Urban sewage intelligent monitoring and resourceful treatment system and method

Abstract

The application discloses an intelligent monitoring and resource treatment system and method for municipal sewage, and particularly relates to the technical field of municipal sewage treatment, which comprises an intelligent sewage collection sensing and sensing module for collecting sewage data in real time to form an intelligent sensing network, and an intelligent regulation and control treatment optimization module for intelligently adjusting treatment process parameters according to the collected sewage data. The application optimizes flow prediction and process response through Kalman filtering and PID control algorithm, detects abnormal discharge in real time by using the isolated forest algorithm, and improves treatment efficiency and reduces energy consumption through multi-objective optimization. The system can recover valuable resources such as energy, phosphorus, nitrogen and the like in sewage, and predict water quality change trend through LSTM time series, so as to support decision makers to make optimization scheme. Through these functions, the system not only realizes fine management of the sewage treatment process, but also maximizes the recovery and reuse of resources, and promotes the intelligentization and sustainable development of municipal sewage treatment.

Description

Technical Field

[0001] This invention relates to the field of urban wastewater treatment technology, and in particular to an intelligent monitoring and resource-based treatment system and method for urban wastewater. Background Technology

[0002] With the advancement of urbanization, wastewater treatment has become a key task in urban environmental management. Traditional wastewater treatment systems still have many limitations when facing complex water quality, diverse pollution sources, and unstable water flow. Many wastewater treatment plants still rely on manual operation and experience-based guidance to adjust treatment processes, resulting in low treatment efficiency, high energy consumption, and insufficient resource recovery. Existing wastewater treatment systems have limited monitoring methods, making it impossible to monitor multiple water quality indicators in real time and lacking early warning capabilities for sudden pollution events. Traditional treatment models cannot respond quickly when water quality changes significantly, leading to resource waste and unstable treatment effects.

[0003] Existing urban wastewater treatment systems generally suffer from insufficient real-time monitoring data collection, delayed response times in the treatment process, and low resource recovery efficiency. Traditional wastewater flow monitoring relies heavily on manual inspections or single sensors, lacking precise monitoring of changes in pipe network flow velocity and pressure. This leads to untimely detection of pipe network anomalies such as overflows and blockages, affecting the stability of wastewater treatment. Water quality monitoring and pollutant identification also rely on single sensors, failing to comprehensively process multi-dimensional water quality data, resulting in some pollutants not being identified and treated in a timely manner. Although some existing systems attempt to adjust treatment process parameters through manual settings or empirical rules, the lack of adaptive adjustment mechanisms prevents optimization of treatment efficiency and energy consumption, and fails to adequately address dynamically changing water quality demands. Existing resource recovery technologies and intelligent decision support systems are still under development, unable to perform multi-objective optimization and long-term environmental planning based on system data in real time, making it difficult to achieve intelligent management and maximized resource utilization throughout the entire wastewater treatment process. Therefore, we provide an intelligent monitoring and resource recovery system and method for urban wastewater. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing an intelligent monitoring and resource-based treatment system and method for urban sewage.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: A smart monitoring and resource-based treatment system and method for urban wastewater includes an intelligent wastewater acquisition and sensing module for real-time acquisition of wastewater data to form an intelligent sensing network, an intelligent control and treatment optimization module for intelligently adjusting treatment process parameters based on the acquired wastewater data, a resource-based recycling and reuse module for extracting and reusing useful components from wastewater, and an intelligent decision-making and predictive management module for providing strategy suggestions and long-term planning for global optimization based on comprehensive data and models.

[0006] The present invention is further configured such that: the intelligent sewage collection and sensing module includes a flow monitoring module for real-time monitoring of pipe network flow and velocity to determine abnormal overflow or pipe network blockage; a water quality sensing module for real-time collection of water quality data through multiple sensors (pH, conductivity, dissolved oxygen, COD, nitrogen and phosphorus); a pollutant identification module for analyzing the types and concentrations of specific pollutants in the water using machine vision or spectral sensing; an abnormal alarm module for determining abnormal discharge and triggering early warning based on real-time data and threshold algorithms; and a data fusion storage module for integrating multi-source sensor data, denoising, unifying the format, and storing it in the cloud or edge server. The flow monitoring module provides the water quality sensing module with support data on flow rate and transport status by monitoring the pipeline flow and velocity in real time; the water quality sensing module provides the pollutant identification module with real-time basic water quality information by collecting water quality data; the pollutant identification module uses the data from the water quality sensing module to analyze the type and concentration of specific pollutants to support the abnormal alarm module in triggering abnormal discharge warnings; the abnormal alarm module provides the data fusion and storage module with key alarm information for storage and recording by monitoring abnormal conditions in real time; and the data fusion and storage module denoises and uniformly formats the data from each sensor to support the data analysis and decision-making of the entire system. The flow monitoring module uses Kalman filtering to suppress noise and predict wastewater flow:

[0007] in: Updated traffic forecast; Kalman gain; Actual measured flow rate; Measurement matrix; The anomaly alarm module uses the Isolation Forest scoring method to detect rare pollution events in the water quality:

[0008] in: : Abnormal score; : The average segmentation path length of sample xx; : The normalization constant of the total number of samples.

