Sewage plant supervision system based on digital twinning

Abstract

The invention relates to the technical field of sewage treatment supervision, and discloses a sewage plant supervision system based on digital twinning. The system comprises an operation mirror image generation module which collects data in real time and generates a digital twin mirror image of the sewage treatment facility; the virtual equipment state library construction module is used for extracting key equipment parameters based on the digital twin mirror image and dynamically updating a virtual equipment state library; the strategy optimization module is used for analyzing a sewage flow state and a pollutant migration path and generating a virtual operation strategy optimization parameter set; the abnormal response engine module is used for monitoring abnormal fluctuation of key nodes and generating an abnormal response intervention data set; and the efficiency improvement module is used for optimizing virtual processing path regulation and control parameters and generating a pollutant removal efficiency improvement scheme. According to the system, dynamic monitoring, intelligent optimization and quick response of the whole sewage treatment process are achieved through the digital twinning technology, supervision accuracy and timeliness are improved, and the system adapts to complex and changeable operation conditions.

Description

Technical Field

[0001] This invention relates to the field of wastewater treatment monitoring technology, specifically a wastewater treatment plant monitoring system based on digital twins. Background Technology

[0002] Currently, wastewater treatment plant supervision largely relies on a combination of traditional manual inspections and decentralized sensor monitoring, making it difficult to achieve precise control over the entire wastewater treatment process. Regarding equipment operation status monitoring, wastewater treatment facilities include various devices such as screens, sedimentation tanks, and bioreactors. The operating status of each device is interconnected; a failure or performance degradation of a single device can trigger a chain reaction, leading to a decrease in overall treatment efficiency. However, existing monitoring methods typically only obtain isolated operational data from a portion of the equipment, lacking analysis of the synergistic effects between devices and failing to promptly identify systemic problems caused by equipment degradation.

[0003] In terms of operational strategy adjustments, wastewater flow and pollutant concentration exhibit significant dynamic changes. For example, the intermittent nature of industrial wastewater discharge and the periodic fluctuations in domestic sewage flow lead to complex and ever-changing operating conditions for the treatment system. Traditional dosing strategies and flow control are mostly based on experience-based fixed parameter settings, making it difficult to dynamically optimize according to real-time operating conditions. This often results in problems such as excessive dosage leading to waste, or insufficient dosage leading to substandard treatment. Furthermore, when system anomalies occur, such as sudden pressure changes due to pipe blockage or abnormal flow fluctuations, existing response mechanisms rely heavily on manual judgment and delayed adjustments, making it difficult to quickly locate the anomaly and take targeted measures. This can cause a sharp drop in pollutant removal efficiency and even trigger environmental risks.

[0004] Existing monitoring systems lack the ability to simulate and predict the entire wastewater treatment process, making it impossible to assess the impact on overall efficiency before adjusting equipment operating parameters, leading to blind optimization strategies. With increasingly stringent environmental requirements and the continuous expansion of wastewater treatment scale, the shortcomings of traditional monitoring models in terms of accuracy, timeliness, and economy are becoming increasingly apparent, necessitating an integrated monitoring solution capable of achieving dynamic monitoring, intelligent optimization, and rapid response throughout the entire process. Summary of the Invention

[0005] The purpose of this invention is to provide a wastewater treatment plant monitoring system based on digital twins to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides a wastewater treatment plant monitoring system based on digital twins, the system comprising:

[0007] The image generation module is used to collect real-time operation data packets of various equipment in the wastewater treatment facility. Combined with wastewater flow distribution data and pollutant concentration change rate, it analyzes the degree of deviation between the actual operating status and the preset operating benchmark, and generates a digital twin image of the wastewater treatment facility.

[0008] The virtual device status database construction module is used to extract the operating wear coefficient and energy consumption fluctuation value of key equipment based on the digital twin image, assess the impact of equipment degradation on virtual operation stability, and dynamically update the virtual device status database.

[0009] The strategy optimization module is used to analyze the sewage flow distribution characteristics and pollutant migration paths based on the virtual device state library, adjust the dosing point distribution and dosing rate of the virtual dosing strategy, and generate a set of virtual operation strategy optimization parameters.

[0010] The anomaly response engine module is used to optimize the parameter set based on the virtual operation strategy, monitor the pressure mutation rate and abnormal flow fluctuation of key nodes during virtual operation, identify the impact threshold of the abnormal fluctuation range on processing efficiency, dynamically correct the virtual dosing path and flow control parameters, and generate anomaly response intervention dataset.

[0011] The efficiency improvement module is used to calculate the removal efficiency change curve and virtual processing time of the target pollutant based on the abnormal response intervention dataset, optimize the control parameters of the virtual processing path, and generate a pollutant removal efficiency improvement scheme.

