Distributed sewage treatment process management and control system

Abstract

The invention relates to the technical field of sewage treatment, in particular to a distributed sewage treatment process management and control system, which adopts a distributed architecture to replace traditional centralized management and control, breaks geographical space and scale limitations, supports flexible access of sewage treatment stations of different areas and sizes, reduces system deployment cost and complexity, and improves system deployment efficiency. All the units realize data sharing and cooperative processing through interconnection and intercommunication, remote areas or small sites can also obtain efficient management and control, balanced development of urban and rural sewage treatment is promoted, the system supports butt joint with energy-saving and consumption-reducing sewage treatment technologies such as SBR and MBBR and an ozone catalytic oxidation intelligent disinfection technology, technological parameters are collected in real time to assist in optimization management and control, and the system is suitable for large-scale popularization and application. Data support and function verification are provided for technology research and development, conversion of new technology achievements is accelerated, meanwhile, on the premise that the sewage treatment requirement is met, system operation energy consumption is reduced through an energy consumption optimization strategy, the goal of carbon emission reduction is assisted to be achieved, and green and low-carbon conversion of the sewage treatment industry is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of sewage treatment, and in particular to a decentralized sewage treatment process management and control system. Background Art

[0002] With the acceleration of urbanization and tightening environmental protection standards, the demand for decentralized wastewater treatment has exploded, encompassing scenarios such as rural domestic sewage, small urban community sewage, and industrial wastewater in remote areas. However, traditional centralized wastewater treatment management and control systems have significant limitations: First, they rely on central control nodes, with limited geographical coverage, making it difficult to adapt to the flexible deployment requirements of decentralized, small-scale wastewater treatment sites. This results in remote areas or small sites being unable to be effectively managed due to high access costs. Second, the system's poor scalability makes it difficult to efficiently integrate equipment and data across different scales and types of wastewater treatment processes, resulting in high operation and maintenance costs. Summary of the Invention

[0003] In view of the shortcomings of the existing technology, the present invention solves the technical problems thereof by adopting a technical solution: a decentralized sewage treatment process control system, comprising: a monitoring unit, a processing unit, and a management unit, wherein:

[0004] The monitoring unit is used for equipment status monitoring and water quality parameter detection at the sewage treatment site;

[0005] The processing unit is used to receive the detected data information, analyze the received data information, and generate a processing strategy for abnormal conditions. The processing unit is connected to the monitoring unit;

[0006] The management unit is used to remotely receive data information from the processing unit and provide abnormality reminders, information display and operation and maintenance management functions. The management unit is connected to the processing unit;

[0007] The system supports docking with core devices related to sewage treatment and utilization technologies based on energy conservation, consumption reduction, and simultaneous carbon reduction such as SBR and MBBR, receives and utilizes their process parameter information, and assists the system in optimizing and controlling the sewage treatment process. The system supports association with the results of ozone catalytic oxidation technology, providing data support and functional verification for the research and development of smart disinfection technology. The system supports docking with dual-standard sewage treatment equipment in rural sewage treatment demand areas, and promotes the implementation of sewage treatment results through the system based on actual regional needs, including remote control and adaptation of equipment, water quality monitoring, and feedback on treatment effects.

[0008] Through modular design, the functional boundaries of each system component are clearly defined, creating a complete closed loop of "monitoring-analysis-management." The monitoring unit provides the system with real-time data, the processing unit enables intelligent decision-making, and the management unit facilitates remote operation and maintenance. These three elements work together to overcome the limitations of traditional centralized management and control, supporting flexible access and unified management of decentralized wastewater treatment sites, significantly improving the system's deployment flexibility and applicability.

[0009] The present invention further provides for interconnection among the monitoring unit, processing unit, and management unit via a distributed architecture, supporting access to sewage treatment sites of varying sizes and geographic locations. This distributed architecture avoids the traditional centralized system's reliance on central nodes, enhancing the system's fault tolerance and scalability. Sewage treatment sites of varying regions and sizes can be connected on demand, reducing deployment costs. Furthermore, data sharing and collaborative processing between units optimize resource allocation, ensuring efficient management and control even for remote areas or small sites, and promoting balanced development of urban and rural sewage treatment.