[0009] The present invention is further configured such that: the intelligent control and treatment optimization module includes an adaptive process parameter control module for adjusting aeration volume, reagent dosage, and flow rate according to water quality fluctuations; a dynamic reagent dosing module for accurately controlling the dosage of flocculant, phosphorus removal agent, and disinfectant in conjunction with real-time water quality; an energy consumption optimization module for automatically adjusting equipment operation strategies to reduce energy consumption using energy monitoring and prediction models; an intelligent flow regulation module for achieving pipeline pressure balance and uniform sewage distribution through pump station scheduling; and an adaptive graded treatment module for diverting sewage to different treatment units according to the degree of pollution. The adaptive process parameter control module adjusts the dosage and accuracy of the reagents in the dynamic reagent dosing module according to water quality fluctuations and demand; the dynamic reagent dosing module adjusts the dosage of chemicals according to water quality changes and works with the energy consumption optimization module to optimize equipment energy consumption and reagent utilization efficiency; the energy consumption optimization module automatically adjusts the equipment operation mode using energy monitoring data, thereby cooperating with the intelligent flow regulation module to maintain pipeline pressure balance; the intelligent flow regulation module ensures uniform distribution of water flow within the pipeline network through pump station scheduling, influencing the wastewater diversion strategy of the adaptive graded treatment module; the adaptive graded treatment module directs wastewater to the most suitable treatment unit based on the concentration and type of wastewater pollutants. The adaptive control module for process parameters ensures optimized dynamic response of the process system through a PID optimization algorithm.

[0010] in: : Control output (aeration intensity); Error between set value and actual value; , , These are the proportional, integral, and differential coefficients, respectively.

[0011] The present invention is further configured such that: the resource recycling and reuse module includes an energy recovery module for converting wastewater organic matter into electrical energy or biogas using anaerobic digestion or microbial fuel cells; a nutrient recovery module for recovering phosphorus, nitrogen, and other nutrients for use as fertilizers or chemical raw materials; a water reuse module for recycling and reusing water resources in wastewater through membrane technology and ultraviolet disinfection; a solid waste treatment module for drying, incinerating, or producing biochar and other resource-based utilization of sludge; and a high-value-added chemical extraction module for extracting proteins and polysaccharides as industrial raw materials from wastewater or sludge. The energy recovery module converts organic matter in wastewater into biogas or electricity to support the nutrient recovery module, providing a sufficient source of nutrients for fertilizers or chemical raw materials. The nutrient recovery module recovers phosphorus and nitrogen resources from the water, supporting the water reuse module to meet the high-quality water standards required for reuse. The water reuse module uses membrane technology and ultraviolet disinfection to treat the recovered water, supplying it to the solid waste treatment module's treatment system, thus optimizing water resource utilization. The biochar or energy recovered from the sludge in the solid waste treatment module provides raw materials for the high-value-added chemical extraction module. The high-value-added chemical extraction module utilizes polysaccharides and protein extracts from wastewater or sludge to provide a more efficient resource recovery method for the energy recovery module. The energy recovery module utilizes a microbial kinetic model to optimize organic matter conversion efficiency.

[0012] in: Microbial growth rate; Maximum growth rate; Substrate concentration; : Half-saturation constant.

[0013] The present invention is further configured such that: the intelligent decision prediction management module includes a water quality prediction module for using historical data to predict future water quality change trends to assist in regulation; a risk assessment scheduling module for assessing system operation risks (excessive discharge, equipment failure) and proposing scheduling schemes; an economic environment optimization module for providing optimal operation strategies through multi-objective optimization (cost, energy consumption, environmental impact); a decision visualization module for displaying operation status through dashboards, maps and trend charts to support managers in making rapid decisions; and a system self-learning module for continuously updating model parameters based on historical operation data. The water quality prediction module analyzes historical data to predict future water quality changes, assisting the risk assessment and scheduling module in formulating timely system scheduling plans. The risk assessment and scheduling module assesses system operational risks and provides scheduling plans for the economic and environmental optimization module to perform multi-objective optimization. The economic and environmental optimization module, based on multi-objective data on cost, energy consumption, and environmental impact, supports the decision visualization module in displaying the optimal plan and operational status. The decision visualization module uses dashboards and trend charts to display system operational status, helping the system self-learning module improve model updates and optimization algorithms. The system self-learning module continuously optimizes model parameters based on historical data, providing new training data for the water quality prediction module to improve prediction accuracy. The water quality prediction module uses LSTM time series prediction to achieve short-term water quality trend prediction.

[0014] in: Hidden state, storing time series information; : Current water quality parameters; : Predicted output.

[0015] The present invention is further configured to include the following steps: S1. Establish comprehensive information on wastewater flow, water quality, and pollutants to provide basic data for subsequent intelligent treatment; S2. Based on the collected data, the parameters of the sewage treatment process are intelligently adjusted to achieve precise control of the treatment process; S3. Extract usable resources from sewage and sludge to realize the energy, nutrient, and water reuse of wastewater; S4. Based on historical and real-time data, predict, assess risks, and optimize the scheduling of wastewater treatment systems to support long-term planning.

