiency and serious waste of chemicals; existing systems lack comprehensive monitoring and optimization of energy consumption, unable to identify and solve high-energy-consuming links, resulting in high system operation 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, which automatically adjusts the treatment process according to real-time data through tunnel sewage monitoring, analysis and automated treatment, optimizes the system operation, significantly improves the sewage treatment efficiency, not only improves the treatment effect, but also significantly reduces the energy consumption and operation costs.

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

[0005] An intelligent sewage treatment system for extra-long tunnels, comprising a sewage monitoring module, a water quality analysis module, an automated treatment module, an energy consumption management module and an anomaly detection module that are communicatively connected in sequence;

[0006] The sewage monitoring module is used to monitor the 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 rate, 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 multi-spectral imaging technology and electro-chemical sensing technology to improve data accuracy through cross-validation, and combine a dynamic water quality balance calculation method to construct a spatio-temporal water quality change map, deeply analyze the data transmitted by the sewage monitoring module, identify water quality change trends and potential pollution sources, and generate a dynamic water quality report and treatment suggestions, which are transmitted to the automated treatment module;

[0008] The automated processing module is used to intelligently adjust the processing process parameters according to the dynamic water quality report and treatment suggestions provided by the water quality analysis module, adopting an adaptive control algorithm and a fuzzy logic control strategy, including precisely controlling the chemical dosing, aeration volume, and sedimentation time;

[0009] The energy consumption management module is used to monitor and optimize the energy consumption situation in the entire sewage treatment process, identify high-energy-consuming links through an energy consumption assessment algorithm, 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 an anomaly detection algorithm, and send multi-level warning information and a detailed fault diagnosis report 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 multi-spectral imaging technology to detect the spectral characteristics of various pollutants in the sewage, use electrochemical sensing technology to measure the ion concentration and electrochemical characteristics in the sewage, and combine the two technologies for data cross-validation;

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

[0015] Use a graph convolutional network and a sparse coding algorithm to deeply analyze the spatio-temporal map of water quality changes, identify long-term and short-term water quality change trends, predict future water quality conditions, and conduct pollution source tracing analysis in combination with geographic information system technology to locate potential pollution sources;

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

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

[0018]

[0019] Among them, 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 the pollutant emission of the i-th pollution source; F i represents the flow rate of the i-th pollution source; A i represents the affected area of the i-th pollution source; R jDenote the removal efficiency of the j-th processing unit; E j Denote the energy consumption of the j-th processing unit; V j Denote the processing volume of the j-th 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] Where, Denote the eigenvalue of the j-th water quality parameter at the i-th monitoring point in the (I + 1)-th layer; Denote the eigenvalue of the j-th water quality parameter at the i-th monitoring point in the I-th layer; A k，im Denote 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 Denote the degrees of node i and node m, that is, the number of monitoring points they are respectively connected to; Θ k，j Denote the j-th weight matrix parameter of the k-th convolutional kernel; B j Denote the bias term of the j-th feature.

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

[0024] Further, the operation process of the automatic 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 processed and their change trends, and determine the treatment strategy;

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

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

[0028] Real-time monitor the key parameters in the treatment process, and generate treatment logs and reports.

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

[0030]

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

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

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

[0034] The beneficial effects of the present invention are as follows: The sewage monitoring module of the present invention can monitor various sewage parameters in real time through a distributed sensor array, ensuring the timeliness and comprehensiveness of data, and providing an accurate monitoring basis. The water quality analysis module uses multi-spectral imaging technology and electrochemical sensing technology, and through a cross-validation method, improves the accuracy of data analysis. At the same time, the construction of the dynamic water quality balance calculation method and the spatio-temporal water quality change map helps to identify the water quality change trend and potential pollution sources, and provides a detailed dynamic water quality report and treatment suggestions. The automatic processing module, according to the dynamic water quality report and treatment suggestions generated by the water quality analysis module, adopts an adaptive control algorithm and a fuzzy logic control strategy to intelligently adjust the processing process parameters, realizing the precise control of chemical dosage, aeration volume, and sedimentation time, reducing the waste of chemicals and energy consumption while improving the sewage treatment effect. The energy consumption management module monitors and optimizes the energy consumption situation in the sewage treatment process, identifies high-energy-consuming links through the energy consumption assessment algorithm, and takes corresponding energy-saving measures, effectively reducing the system operation cost and improving the energy efficiency. The anomaly detection module monitors each link of the system in real time, uses the anomaly detection algorithm to identify and warn 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 is a schematic diagram of the modules of a special long tunnel sewage intelligent treatment system of the present invention.