[0010] The present invention further provides that the monitoring unit includes an equipment status monitoring module and a water quality parameter detection module. This functional separation enables precise monitoring. The equipment status monitoring module promptly detects abnormalities in equipment current, temperature, pressure, and other indicators to prevent malfunctions. The water quality parameter detection module monitors indicators such as COD and ammonia nitrogen in real time, providing data support for process adjustments. The combination of these two modules provides three-dimensional monitoring of the entire sewage treatment process, ensuring stable system operation and improving treatment efficiency and water quality compliance.

[0011] The equipment status monitoring module is used to monitor the equipment operating status parameters of the sewage treatment site in real time, including but not limited to equipment voltage, current, temperature, pressure, flow, etc.

[0012] The water quality parameter detection module is used to detect sewage water quality parameters in real time, including but not limited to conventional water quality indicators such as COD, BOD, ammonia nitrogen, total phosphorus, and suspended solids.

[0013] The present invention further provides that the water quality parameter detection module includes distributed water quality sensors that utilize Internet of Things (IoT) technology for data transmission, supporting remote calibration and fault diagnosis. IoT technology enables sensors to communicate remotely and maintain themselves, reducing manual inspection costs. The distributed arrangement ensures data coverage of the entire sewage process, avoiding monitoring blind spots. Remote calibration improves data accuracy, while fault diagnosis quickly locates abnormal sensors, ensuring data continuity and providing a reliable basis for decision-making by the processing unit.

[0014] Conventional pollutant indicators:

[0015] When the COD data collected by the monitoring unit exceeds the preset process treatment threshold and lasts for more than 30 minutes, the strategy generation module determines that the biochemical treatment unit needs to be strengthened, increasing the aeration time of the BR process and increasing the packing stirring frequency of the MBBR process, and simultaneously triggering an abnormal warning; if the ammonia nitrogen concentration is higher than the designed treatment upper limit and the nitrifying bacteria activity monitoring data is lower than the normal range through online microbial sensors or enzyme activity detection, it is determined to start the auxiliary denitrification strategy, add a slow-release carbon source, and adjust the anoxic / aerobic period duration;

[0016] Abnormal water quality fluctuations:

[0017] If the water quality parameters suddenly change by more than 50% within a short period of time, the emergency switching process will be implemented, and the emergency regulating tank will be activated for temporary storage and the backup pretreatment unit will be switched to prioritize the stability of the core treatment process.

[0018] Equipment energy efficiency optimization:

[0019] When the system energy consumption data is 15% higher than the historical optimal value for the same period, and the water quality meets the standard and the equipment is fault-free, the strategy generates a module that combines the influent water quality, treated water volume, and equipment operating parameters based on the energy efficiency model. It determines that equipment scheduling needs to be optimized, the number of operating units of multiple parallel pump groups needs to be adjusted, and the timing control of aeration equipment needs to be optimized to reduce ineffective energy consumption.

[0020] The present invention is further configured such that the processing unit includes a data storage module, a data analysis module and a control strategy generation module; the data storage module establishes a "knowledge base" of historical and real-time data to provide a reference for analysis and decision-making; the data analysis module quickly locates water quality or equipment abnormalities through intelligent algorithms; and the control strategy generation module dynamically adjusts process parameters based on preset rules and algorithms to achieve automated and intelligent control of the sewage treatment process, optimize treatment effects and reduce energy consumption.

[0021] The data storage module is used to store and receive data information of the monitoring unit and store the set normal operation data information and processing parameter thresholds;

[0022] The data analysis module is used to compare and analyze the received data information with the stored normal operation data information to identify water quality anomalies and equipment failures;

[0023] The control strategy generation module generates control instructions for the sewage treatment process based on the data analysis results and the preset treatment strategy, and adjusts the equipment operating parameters of the sewage treatment site.

[0024] The present invention further provides that the processing unit also includes an abnormality warning module, which is used to transmit abnormal data analyzed by the data analysis module and issue different levels of warning information based on the abnormality level. The abnormality warning module uses a graded response mechanism to distinguish between minor abnormalities (water quality indicators approaching thresholds) and serious failures (equipment shutdowns). Low-level warnings can alert operations and maintenance personnel in advance and prevent deterioration; high-level warnings immediately trigger emergency response procedures, such as activating backup equipment or switching treatment processes, to reduce accident losses and ensure the stability and safety of sewage treatment.