[0016] The present invention is further configured such that: in step S1, establishing comprehensive environmental information on wastewater flow, water quality, and pollutants to provide basic data for subsequent intelligent treatment: S1.1 Utilize embedded flow sensors and pressure monitors to dynamically scan urban sewage pipe networks, acquire water flow velocity, pressure and instantaneous flow information, and eliminate measurement noise through recursive filtering algorithms; S1.2 Continuous sampling of key indicators such as chemical oxygen demand (COD), dissolved oxygen, total nitrogen, and total phosphorus in wastewater was conducted, and the measurement data from different sensors were integrated into a consistent multidimensional water quality vector using a weighted sensor fusion method, providing a reliable basis for subsequent pollution analysis. S1.3. Perform principal component analysis (PCA) or spectral feature extraction on the collected multidimensional water quality data to identify potential pollutants in the water and their concentration patterns, providing a high-dimensional feature space for anomaly event identification; S1.4. All flow, water quality, and anomaly detection data are synchronized to the edge server through a distributed database, and timestamp alignment and noise reduction are performed to ensure data consistency and traceability, providing a reliable information flow for subsequent intelligent optimization.

[0017] The present invention is further configured such that: in step S2, the wastewater treatment process parameters are intelligently adjusted based on the collected data to achieve precise control of the treatment process: S2.1 Calculate the optimal operating parameters of each treatment unit, including aeration rate, stirring intensity, and chemical dosage, based on real-time water quality data and historical process response curves, and form an operating recommendation matrix; S2.2 Utilize real-time power monitoring and prediction models to evaluate the energy consumption of each processing unit, and balance energy consumption and processing efficiency through multi-objective optimization algorithms to provide energy optimization strategies for control decisions; S2.3. Based on the pipeline flow rate and the load of the treatment unit, dynamically adjust the operation of the pump station and valves to achieve a reasonable distribution of sewage among the units and prevent some units from being overloaded or stagnant; S2.4. Wastewater is classified according to its pollution level, chemical composition, and recyclable content to determine which treatment modules or resource utilization paths the wastewater will enter, ensuring refined management of the treatment process and maximizing resource recovery.

[0018] The present invention is further configured such that: in step S3, extracting usable resources from sewage and sludge to realize the energy recovery, nutrient recovery, and water reuse of wastewater: S3.1. Utilize anaerobic digestion or microbial fuel cells to convert organic matter into biogas or electrical energy, and optimize the conversion efficiency through the Monod kinetic model; S3.2. Recover nitrogen, phosphorus and trace elements in water through chemical precipitation or adsorption methods and convert them into fertilizers or industrial raw materials; S3.3. Utilize multiple technologies such as membrane separation, ultraviolet disinfection, and ozone treatment to deeply purify water, making water resources reusable and ensuring that the water quality standards required by each treatment module are met. S3.4. Dewater, dry, thermally treat, or produce biochar from sludge to convert by-products into energy or resources, providing a raw material basis for the extraction of high-value-added chemicals; S3.5 Extract high-value-added industrial raw materials such as proteins and polysaccharides from sewage and treated sludge, using an optimized separation algorithm.

[0019] The present invention is further configured such that: in step S4, based on historical and real-time data, prediction, risk assessment, and optimized scheduling of the wastewater treatment system are performed to support long-term planning. S4.1 Utilize historical water quality and flow data to construct a time series model and use the LSTM algorithm to predict short-term water quality trends; S4.2 Conduct probability assessments of potential risks such as pipeline overflow, equipment failure, and excessive emissions, and formulate dynamic scheduling strategies to avoid high-risk events; S4.3 Perform weighted multi-objective optimization calculations on processing costs, energy consumption, and environmental impact, and determine the optimal system operation scheme through a constrained optimization model; S4.4 Display the predicted trends, risk assessments and optimization results in the form of dashboards, maps and trend charts so that managers can quickly understand the system status and control plans; S4.5. Continuously update the prediction model parameters based on actual operating data and iteratively optimize them using the gradient descent algorithm.

[0020] The beneficial effects of this invention are as follows: 1. This invention, by collecting real-time data on pipeline flow, water quality, and pollutants, and combining intelligent control and processing modules, resource recovery modules, and decision management modules, can efficiently monitor the wastewater treatment process and automatically adjust process parameters. It optimizes flow prediction and process response through Kalman filtering and PID control algorithms, utilizes the isolated forest algorithm to detect abnormal emissions in real time, and improves treatment efficiency and reduces energy consumption through multi-objective optimization. The system can recover valuable resources from wastewater, such as energy, phosphorus, and nitrogen, and predict water quality change trends through LSTM time series analysis, supporting decision-makers in formulating optimization plans. Through these functions, the system not only achieves refined management of the wastewater treatment process but also maximizes resource recovery and reuse, promoting the intelligent and sustainable development of urban wastewater treatment.

[0021] 2. This invention monitors wastewater flow, water quality, and pollutants in real time, and dynamically adjusts the parameters of the wastewater treatment process to optimize energy consumption and resource recovery. This ensures the high efficiency and refined management of the wastewater treatment process. By utilizing advanced algorithms, such as LSTM for water quality trend prediction, Monod model for optimizing organic matter conversion efficiency, and multi-objective optimization algorithms to balance treatment efficiency and energy consumption, the invention ensures maximum recovery of wastewater resources for energy, eutrophication, and water reuse. Through intelligent decision-making and risk assessment, the method can effectively avoid potential risks, dynamically adjust treatment strategies, and support rapid decision-making through visualization, thereby achieving continuous optimization of the wastewater treatment process and sustainable environmental development. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the system modules in this invention.