[0036] Figure 2 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 schematic flow diagram of the operation process of the automated processing module in the present invention. Detailed implementation manners

[0038] Please refer to Figures 1-3 As shown, the present invention relates to an intelligent sewage treatment system for extra-long tunnels.

[0039] Embodiment

[0040] An intelligent sewage treatment system for extra-long tunnels includes a sewage monitoring module, a water quality analysis module, an automated processing module, an energy consumption management module, and an anomaly detection module that are communicatively connected in sequence;

[0041] The sewage monitoring module is used to monitor the 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 rate, 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 specifically as follows:

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

[0044] pH value sensor: pH value sensors are installed at different positions along the tunnel, including the tunnel entrance, middle section, key discharge ports, and exit. The sensor is installed inside the sewage pipeline through a fixed bracket to ensure that the electrode can be completely 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. It monitors the change of pH value in real time and transmits the data to the central monitoring system for water quality analysis and treatment decision-making.

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

[0046] Temperature sensor: Thermocouple temperature sensors are installed distributively to monitor the temperature change of sewage in real time. The thermocouple sensors generate corresponding electrical signals by measuring the temperature change of sewage and convert them into temperature data. They monitor the temperature change in real time and transmit the data to the central monitoring system for water quality analysis and treatment regulation.

[0047] Chemical Oxygen Demand (COD) sensor: An electrochemical sensor is used to measure the concentration of organic matter in sewage. The sensor is coated with anti-corrosion materials to ensure long-term stable operation. It measures the concentration of organic matter in sewage through electrochemical reactions and calculates the Chemical Oxygen Demand (COD) value. It transmits the COD data to the central monitoring system in real time for water quality analysis and treatment optimization.

[0048] Biochemical Oxygen Demand (BOD) sensor: Through a bio-sensor array, it determines the amount of oxygen required for microorganisms in sewage to decompose organic matter. The sensor detects the amount of oxygen required for the decomposition of organic matter in sewage through microbial sensors and calculates the Biochemical Oxygen Demand (BOD) value. It transmits the BOD data to the central monitoring system in real time for water quality monitoring and treatment decision-making.

[0049] Heavy metal concentration sensor: Using electrochemical sensors and spectroscopic techniques, it detects the concentration of heavy metal ions in sewage. It uses electrochemical sensors and spectroscopic techniques to detect the concentration of heavy metal ions in sewage, such as lead, mercury, cadmium, etc. It transmits the heavy metal concentration data to the central monitoring system in real time for water quality analysis and pollution control.

[0050] Microbial activity sensor: A bio-sensor is used to monitor the activity level of microorganisms in sewage. The bio-sensor calculates the microbial activity level by detecting the respiratory activity and metabolites of microorganisms in sewage. It transmits the microbial activity data to the central monitoring system in real time for water quality analysis and treatment process optimization.

[0051] The water quality analysis module uses multi-spectral imaging technology and electrochemical sensing technology to improve data accuracy through cross-validation, and combines dynamic water quality balance calculation methods to construct a spatio-temporal water quality change map, deeply analyze the data transmitted by the sewage monitoring module, identify water quality change trends and potential pollution sources, and generate a dynamic water quality report and treatment suggestions, which are transmitted to the automated treatment module;

[0052] Among them, 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] The spectral characteristics of various pollutants in sewage are detected using multispectral imaging technology, and the ion concentration and electrochemical characteristics in sewage are determined using electrochemical sensing technology. The two technologies are combined for data cross-verification. The multispectral imaging technology includes imaging of ultraviolet spectrum, visible spectrum, and near-infrared spectrum, and different chemical components and their concentration changes in sewage are identified 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 of which is used to detect the electrochemical reaction signal of a specific pollutant.