[0025] The present invention is further configured such that the management unit includes a display module, an abnormality reminder module and an operation and maintenance management module;

[0026] The display module is used to display the real-time data information, historical data trends and abnormal warning information received from the sewage treatment site;

[0027] The abnormality reminder module is used to issue an abnormality reminder warning alarm;

[0028] The operation and maintenance management module is used to manage the operation and maintenance process, including dispatching maintenance work orders, tracking processing progress, and managing equipment maintenance plans. The display module presents data in intuitive forms such as charts and curves, allowing managers to quickly understand the system's operating status. The abnormality reminder module ensures timely delivery of abnormal information through multiple channels such as pop-up windows and text messages. The operation and maintenance management module implements functions such as work order dispatching and progress tracking, optimizing the operation and maintenance process, reducing manual scheduling costs, and improving operation and maintenance efficiency and management standardization.

[0029] The present invention is further configured such that the control method of the processing unit is as follows:

[0030] Step S1: Acquire water quality parameters and equipment status data through the monitoring unit;

[0031] Step S2: The acquired data is input into the processing unit, and compared with the internally preset normal operating data and process parameter thresholds through the data storage module. The data analysis module analyzes the data change trend to obtain the analysis results;

[0032] Step S3: Based on the analysis results, the control strategy generation module formulates a sewage treatment process control strategy based on the dynamic adjustment algorithm, real-time monitoring data and historical operation data, and dynamically adjusts the sewage treatment process parameters;

[0033] Step S4: Send the control instructions to the corresponding sewage treatment site equipment to achieve real-time adjustment of the sewage treatment process, and send abnormal warning information to the management unit.

[0034] The present invention is further configured such that the steps of establishing the dynamic adjustment algorithm in step S are as follows:

[0035] Step A1: Temporally and spatially align the water quality parameters, equipment status data, and environmental parameters collected by the monitoring units to construct a multidimensional dataset. Based on the MBBR process kinetic model, a data-driven-mechanism fusion model is established to quantify the mapping relationship between process parameters and treatment effects.

[0036] The real-time water quality parameters collected are COD, ammonia nitrogen, and total phosphorus concentrations; the equipment operating status data are flow rate, pressure, and temperature; the environmental parameters are air temperature and pH value; the integrated sewage treatment process mechanism is the timing control logic of the SBR process and the biofilm growth dynamics of the MBBR process, and the quantitative process parameters are the mapping relationship between aeration volume, sludge return ratio, treatment effect effluent water quality indicators, and energy consumption;

[0037] Step A2: Set a comprehensive objective function F to balance the four dimensions of water quality compliance rate, energy consumption, carbon emissions, and equipment maintenance costs. Dynamically adjust the weight coefficients W1-W4 to adapt to different application scenarios.

[0038] The four dimensions are water quality compliance rate: loss function based on comparison between discharge standards and real-time monitoring data;

[0039] Energy consumption model: predict the energy consumption under different process parameters;

[0040] Carbon emissions calculation: covering the carbon footprint of energy consumption and pharmaceutical use;

[0041] Maintenance cost estimation: Relationship model between equipment operating time and failure probability;

[0042] Step A3: Set process operation constraints and embed emission constraints. Use the DRL deep reinforcement learning algorithm and Markov decision process modeling to achieve optimal control strategy learning in a continuous state space.

[0043] Set the process operation constraints as physical limitations of equipment parameters; water quality gradient limitations; equipment start and stop frequency constraints; embed emission constraints to ensure that the effluent indicators strictly meet local or national standards, and then use the DRL deep reinforcement learning algorithm to design a reward function R to guide the system to find the best solution under the constraints.

[0044] Step A4: Establish an incremental learning mechanism, design anomaly detection and transfer learning modules, and build a domain knowledge graph;

[0045] An incremental learning mechanism is established to continuously update model parameters using new data, balancing the weight of historical data and real-time information through a forgetting factor. An anomaly detection and transfer learning module is designed to quickly call optimization strategies for similar historical scenarios when a sudden change in water quality is detected. Parameters are fine-tuned through transfer learning to avoid the time cost of training from scratch.