[0023] Figure 2 This is a schematic diagram of the method steps in this invention. Detailed Implementation

[0024] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Example 1

[0025] like Figure 1 As shown, an intelligent monitoring and resource-based treatment system for urban sewage includes an intelligent sewage acquisition and sensing module for collecting sewage data in real time to form an intelligent sensing network, an intelligent control and treatment optimization module for intelligently adjusting treatment process parameters based on the collected sewage data, a resource-based recycling and reuse module for extracting and reusing useful components from sewage, and an intelligent decision-making and predictive management module for providing strategy suggestions and long-term planning for global optimization based on comprehensive data and models. The intelligent wastewater collection and sensing module includes a flow monitoring module for real-time monitoring of pipe network flow and velocity to determine abnormal overflow or pipe network blockage; a water quality sensing module for real-time collection of water quality data through multiple sensors (pH, conductivity, dissolved oxygen, COD, nitrogen and phosphorus); a pollutant identification module for analyzing the types and concentrations of specific pollutants in water using machine vision or spectral sensing; an abnormal alarm module for determining abnormal discharge and triggering early warning based on real-time data and threshold algorithms; and a data fusion storage module for integrating multi-source sensor data, denoising, unifying the format, and storing it in the cloud or edge server. The flow monitoring module provides the water quality sensing module with support data on flow rate and velocity in real time to monitor the pipeline network. The water quality sensing module provides the pollutant identification module with real-time basic water quality information by collecting water quality data. The pollutant identification module uses the data from the water quality sensing module to analyze the type and concentration of specific pollutants to support the abnormal alarm module in triggering abnormal discharge warnings. The abnormal alarm module provides the data fusion and storage module with key alarm information for storage and recording by monitoring abnormal conditions in real time. The data fusion and storage module denoises and uniformly formats the data from each sensor to support the data analysis and decision-making of the entire system. The flow monitoring module uses Kalman filtering to suppress noise and predict wastewater flow.

[0026] in: Updated traffic forecast; Kalman gain; Actual measured flow rate; Measurement matrix; The anomaly alarm module uses the Isolation Forest scoring method to detect rare pollution events in water quality:

[0027] in: : Abnormal score; : The average segmentation path length of sample xx; : The normalization constant of the total number of samples; The intelligent control and treatment optimization module includes an adaptive control module for process parameters such as aeration volume, chemical dosage, and flow rate, which adjusts the aeration volume, chemical dosage, and flow rate according to water quality fluctuations; a dynamic chemical dosing module for precise control of flocculant, phosphorus removal agent, and disinfectant dosage in conjunction with real-time water quality; an energy consumption optimization module for automatically adjusting equipment operation strategies to reduce energy consumption using energy monitoring and prediction models; an intelligent flow regulation module for achieving pipeline pressure balance and uniform wastewater distribution through pump station scheduling; and an adaptive graded treatment module for diverting wastewater to different treatment units according to the degree of pollution. The adaptive process parameter control module adjusts the dosage and accuracy of chemicals in the dynamic chemical dosing module based on water quality fluctuations and demand; the dynamic chemical dosing module adjusts the chemical dosage according to water quality changes and works with the energy consumption optimization module to optimize equipment energy consumption and chemical utilization efficiency; the energy consumption optimization module automatically adjusts equipment operation mode using energy monitoring data, thereby cooperating with the intelligent flow regulation module to maintain pipeline pressure balance; the intelligent flow regulation module ensures uniform distribution of water flow within the pipeline network through pump station scheduling, influencing the wastewater diversion strategy of the adaptive graded treatment module; the adaptive graded treatment module directs wastewater to the most suitable treatment unit based on the concentration and type of wastewater pollutants. The adaptive control module for process parameters ensures optimized dynamic response of the process system through a PID optimization algorithm.

[0028] in: : Control output (aeration intensity); Error between set value and actual value; , , These are the proportional, integral, and differential coefficients, respectively. The resource recycling and reuse module includes an energy recovery module for converting wastewater organic matter into electricity or biogas using anaerobic digestion or microbial fuel cells; a nutrient recovery module for recovering phosphorus, nitrogen, and other nutrients for use as fertilizers or chemical raw materials; a water reuse module for recycling and reusing water resources in wastewater through membrane technology and ultraviolet disinfection; a solid waste treatment module for resource utilization such as drying, incinerating, or producing biochar from sludge; and a high-value-added chemical extraction module for extracting proteins and polysaccharides from wastewater or sludge as industrial raw materials. The energy recovery module converts organic matter in wastewater into biogas or electricity to support the nutrient recovery module, providing a sufficient source of nutrients for fertilizers or chemical raw materials. The nutrient recovery module recovers phosphorus and nitrogen resources from the water, supporting the water reuse module to meet the high-quality water standards required for reuse. The water reuse module uses membrane technology and ultraviolet disinfection to supply the recovered water to the solid waste treatment module's treatment system, optimizing water resource utilization. The biochar or energy recovered from the sludge in the solid waste treatment module provides raw materials for the high-value-added chemical extraction module. The high-value-added chemical extraction module utilizes polysaccharides and protein extracts from wastewater or sludge to provide a more efficient resource recovery method for the energy recovery module. The energy recovery module utilizes a microbial kinetic model to optimize organic matter conversion efficiency.