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

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

[0057] In the electrochemical sensing technology, the array includes multiple independent microelectrodes, each of which is used to detect the electrochemical reaction signal of a specific pollutant, such as heavy metal ions, ammonia nitrogen, phosphate, etc. The ion concentration and electrochemical characteristics in sewage are monitored in real time using the microelectrode array. The electrochemical signals of various ions in sewage, such as potential, current, conductance, etc., are measured through an electrochemical sensor. The electrochemical sensing data and the multispectral imaging data are cross-verified to improve the accuracy of the data.

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

[0059] Specifically, the dynamic water quality balance model is constructed according to historical data and experimental data, and the initial water quality parameter values are set. The flow rate and emission changes of each pollution source are calculated, and combined with the affected area, the diffusion and influence range of pollutants are determined. Combined with the removal efficiency and treatment volume of each treatment unit, the water quality change after treatment is calculated. The effects of each pollution source and treatment unit are dynamically simulated, and the water quality parameters are updated in real time. A real-time water quality change map is generated to show the spatio-temporal distribution of each parameter.

[0060] Using a graph convolutional network and a sparse coding algorithm, the spatio-temporal map of water quality changes is deeply analyzed to identify long-term and short-term water quality change trends, predict future water quality conditions, and combine geographic information system technology for pollution source tracing analysis to locate potential pollution sources.

[0061] Specifically, a graph convolutional network is used to analyze the spatio-temporal variation trends of water quality parameters and identify long-term and short-term change patterns. Through a sparse coding algorithm, key features are extracted to predict the future water quality conditions. Combining with GIS technology, spatial analysis of pollution sources is carried out to locate potential pollution sources. Machine learning algorithms are used to analyze the historical data and emission patterns of pollution sources to identify major and minor pollution sources.

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

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

[0064]

[0065] where 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 of the i-th pollution source; F i represents the flow rate 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 j-th treatment unit; E j represents the energy consumption of the j-th treatment unit; V j represents the treatment volume of the j-th treatment unit; n represents the total number of pollution sources; m represents the total number of treatment units.

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

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

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

[0069] Treatment capacity: The treatment volume V of the treatment unit j determines the amount of sewage that can be treated within a specific time period. This directly affects the treatment capacity and efficiency of the system.

[0070] System Optimization: By monitoring and adjusting the parameters of the processing unit in real time, the system can optimize its operating state, improve the sewage treatment effect, and reduce energy consumption and operating costs simultaneously.

[0071] Response to Abnormal Situations: With the cooperation of the system anomaly detection module, the processing unit can respond promptly to emergencies, 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] Where, represents the eigenvalue of the jth water quality parameter at the ith monitoring point in the (I + 1)th layer; represents the eigenvalue of the jth water quality parameter at the ith monitoring point in the Ith layer; A k，im represents the connection relationship between the ith monitoring point and the mth monitoring point; N(i) represents the set of all nodes adjacent to node i; d i and d m represent the degrees of node i and node m, that is, the number of monitoring points they are connected to respectively; Θ k，j represents the jth weight matrix parameter of the kth convolutional kernel; B j represents the bias term of the jth feature.

[0075] The automated processing module is used to intelligently adjust the processing process parameters according to the dynamic water quality report and treatment suggestions provided by the water quality analysis module, adopting an adaptive control algorithm and a fuzzy logic control strategy, including precisely controlling the chemical dosing, aeration volume, and sedimentation time;

[0076] Among them, the operation process of the automated processing module includes the following steps:

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

[0078] Apply the fractional calculus algorithm, accurately model the sewage treatment process based on the fractional differential equation, and flexibly control the processing process by adjusting the fractional parameters, including the chemical dosing amount, aeration volume, and sedimentation time;

[0079] Specifically, the specific steps of 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 algorithms, the optimal fractional-order parameters are determined to ensure the accuracy of the model. The fractional-order model is used to calculate the required chemical dosage, and the operation parameters of the dosing pump are adjusted in real time to ensure the accuracy and timeliness of chemical dosing. According to the fractional-order model, the operation parameters of the aeration system (such as fan speed, aeration time) are dynamically adjusted to optimize the microbial treatment effect. The fractional-order model is used to predict the sedimentation velocity and concentration of suspended solids, and the operation time and sludge discharge frequency of the sedimentation tank are adjusted to ensure the solid-liquid separation effect.