[0046] Constructing a domain knowledge graph to transform sewage treatment expert experience into computable rule constraints and embed them into the algorithm decision-making process;

[0047] Step A5: Implement a rolling optimization strategy, adopt a multi-timescale control architecture, and establish an execution feedback mechanism.

[0048] The rolling optimization strategy uses a 15-minute control cycle to predict the process status for the next 24 hours based on current data;

[0049] The multi-timescale control architecture is as follows: minute-level: rapid response to sudden water quality changes, such as activating emergency regulating ponds; hour-level: optimization of process operating parameters, such as adjusting the duration of the SBR reaction phase; weekly level: updating model weights and constraints to adapt to seasonal water quality fluctuations;

[0050] Establish an execution feedback mechanism: compare control instructions with actual results, calculate deviations and correct algorithm parameters to form a closed-loop control loop of "prediction-execution-feedback-optimization".

[0051] The beneficial effects of the present invention are as follows:

[0052] 1. This invention adopts a distributed architecture to replace traditional centralized management and control, breaking the geographical space and scale limitations, supporting flexible access to sewage treatment sites in different regions and sizes, significantly reducing the cost and complexity of system deployment. Each unit realizes data sharing and collaborative processing through interconnection, and remote areas or small sites can also obtain efficient management and control, promoting the balanced development of urban and rural sewage treatment, and improving the overall applicability and scalability of the system.

[0053] 2. This invention adopts a modular closed-loop design of "monitoring-analysis-management". The monitoring unit collects equipment status and water quality parameters in real time, providing an accurate data foundation for the system. The processing unit uses data analysis and dynamic adjustment algorithms to quickly identify anomalies and generate optimization strategies, such as strengthening biochemical treatment for COD exceeding the standard and optimizing equipment scheduling when energy consumption is too high. The management unit ensures command execution and operation and maintenance management. The three work together to realize automated and intelligent control of the entire sewage treatment process, effectively improving treatment efficiency and ensuring stable and standard water quality.

[0054] 3. The system supports integration with energy-saving and consumption-reducing wastewater treatment technologies such as SBR and MBBR, as well as ozone catalytic oxidation intelligent disinfection technology. By collecting process parameters in real time to assist in optimization and control, it provides data support and functional verification for technology research and development, accelerating the application of new technological achievements. Furthermore, while meeting wastewater treatment needs, it reduces system operating energy consumption through energy optimization strategies, helping achieve carbon emission reduction goals and promoting a green and low-carbon transformation of the wastewater treatment industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 is a system diagram of the present invention;

[0056] Figure 2 is a system diagram of the monitoring unit of the present invention;

[0057] Figure 3 is a system diagram of the processing unit of the present invention;

[0058] Figure 4 It is a system diagram of the management unit of the present invention. DETAILED DESCRIPTION

[0059] The present invention will be described in further detail below with reference to the accompanying drawings and specific embodiments. The embodiments of the present invention are provided for purposes of illustration and description and are not intended to be exhaustive or to limit the invention to the disclosed forms. Many modifications and variations will be apparent to those skilled in the art. The embodiments are chosen and described to better illustrate the principles of the invention and its practical application, and to enable those skilled in the art to understand the invention and design various embodiments with various modifications suitable for specific applications.

[0060] Example:

[0061] See also Figure 1 - Figure 4 The present invention provides a technical solution: a decentralized sewage treatment process control system, comprising: a monitoring unit 100, a processing unit 200 and a management unit 300, wherein:

[0062] The monitoring unit 100 is used for equipment status monitoring and water quality parameter detection at the sewage treatment site;

[0063] The processing unit 200 is used to receive the detected data information, analyze the received data information, and generate a processing strategy for abnormal conditions. The processing unit 200 is connected to the monitoring unit 100;

[0064] The management unit 300 is used to remotely receive data information from the processing unit 200 and provide abnormal reminders, information display, and operation and maintenance management functions. The management unit 300 is connected to the processing unit 200;

[0065] The monitoring unit 100, the processing unit 200 and the management unit 300 are interconnected through a distributed architecture, supporting access to sewage treatment sites of different sizes and geographical locations.