[0029] in: Microbial growth rate; Maximum growth rate; Substrate concentration; : Half-saturation constant; The intelligent decision-making and prediction management module includes a water quality prediction module for using historical data to predict future water quality change trends to assist in regulation; a risk assessment and scheduling module for assessing system operation risks (excessive discharge, equipment failure) and proposing scheduling schemes; an economic environment optimization module for providing optimal operation strategies through multi-objective optimization (cost, energy consumption, environmental impact); a decision visualization module for displaying operation status through dashboards, maps, and trend charts to support managers in making rapid decisions; and a system self-learning module for continuously updating model parameters based on historical operation data. The water quality prediction module analyzes historical data to predict future water quality changes, assisting the risk assessment and scheduling module in formulating timely system scheduling plans. The risk assessment and scheduling module assesses system operational risks and provides scheduling plans for the economic and environmental optimization module to perform multi-objective optimization. The economic and environmental optimization module supports the decision visualization module by displaying the optimal plan and operational status based on multi-objective data of cost, energy consumption, and environmental impact. The decision visualization module displays the system's operational status through dashboards and trend charts, helping the system self-learning module improve model updates and optimization algorithms. The system self-learning module continuously optimizes model parameters based on historical data, providing new training data for the water quality prediction module to improve prediction accuracy. The water quality prediction module uses LSTM time series forecasting to predict short-term water quality trends.

[0030] in: Hidden state, storing time series information; : Current water quality parameters; : Predicted output.

[0031] In the above embodiments, the urban wastewater intelligent monitoring and resource-based treatment system collects real-time data on pipe network flow, water quality, and pollutants. Combined with intelligent control and treatment modules, resource recovery modules, and decision management modules, it can efficiently monitor the wastewater treatment process and automatically adjust process parameters. It optimizes flow prediction and process response through Kalman filtering and PID control algorithms, uses the isolated forest algorithm to detect abnormal emissions in real time, and improves treatment efficiency and reduces energy consumption through multi-objective optimization. The system can recover valuable resources from wastewater, such as energy, phosphorus, and nitrogen, and predict water quality change trends through LSTM time series analysis, supporting decision-makers in formulating optimization plans. Through these functions, the system not only achieves refined management of the wastewater treatment process but also maximizes resource recovery and reuse, promoting the intelligent and sustainable development of urban wastewater treatment. Example 2

[0032] like Figure 1-2 As shown, a method for intelligent monitoring and resource-based treatment of urban sewage includes the following steps: S1. Establish comprehensive information on wastewater flow, water quality, and pollutants to provide basic data for subsequent intelligent treatment; S2. Based on the collected data, the parameters of the sewage treatment process are intelligently adjusted to achieve precise control of the treatment process; S3. Extract usable resources from sewage and sludge to realize the energy, nutrient, and water reuse of wastewater; S4. Based on historical and real-time data, predict, assess risks, and optimize the scheduling of wastewater treatment systems to support long-term planning; Step S1: Establish comprehensive environmental information on wastewater flow, water quality, and pollutants to provide basic data for subsequent intelligent treatment. S1.1 Utilize embedded flow sensors and pressure monitors to dynamically scan urban sewage pipe networks, acquire water flow velocity, pressure and instantaneous flow information, and eliminate measurement noise through recursive filtering algorithms; S1.2 Continuous sampling of key indicators such as chemical oxygen demand (COD), dissolved oxygen, total nitrogen, and total phosphorus in wastewater was conducted, and the measurement data from different sensors were integrated into a consistent multidimensional water quality vector using a weighted sensor fusion method, providing a reliable basis for subsequent pollution analysis. S1.3. Perform principal component analysis (PCA) or spectral feature extraction on the collected multidimensional water quality data to identify potential pollutants in the water and their concentration patterns, providing a high-dimensional feature space for anomaly event identification; S1.4. Synchronize all flow, water quality, and anomaly detection data to the edge server through a distributed database, and perform timestamp alignment and noise reduction to ensure data consistency and traceability, providing a reliable information flow for subsequent intelligent optimization; Step S2: Intelligently adjust the wastewater treatment process parameters based on the collected data to achieve precise control of the treatment process. S2.1 Calculate the optimal operating parameters of each treatment unit, including aeration rate, stirring intensity, and chemical dosage, based on real-time water quality data and historical process response curves, and form an operating recommendation matrix; S2.2 Utilize real-time power monitoring and prediction models to evaluate the energy consumption of each processing unit, and balance energy consumption and processing efficiency through multi-objective optimization algorithms to provide energy optimization strategies for control decisions; S2.3. Based on the pipeline flow rate and the load of the treatment unit, dynamically adjust the operation of the pump station and valves to achieve a reasonable distribution of sewage among the units and prevent some units from being overloaded or stagnant; S2.4. Wastewater is classified according to its pollution level, chemical composition and recyclable content, and the wastewater is determined to enter different treatment modules or resource utilization paths to ensure refined management of the treatment process and maximize resource recovery. Step S3: Extracting usable resources from sewage and sludge to achieve energy recovery, nutrient recovery, and water reuse from wastewater: S3.1. Utilize anaerobic digestion or microbial fuel cells to convert organic matter into biogas or electrical energy, and optimize the conversion efficiency through the Monod kinetic model; S3.2. Recover nitrogen, phosphorus and trace elements in water through chemical precipitation or adsorption methods and convert them into fertilizers or industrial raw materials; S3.3. Utilize multiple technologies such as membrane separation, ultraviolet disinfection, and ozone treatment to deeply purify water, making water resources reusable and ensuring that the water quality standards required by each treatment module are met. S3.4. Dewater, dry, thermally treat, or produce biochar from sludge to convert by-products into energy or resources, providing a raw material basis for the extraction of high-value-added chemicals; S3.5 Extracting high-value-added industrial raw materials such as proteins and polysaccharides from sewage and treated sludge, using an optimized separation algorithm; Step S4: Based on historical and real-time data, predict, assess risks, and optimize the scheduling of the wastewater treatment system to support long-term planning. S4.1 Utilize historical water quality and flow data to construct a time series model and use the LSTM algorithm to predict short-term water quality trends; S4.2 Conduct probability assessments of potential risks such as pipeline overflow, equipment failure, and excessive emissions, and formulate dynamic scheduling strategies to avoid high-risk events; S4.3 Perform weighted multi-objective optimization calculations on processing costs, energy consumption, and environmental impact, and determine the optimal system operation scheme through a constrained optimization model; S4.4 Display the predicted trends, risk assessments and optimization results in the form of dashboards, maps and trend charts so that managers can quickly understand the system status and control plans; S4.5. Continuously update the prediction model parameters based on actual operating data and iteratively optimize them using the gradient descent algorithm.