[0080] Using the chaos theory control strategy, 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 operation data of the system, perform phase space reconstruction to identify the chaotic characteristics of the system. Calculate the Lyapunov exponent of the system to evaluate the degree of chaos of the system. Use chaos synchronization technology to make the states of the system at different time points converge, thereby controlling the chaotic behavior of the system. By making small perturbations to the input parameters of the system, regulate the operation state of the system to avoid falling into the chaotic region. Use the chaos control strategy to dynamically adjust the chemical dosage, aeration volume and sedimentation time to optimize the treatment process. Monitor the system response in real time, and adjust the control parameters through the feedback control loop to ensure the stable operation of the system.

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

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

[0084]

[0085] where D α represents the fractional-order 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 certain pollutant in the sewage; f(t, y(t)) represents the dynamic function, describing the natural change law in the sewage treatment process; u i (t) represents the i-th control variable, including chemical dosage, aeration volume and sedimentation time; n represents the total number of control variables.

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

[0087] It should be noted that the energy consumption assessment 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 for energy consumption prediction to identify potential high-energy consumption risks.

[0089] Self-organizing mapping algorithm: Through the self-organizing mapping algorithm (SOM), cluster analysis is performed on the energy consumption data to identify energy consumption patterns and outliers. According to the clustering results, the operating parameters of the equipment are optimized to reduce energy consumption.

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

[0091] Fractal geometry analysis algorithm: The complexity and variation law of the energy consumption data are analyzed by using fractal geometry to identify energy consumption anomalies and fluctuations. Through the fractal dimension and fractal spectrum, the operating state of the energy consumption system is diagnosed, and energy-saving measures are proposed.

[0092] The energy-saving measures are as follows:

[0093] Optimizing equipment operation: Adjusting the operation time: According to the energy consumption assessment results, arrange the operation time of high-energy consumption equipment during the period with lower electricity charges to reduce the operation cost. Optimizing the operation mode: Adjust the operation mode of the equipment, such as the intermittent operation of pumps and the on-demand operation of aeration equipment, to reduce energy consumption.

[0094] Improving energy efficiency: Using high-efficiency motors, low-energy consumption aeration equipment, and energy-saving pumps to improve the overall energy efficiency. Regularly detect and maintain the equipment to ensure its efficient operation and reduce energy consumption 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 the biogas generated by sludge anaerobic digestion for power generation to reduce the dependence on external electricity.

[0096] In one embodiment, according to the real-time flow rate and water quality data, the operating parameters of the pump, such as the pump speed and flow rate, are dynamically adjusted to avoid over-operation and energy consumption waste of the pump. During off-peak hours, appropriately reduce the operating speed of the pump to reduce energy consumption. Use a dynamic water quality balance model to accurately control the aeration time and aeration volume to ensure the microbial treatment effect while reducing energy consumption. During low-oxygen demand periods, reduce the aeration time to reduce energy consumption. According to the real-time water quality analysis results, optimize the chemical dosing amount and dosing time to reduce the overuse of chemical agents and the corresponding energy consumption. Use an intelligent dosing system to accurately control the chemical dosing amount to ensure the effective use of chemical agents.

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

[0098] It also includes a 3D visualization module, which is used to present the operating state of the system in a three-dimensional stereoscopic manner.

[0099] Among them, the 3D 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, automated processing data, energy consumption management data, and abnormal detection data; the graphics rendering unit is used to generate a three-dimensional stereoscopic model based on the data provided by the data processing unit, showing the dynamic changes in the sewage treatment process, including the spatio-temporal distribution of sewage parameters, water quality change trends, energy consumption conditions, and abnormal conditions; the display unit is used to display the generated three-dimensional stereoscopic model to the operator in real time for easy 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, automated processing module, energy consumption management module, and abnormal detection module. Cleans the received data to remove noise and outliers to ensure the accuracy and consistency of the data. Integrates the data from different modules to form a unified database for subsequent analysis and processing. Converts the data into a format recognizable by the visualization system, including time series data, spatial distribution data, etc.