[0066] The monitoring unit 100 includes an equipment status monitoring module 110 and a water quality parameter monitoring module 120;

[0067] The equipment status monitoring module 110 is used to monitor the equipment operating status parameters of the sewage treatment site in real time;

[0068] The water quality parameter detection module 120 is used to detect sewage water quality parameters in real time.

[0069] The water quality parameter detection module 120 includes distributed water quality sensors, which use Internet of Things technology to achieve data transmission and support remote calibration and fault diagnosis.

[0070] The processing unit 200 includes a data storage module 210, a data analysis module 220, and a control strategy generation module 230;

[0071] The data storage module 210 is used to store and receive data information of the monitoring unit 100 and store the set normal operation data information and processing parameter thresholds;

[0072] The data analysis module 220 is used to compare and analyze the received data information with the stored normal operation data information to identify water quality anomalies and equipment failures;

[0073] The control strategy generation module 230 generates control instructions for the sewage treatment process based on the data analysis results and the preset treatment strategy, and adjusts the equipment operating parameters of the sewage treatment site.

[0074] The processing unit 200 further includes an abnormal warning module 240 , which is used to transmit the abnormal data analyzed by the data analysis module 220 and issue different levels of warning information according to the abnormality level.

[0075] The management unit 300 includes a display module 310, an abnormality reminder module 320 and an operation and maintenance management module 330;

[0076] The display module 310 is used to display the real-time data information, historical data trends and abnormal warning information received from the sewage treatment site;

[0077] The abnormality reminder module 320 is used to issue abnormality reminder warning alarms;

[0078] The operation and maintenance management module 330 is used to implement the operation and maintenance process management functions of operation and maintenance work order distribution, processing progress tracking and equipment maintenance plan management.

[0079] The control method of the processing unit 200 is as follows:

[0080] Step S1: Acquire water quality parameters and equipment status data through the monitoring unit 100;

[0081] Step S2: The acquired data is input into the processing unit 200, and compared with the internally preset normal operating data and process parameter thresholds through the data storage module 210. The data analysis module 220 analyzes the data change trend to obtain the analysis results;

[0082] Step S3: Based on the analysis results, the control strategy generation module 230 formulates a sewage treatment process control strategy based on the dynamic adjustment algorithm, the real-time monitoring data and the historical operation data, and dynamically adjusts the sewage treatment process parameters;

[0083] Step S4: Sending control instructions to corresponding sewage treatment site equipment to achieve real-time adjustment of the sewage treatment process, and sending abnormal warning information to the management unit 300.

[0084] The steps for establishing the dynamic adjustment algorithm in step S3 are as follows:

[0085] Step A1: The water quality parameters, equipment status data, and environmental parameters collected by the monitoring unit 100 are aligned in time and space to construct a multidimensional dataset. Based on the MBBR process kinetic model, a data-driven-mechanism fusion model is established to quantify the mapping relationship between process parameters and treatment effects.

[0086] Step A2: Set a comprehensive objective function F to balance the four dimensions of water quality compliance rate, energy consumption, carbon emissions, and equipment maintenance costs. Dynamically adjust the weight coefficients W1-W4 to adapt to different application scenarios.

[0087] Step A3: Set process operation constraints and embed emission constraints. Use the DRL deep reinforcement learning algorithm and Markov decision process modeling to achieve optimal control strategy learning in a continuous state space.

[0088] Step A4: Establish an incremental learning mechanism, design anomaly detection and transfer learning modules, and build a domain knowledge graph;

[0089] Step A5: Implement a rolling optimization strategy, adopt a multi-timescale control architecture, and establish an execution feedback mechanism.

[0090] Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field and related fields without making creative work should fall within the scope of protection of the present invention. Structures, devices, and operating methods not specifically described and explained in the present invention are implemented in accordance with conventional means in the field unless otherwise specified or limited.

Claims

1. A decentralized sewage treatment process control system, characterized in that: include: A monitoring unit (100), a processing unit (200) and a management unit (300), wherein: The monitoring unit (100) is used for equipment status monitoring and water quality parameter detection at a sewage treatment site; The processing unit (200) is used to receive the detected data information, analyze the received data information, and generate a processing strategy for abnormal conditions. The processing unit (200) is connected to the monitoring unit (100); The management unit (300) is used to remotely receive data information from the processing unit (200) and provide abnormality reminders, information display, and operation and maintenance management functions. The management unit (300) is connected to the processing unit (200).