[0033] In the above embodiments, the intelligent monitoring and resource-based treatment method for urban wastewater monitors wastewater flow, water quality, and pollutants in real time, and dynamically adjusts the parameters of the wastewater treatment process to optimize energy consumption and resource recovery. This ensures the high efficiency and refined management of the wastewater treatment process. By utilizing advanced algorithms, such as LSTM for water quality trend prediction, Monod model for optimizing organic matter conversion efficiency, and multi-objective optimization algorithms to balance treatment efficiency and energy consumption, the method ensures maximum recovery of wastewater resources for energy, nutrient recovery, and water reuse. Through intelligent decision-making and risk assessment, the method can effectively avoid potential risks, dynamically adjust treatment strategies, and support rapid decision-making through visualization, thereby achieving continuous optimization of the wastewater treatment process and sustainable environmental development.

[0034] Working Principle: This invention collects real-time data on pipe network flow, water quality, and pollutants to establish comprehensive environmental information on sewage flow and pollutants, providing data support for subsequent intelligent treatment. Embedded sensors and pressure monitors dynamically scan the pipe network, collecting data such as water flow velocity and pressure. Noise is eliminated using a recursive filtering algorithm. Multi-sensor technology collects real-time water quality data such as chemical oxygen demand (COD), dissolved oxygen, nitrogen, and phosphorus, integrating them into a multi-dimensional water quality vector using a weighted sensor fusion method. Principal component analysis (PCA) and spectral feature extraction are used to identify potential pollutants and their concentrations in the water, providing data support for real-time monitoring of abnormal discharge events. All data is synchronized to an edge server via a distributed database, ensuring data consistency and traceability, supporting intelligent optimization and precise control.

[0035] During the treatment process, the system dynamically adjusts process parameters, such as aeration rate, reagent dosage, and flow rate, based on collected water quality data and historical process response curves using a PID control algorithm to ensure precise control of the treatment process. It uses real-time power monitoring and prediction models to assess the energy consumption of each treatment unit, and combines multi-objective optimization algorithms to balance energy efficiency and treatment efficiency, avoiding resource waste. Dynamic adjustment of flow rate and load is achieved through pump station scheduling to balance pipeline pressure, ensuring that sewage is evenly distributed to different treatment units. In terms of sewage diversion, based on the degree of water pollution and the content of recyclables, sewage is guided to the most suitable treatment module or resource recovery path, thereby ensuring maximum resource recovery and utilization.

[0036] In terms of resource recovery and predictive management, the system utilizes microbial kinetics models to optimize organic matter conversion efficiency, converting organic matter in wastewater into biogas or electricity. Membrane technology and ultraviolet disinfection ensure water reuse. Weighted multi-objective optimization calculations balance treatment costs, energy consumption, and environmental impact, providing optimal operating strategies. LSTM time series forecasting is used to predict short-term water quality trends, providing a basis for risk assessment and scheduling. Through intelligent decision-making and risk assessment modules, the system can assess potential risks of pipeline overflows, equipment failures, and excessive emissions in real time, formulate dynamic scheduling strategies, and visualize the operating status through dashboards and trend charts, supporting rapid decision-making by managers. This continuously optimizes the wastewater treatment process and promotes sustainable environmental development.

[0037] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A smart monitoring and resource-based treatment system for urban sewage, characterized in that, It includes an intelligent wastewater acquisition and sensing module for collecting wastewater data in real time to form an intelligent sensing network; an intelligent control and treatment optimization module for intelligently adjusting treatment process parameters based on the collected wastewater data; a resource recovery and reuse module for extracting and reusing useful components in wastewater; and an intelligent decision prediction and management module for providing strategy suggestions and long-term planning for global optimization based on comprehensive data and models.