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

[0102] The 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 chemical agent dosage, aeration volume, and sedimentation time.

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

[0105] The abnormal detection data includes real-time monitoring data, abnormal detection results, warning information, and fault diagnosis reports, etc.

[0106] Graphics Rendering Unit: Build a 3D spatial model of the sewage treatment system, including tunnel structures, sewage pipes, treatment equipment, monitoring points, etc. According to real-time data, dynamically generate a change model during the sewage treatment process, including sewage flow, water quality parameter changes, equipment operation status, etc. Map sewage monitoring data and water quality analysis data onto the 3D spatial model to display the spatio-temporal distribution of sewage parameters. Through animation effects, display the changing trends of water quality parameters, energy consumption data, and equipment status, intuitively reflecting the operation of the system. Utilize high-performance graphics rendering technology to achieve real-time rendering and ensure smooth display of the 3D model. Generate high-resolution 3D images to ensure clear details for easy observation and analysis by operators. Through visual elements such as colors, shapes, and animations, display the real-time changes of sewage parameters, such as flow rate, pH value, turbidity, etc. Use dynamic charts and animations to display the changing trends and distribution of water quality parameters. Through energy consumption heat maps and dynamic curves, display the energy consumption of each device and the optimization results. Real-time display of abnormal detection results and warning information, through color changes and warning icons, to prompt operators of potential problems. Users can freely zoom in and out and rotate the 3D model to observe different angles and details. By clicking or hovering, query the detailed information of specific data points, such as water quality parameters at a certain location, the operation status of a certain device, etc. Users can control through the timeline to playback historical data and observe past operation conditions and changing trends.

[0107] Display Unit: Display the 3D model of the entire sewage treatment system, including tunnel structures, treatment equipment, and monitoring points. Display real-time data and key parameters, such as flow rate, water quality parameters, energy consumption data, etc. Provide interactive tools, such as zooming, rotating, timeline control, data query, etc. The interface updates monitoring data and analysis results in real-time to ensure that operators can always keep abreast of the latest situation. Display warning information of abnormal detection on the interface, through color changes and warning icons, to prompt operators of potential problems. Provide a function to generate reports with one click, facilitating operators to export key data and analysis results as reports. Operators can monitor the operation of the sewage treatment system in real-time through the 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 comprehensively understand the system operation status and provide data support and visualization analysis results for decision-making.

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

[0109] The present invention uses multi - spectral imaging technology and electrochemical sensing technology for data cross - verification, improving the accuracy of water quality analysis data. It can identify water quality change trends and potential pollution sources through spatio - temporal water quality change maps, generate dynamic water quality reports and treatment suggestions, enhancing the analysis ability of the system. Based on real - time data, a dynamic water quality balance model is constructed, which can generate water quality change maps to display the spatio - temporal distribution of water quality parameters. Using graph convolutional networks and sparse coding algorithms, it deeply analyzes water quality change trends, predicts future water quality conditions, and provides accurate pollution source tracing analysis.

[0110] In the present invention, the automatic processing module adopts an adaptive control algorithm and a fuzzy logic control strategy to intelligently adjust the processing process parameters, optimize the processing effect, and reduce energy consumption. The energy consumption management module identifies high - energy - consumption links through an energy consumption assessment algorithm, takes energy - saving measures, and improves the energy efficiency ratio of the system. The anomaly detection module monitors each link of the system in real - time, identifies and warns of possible abnormal conditions through an anomaly detection algorithm, and timely sends warning messages and detailed fault diagnosis reports to ensure the stable operation of the system.

[0111] In the present invention, the 3D visualization module can present the operating state of the system in a three - dimensional form, showing the dynamic changes during the sewage treatment process, including the spatio - temporal distribution of sewage parameters, water quality change trends, energy consumption conditions, and abnormal conditions. It provides an interactive monitoring interface, enabling operators to intuitively observe and analyze the system operation, timely discover and handle abnormal conditions, and improve management efficiency.