2. A decentralized sewage treatment process control system according to claim 1, characterized in that: The monitoring unit (100), the processing unit (200) and the management unit (300) are interconnected through a distributed architecture, supporting access to sewage treatment sites of different sizes and geographical locations.

3. A decentralized sewage treatment process control system according to claim 1, characterized in that: The monitoring unit (100) comprises an equipment status monitoring module (110) and a water quality parameter detection module (120); The equipment status monitoring module (110) is used to monitor equipment operating status parameters of the sewage treatment site in real time; The water quality parameter detection module (120) is used to detect sewage water quality parameters in real time.

4. A decentralized sewage treatment process control system according to claim 3, characterized in that: The water quality parameter detection module (120) comprises distributedly arranged water quality sensors, which use Internet of Things technology to achieve data transmission and support remote calibration and fault diagnosis.

5. A decentralized sewage treatment process control system according to claim 1, characterized in that: The processing unit (200) includes a data storage module (210), a data analysis module (220) and a control strategy generation module (230); The data storage module (210) is used to store and receive data information of the monitoring unit (100) and store set normal operation data information and processing parameter thresholds; The data analysis module (220) is used to compare and analyze the received data information with the stored normal operation data information to identify water quality anomalies and equipment failures; The control strategy generation module (230) generates control instructions for the sewage treatment process based on the data analysis results and the preset treatment strategy, and adjusts the equipment operating parameters of the sewage treatment site.

6. A decentralized sewage treatment process control system according to claim 5, characterized in that: The processing unit (200) further comprises an abnormal warning module (240), wherein the abnormal warning module (240) is used to transmit abnormal data analyzed by the data analysis module (220) and issue warning information of different levels according to the abnormality level.

7. A decentralized sewage treatment process control system according to claim 1, characterized in that: The management unit (300) includes a display module (310), an abnormality reminder module (320) and an operation and maintenance management module (330); The display module (310) is used to display the received real-time data information, historical data trends and abnormal warning information of the sewage treatment site; The abnormality reminder module (320) is used to issue an abnormality reminder warning alarm; The operation and maintenance management module (330) is used to implement the operation and maintenance process management functions of operation and maintenance work order dispatching, processing progress tracking and equipment maintenance plan management.

8. A decentralized sewage treatment process control system according to claim 1, characterized in that: The control method of the processing unit (200) is as follows: Step S1: obtaining water quality parameters and equipment status data through the monitoring unit (100); Step S2: inputting the acquired data into the processing unit (200), comparing the data with internally preset normal operating data and process parameter thresholds through the data storage module (210), and analyzing the data change trend by the data analysis module (220) to obtain analysis results; Step S3: Based on the analysis results, the control strategy generation module (230) formulates a sewage treatment process control strategy according to the dynamic adjustment algorithm, the real-time monitoring data and the historical operation data, and dynamically adjusts the sewage treatment process parameters; Step S4: Send the control instructions to the corresponding sewage treatment site equipment to achieve real-time adjustment of the sewage treatment process, and send abnormal warning information to the management unit (300).

9. A decentralized sewage treatment process control system according to claim 1, characterized in that: The steps for establishing the dynamic adjustment algorithm in step S3 are as follows: Step A1: aligning the water quality parameters, equipment status data and environmental parameters collected by the monitoring unit (100) in time and space, constructing a multidimensional data set, and establishing a data-driven-mechanism fusion model based on the MBBR process kinetic model to quantify the mapping relationship between process parameters and treatment effects; Step A2: Set a comprehensive objective function F to balance the four dimensions of water quality compliance rate, energy consumption, carbon emissions, and equipment maintenance costs. Dynamically adjust the weight coefficients W1-W4 to adapt to different application scenarios. Step A3: Set process operation constraints and embed emission constraints. Use the DRL deep reinforcement learning algorithm and Markov decision process modeling to achieve optimal control strategy learning in a continuous state space. Step A4: Establish an incremental learning mechanism, design anomaly detection and transfer learning modules, and build a domain knowledge graph; Step A5: Implement a rolling optimization strategy, adopt a multi-timescale control architecture, and establish an execution feedback mechanism.

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