2. The intelligent monitoring and resource-based treatment system for urban sewage according to claim 1, characterized in that, The intelligent wastewater collection and sensing module includes a flow monitoring module for real-time monitoring of pipe network flow and velocity to determine abnormal overflow or pipe network blockage; a water quality sensing module for real-time collection of water quality data through multiple sensors (pH, conductivity, dissolved oxygen, COD, nitrogen and phosphorus); a pollutant identification module for analyzing the types and concentrations of specific pollutants in the water using machine vision or spectral sensing; an abnormal alarm module for determining abnormal discharge and triggering early warning based on real-time data and threshold algorithms; and a data fusion storage module for integrating multi-source sensor data, denoising, unifying the format, and storing it in the cloud or edge server. The flow monitoring module provides the water quality sensing module with support data on flow rate and transport status by monitoring the pipeline flow and velocity in real time; the water quality sensing module provides the pollutant identification module with real-time basic water quality information by collecting water quality data; the pollutant identification module uses the data from the water quality sensing module to analyze the type and concentration of specific pollutants to support the abnormal alarm module in triggering abnormal discharge warnings; the abnormal alarm module provides the data fusion and storage module with key alarm information for storage and recording by monitoring abnormal conditions in real time; and the data fusion and storage module denoises and uniformly formats the data from each sensor to support the data analysis and decision-making of the entire system. The flow monitoring module uses Kalman filtering to suppress noise and predict wastewater flow: in: The anomaly alarm module uses the Isolation Forest scoring method to detect rare pollution events in the water quality: in:

3. The intelligent monitoring and resource-based treatment system for urban sewage according to claim 1, characterized in that, The intelligent control and optimization module includes an adaptive process parameter control module for adjusting aeration volume, reagent dosage, and flow rate according to water quality fluctuations; a dynamic reagent dosing module for accurately controlling the dosage of flocculants, phosphorus removers, and disinfectants in conjunction with real-time water quality; an energy consumption optimization module for automatically adjusting equipment operation strategies to reduce energy consumption using energy monitoring and prediction models; an intelligent flow regulation module for achieving pipeline pressure balance and uniform sewage distribution through pump station scheduling; and an adaptive graded treatment module for diverting sewage to different treatment units according to the degree of pollution. The adaptive process parameter control module adjusts the dosage and accuracy of the reagents in the dynamic reagent dosing module according to water quality fluctuations and demand; the dynamic reagent dosing module adjusts the dosage of chemicals according to water quality changes and works with the energy consumption optimization module to optimize equipment energy consumption and reagent utilization efficiency; the energy consumption optimization module automatically adjusts the equipment operation mode using energy monitoring data, thereby cooperating with the intelligent flow regulation module to maintain pipeline pressure balance; the intelligent flow regulation module ensures uniform distribution of water flow within the pipeline network through pump station scheduling, influencing the wastewater diversion strategy of the adaptive graded treatment module; the adaptive graded treatment module directs wastewater to the most suitable treatment unit based on the concentration and type of wastewater pollutants. The adaptive control module for process parameters ensures optimized dynamic response of the process system through a PID optimization algorithm. in:

4. The intelligent monitoring and resource-based treatment system for urban sewage according to claim 1, characterized in that, The resource recycling and reuse module includes an energy recovery module for converting wastewater organic matter into electrical energy or biogas using anaerobic digestion or microbial fuel cells; a nutrient recovery module for recovering phosphorus, nitrogen, and other nutrients for use as fertilizers or chemical raw materials; a water reuse module for recycling and reusing water resources in wastewater through membrane technology and ultraviolet disinfection; a solid waste treatment module for drying, incinerating, or producing biochar from sludge for resource utilization; and a high-value-added chemical extraction module for extracting proteins and polysaccharides from wastewater or sludge as industrial raw materials. The energy recovery module converts organic matter in wastewater into biogas or electricity to support the nutrient recovery module, providing a sufficient source of nutrients for fertilizers or chemical raw materials. The nutrient recovery module recovers phosphorus and nitrogen resources from the water, supporting the water reuse module to meet the high-quality water standards required for reuse. The water reuse module uses membrane technology and ultraviolet disinfection to treat the recovered water, supplying it to the solid waste treatment module's treatment system, thus optimizing water resource utilization. The biochar or energy recovered from the sludge in the solid waste treatment module provides raw materials for the high-value-added chemical extraction module. The high-value-added chemical extraction module utilizes polysaccharides and protein extracts from wastewater or sludge to provide a more efficient resource recovery method for the energy recovery module. The energy recovery module utilizes a microbial kinetic model to optimize organic matter conversion efficiency. in:

5. The intelligent monitoring and resource-based treatment system for urban sewage according to claim 1, characterized in that, The intelligent decision prediction and management module includes a water quality prediction module for using historical data to predict future water quality change trends to assist in regulation; a risk assessment and scheduling module for assessing system operation risks (excessive discharge, equipment failure) and proposing scheduling schemes; an economic environment optimization module for providing optimal operation strategies through multi-objective optimization (cost, energy consumption, environmental impact); a decision visualization module for displaying operation status through dashboards, maps, and trend charts to support managers in making rapid decisions; and a system self-learning module for continuously updating model parameters based on historical operation data. The water quality prediction module uses historical data analysis to predict future water quality changes, assisting the risk assessment and scheduling module in formulating timely system scheduling plans. The risk assessment and scheduling module provides scheduling schemes by assessing system operational risks, enabling the economic and environmental optimization module to perform multi-objective optimization. The economic and environmental optimization module, based on multi-objective data on cost, energy consumption, and environmental impact, supports the decision visualization module in displaying the optimal scheme and operational status. The decision visualization module uses dashboards and trend charts to display system operational status, helping the system self-learning module improve model updates and optimization algorithms. The system self-learning module continuously optimizes model parameters based on historical data, providing new training data for the water quality prediction module to improve prediction accuracy. The water quality prediction module uses LSTM time series prediction to achieve short-term water quality trend prediction. in:

6. A method for intelligent monitoring and resource-based treatment of urban sewage, characterized in that, Includes the following steps: S1. Establish comprehensive information on wastewater flow, water quality, and pollutants to provide basic data for subsequent intelligent treatment; S2. Based on the collected data, the parameters of the sewage treatment process are intelligently adjusted to achieve precise control of the treatment process; S3. Extract usable resources from sewage and sludge to realize the energy, nutrient, and water reuse of wastewater; S4. Based on historical and real-time data, predict, assess risks, and optimize the scheduling of wastewater treatment systems to support long-term planning.

7. The method for intelligent monitoring and resource-based treatment of urban sewage according to claim 6, characterized in that: In step S1, establishing comprehensive environmental information on wastewater flow, water quality, and pollutants provides foundational data for subsequent intelligent treatment: S1.1 Utilize embedded flow sensors and pressure monitors to dynamically scan urban sewage pipe networks, acquire water flow velocity, pressure and instantaneous flow information, and eliminate measurement noise through recursive filtering algorithms; S1.2 Continuous sampling of key indicators such as chemical oxygen demand (COD), dissolved oxygen, total nitrogen, and total phosphorus in wastewater was conducted, and the measurement data from different sensors were integrated into a consistent multidimensional water quality vector using a weighted sensor fusion method, providing a reliable basis for subsequent pollution analysis. S1.

3. Perform principal component analysis (PCA) or spectral feature extraction on the collected multidimensional water quality data to identify potential pollutants in the water and their concentration patterns, providing a high-dimensional feature space for anomaly event identification; S1.

4. All flow, water quality, and anomaly detection data are synchronized to the edge server through a distributed database, and timestamp alignment and noise reduction are performed to ensure data consistency and traceability, providing a reliable information flow for subsequent intelligent optimization.

8. The method for intelligent monitoring and resource-based treatment of urban sewage according to claim 6, characterized in that: In step S2, the wastewater treatment process parameters are intelligently adjusted based on the collected data to achieve precise control of the treatment process. S2.1 Calculate the optimal operating parameters of each treatment unit, including aeration rate, stirring intensity, and chemical dosage, based on real-time water quality data and historical process response curves, and form an operating recommendation matrix; S2.2 Utilize real-time power monitoring and prediction models to evaluate the energy consumption of each processing unit, and balance energy consumption and processing efficiency through multi-objective optimization algorithms to provide energy optimization strategies for control decisions; S2.

3. Based on the pipeline flow rate and the load of the treatment unit, dynamically adjust the operation of the pump station and valves to achieve a reasonable distribution of sewage among the units and prevent some units from being overloaded or stagnant; S2.

4. Wastewater is classified according to its pollution level, chemical composition, and recyclable content to determine which treatment modules or resource utilization paths the wastewater will enter, ensuring refined management of the treatment process and maximizing resource recovery.

9. A method for intelligent monitoring and resource-based treatment of urban sewage according to claim 6, characterized in that: In step S3, extracting usable resources from sewage and sludge to achieve energy recovery, nutrient recovery, and water reuse from wastewater: S3.

1. Utilize anaerobic digestion or microbial fuel cells to convert organic matter into biogas or electrical energy, and optimize the conversion efficiency through the Monod kinetic model; S3.

2. Recover nitrogen, phosphorus and trace elements in water through chemical precipitation or adsorption methods and convert them into fertilizers or industrial raw materials; S3.

3. Utilize multiple technologies such as membrane separation, ultraviolet disinfection, and ozone treatment to deeply purify water, making water resources reusable and ensuring that the water quality standards required by each treatment module are met. S3.

4. Dewater, dry, thermally treat, or produce biochar from sludge to convert by-products into energy or resources, providing a raw material basis for the extraction of high-value-added chemicals; S3.5 Extract high-value-added industrial raw materials such as proteins and polysaccharides from sewage and treated sludge, using an optimized separation algorithm.

10. A method for intelligent monitoring and resource-based treatment of urban sewage according to claim 6, characterized in that: Step S4 involves predicting, assessing risks, and optimizing the scheduling of the wastewater treatment system based on historical and real-time data, supporting long-term planning. S4.1 Utilize historical water quality and flow data to construct a time series model and use the LSTM algorithm to predict short-term water quality trends; S4.2 Conduct probability assessments of potential risks such as pipeline overflow, equipment failure, and excessive emissions, and formulate dynamic scheduling strategies to avoid high-risk events; S4.3 Perform weighted multi-objective optimization calculations on processing costs, energy consumption, and environmental impact, and determine the optimal system operation scheme through a constrained optimization model; S4.4 Display the predicted trends, risk assessments and optimization results in the form of dashboards, maps and trend charts so that managers can quickly understand the system status and control plans; S4.

5. Continuously update the prediction model parameters based on actual operating data and iteratively optimize them using the gradient descent algorithm.

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