[0112] The above - mentioned embodiments are only descriptions of the preferred embodiments of the present invention, and do not limit the scope of the present invention. Without departing from the design spirit of the present invention, various deformations and improvements made by those of ordinary engineering and technical personnel in the art to the technical solutions of the present invention 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 automated treatment module, an energy consumption management module, and an anomaly detection module that are communicatively connected in sequence; The sewage monitoring module is used to monitor the 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 rate, pH value, turbidity, temperature, chemical oxygen demand, biological oxygen demand, heavy metal concentration, and microbial activity; The water quality analysis module is used to utilize multi-spectral imaging technology and electrochemical sensing technology to improve data accuracy through cross-validation, and combine dynamic water quality balance calculation methods to construct a spatio-temporal water quality change map, deeply analyze the data transmitted by the sewage monitoring module, identify water quality change trends and potential pollution sources, and generate a dynamic water quality report and treatment suggestions, which are transmitted to the automated treatment module; The automated treatment module is used to, according to the dynamic water quality report and treatment suggestions provided by the water quality analysis module, adopt an adaptive control algorithm and a fuzzy logic control strategy to intelligently adjust the treatment process parameters, including precisely controlling chemical dosing, aeration volume, and sedimentation time; The energy consumption management module is used to monitor and optimize the energy consumption situation in the entire sewage treatment process, identify high-energy-consuming links through an energy consumption assessment algorithm, 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 an anomaly detection algorithm, and send multi-level warning information and a detailed fault diagnosis report through the Internet of Things platform.

2. The intelligent sewage treatment system for extra-long tunnels according to claim 1, wherein The operation process of the water quality analysis module includes the following steps: Collect sewage parameter data through a distributed sensor array and perform preliminary filtering and calibration; Use multi-spectral imaging technology to detect the spectral characteristics of various pollutants in the sewage, and use electrochemical sensing technology to measure the ion concentration and electrochemical characteristics in the sewage, and combine the two technologies for data cross-validation; Based on real-time data, apply the dynamic water quality balance calculation method to calculate the spatio-temporal distribution of various water quality parameters and generate a water quality change map; Use a graph convolutional network and a sparse coding algorithm to deeply analyze the spatio-temporal water quality change map, identify long-term and short-term water quality change trends, predict future water quality conditions, and conduct pollution source tracing analysis in combination with geographic information system technology to locate potential pollution sources; Based on the intelligent trend analysis results and pollution source tracing data, adopt a differential evolution algorithm 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.

3. The intelligent sewage treatment system for extra-long tunnels according to claim 2, wherein, The formula of 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 of the i-th pollution source; F i represents the flow rate 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 j-th treatment unit; E j represents the energy consumption of the j-th treatment unit; V j represents the treatment volume of the j-th treatment unit; n represents the total number of pollution sources; m represents the total number of treatment units.

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

5. The intelligent sewage treatment system for extra-long tunnels according to claim 2, wherein The multi-spectral imaging technology in the water quality analysis module includes imaging of ultraviolet spectrum, visible spectrum, and near-infrared spectrum, and different chemical components and their concentration changes in the sewage are identified through spectral analysis; the electrochemical sensing technology includes real-time electrochemical monitoring using a microelectrode array, and the microelectrode array includes multiple independent microelectrodes, and 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, characterized in that, The operation process of the automated treatment 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 processed and their changing trends, and determine the treatment strategy; Apply the fractional calculus algorithm, accurately model the sewage treatment process based on the fractional differential equation, and flexibly control the treatment process by adjusting the fractional parameters, including the chemical dosage, aeration volume, and sedimentation time; Use the chaos theory control strategy to analyze the nonlinear dynamic behavior of the system, and use chaos control technology to optimize the sewage treatment process in real time; Real-time monitor the key parameters during the treatment process, and generate treatment logs and reports.

7. An intelligent sewage treatment system for extra-long tunnels according to claim 6, characterized in that, The fractional differential equation is as follows: Among them, D α represents a fractional-order 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 certain pollutant in sewage; f(t, y(t)) represents a dynamic function that describes the natural change law in the sewage treatment process; u i (t) represents the i-th control variable, including chemical dosage, aeration rate, and sedimentation time; n represents the total number of control variables.

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

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

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