[0012] Preferably, the operation steps of the running image generation module are as follows:

[0013] Real-time access to the operation data packets of the wastewater treatment facility, extraction of wastewater flow distribution data, equipment energy consumption fluctuation values ​​and pollutant concentration change rates, setting of continuous monitoring periods, and matching of operating parameters at the same time;

[0014] By comparing the dynamic correlation between wastewater flow distribution data and equipment energy consumption fluctuations, the coupling difference between flow deviation and energy consumption fluctuations is calculated. The flow distribution curve and energy consumption distribution curve are corrected by combining the pollutant concentration change rate, and the deviation between the actual operating state and the preset operating benchmark is obtained.

[0015] Based on the deviation, a digital twin mirror construction process is performed, mirror stability verification standards are set, the state offset under differentiated flow conditions is evaluated based on the overall facility operation trend, the stability tolerance threshold is compared and the mirror update frequency is optimized, and a digital twin mirror of the sewage treatment facility is generated.

[0016] Preferably, the operation steps of the virtual device state library construction module are as follows:

[0017] Based on the digital twin image, the wear transmission path and virtual stability decay trend of the sewage treatment facility under dynamic operating conditions are analyzed. The wear coefficient of key equipment is weighted and fused to generate the initial virtual equipment status update requirements.

[0018] Based on the initial virtual device status update requirements, diagnose the virtual energy balance status among multiple devices, identify the correlation between energy transmission efficiency and load distribution among devices, and correct the virtual device status parameters.

[0019] Integrate the corrected virtual device status parameters with the image stability verification results, assign virtual energy consumption control weights among multiple devices, match virtual operation stability constraints, and generate a dynamically updated virtual device status library.

[0020] Preferably, the operation steps of the strategy optimization module are as follows:

[0021] The flow distribution feature dataset in the virtual device state library is called to monitor the adsorption rate and diffusion trajectory of pollutants in the virtual treatment unit. The migration path offset is inferred by combining the virtual runtime and environmental variables, and the pollutant migration path correction coefficient is defined.

[0022] The influence of the pollutant migration path correction coefficient on the virtual dosing strategy is analyzed. Based on the removal requirements of the target pollutant, the distribution density of virtual dosing points and the ratio of reagent delivery rate are adjusted to generate a set of virtual operation strategy optimization parameters.

[0023] Preferably, the operation steps of the exception response engine module are as follows:

[0024] Based on the virtual operation strategy, the parameter set is optimized, and the time-series data of the pressure mutation rate and the distribution map of abnormal flow fluctuation of the virtual operation node are captured in real time. The equipment fault interference signal is filtered out, and the average fluctuation intensity is calculated.

[0025] The combined effects of the pressure mutation rate and abnormal flow fluctuation on virtual processing efficiency are analyzed, and the efficiency attenuation under different fluctuation intensities is calculated using a preset processing efficiency model.

[0026] Based on the efficiency decay, the spatial coordinates of the virtual dosing path and the threshold values ​​of the flow control parameters are dynamically adjusted, the dosing rate and the virtual flow field control range are reallocated, and an abnormal response intervention dataset is generated.

[0027] Preferably, the operation steps of the performance enhancement module are as follows:

[0028] Based on the aforementioned abnormal response intervention dataset, the concentration decay curves of the target pollutants during the virtual treatment process are collected, the removal efficiency distribution characteristics of different virtual treatment periods are integrated, and the treatment efficiency data are classified.

[0029] Based on the removal efficiency distribution characteristics, the control parameters of the virtual treatment path are optimized, the correlation between the optimal removal efficiency range of the target pollutant and the virtual treatment time is analyzed, and the operating boundaries of the virtual dosing intensity, treatment time and flow field control parameters are adjusted.

[0030] The virtual processing is executed according to the adjusted operating boundaries, and the variable constraints of processing time, flow field control parameters and dosing intensity are controlled synchronously to generate a pollutant removal efficiency improvement plan.

[0031] Preferably, the system further includes:

[0032] The twin strategy mapping module is used to receive the pollutant removal efficiency improvement scheme, parse the conversion relationship between virtual processing path parameters and actual equipment control commands, and generate a set of physical equipment control commands.

[0033] The real-time strategy execution module is used to drive the dosing device and flow regulating valve in the sewage treatment facility to perform dynamic control actions based on the control instruction set of the physical equipment.

[0034] Preferably, the operation steps of the twin strategy mapping module are as follows:

[0035] Extract the virtual dosing intensity parameters and virtual flow field control parameters from the pollutant removal efficiency improvement scheme, and match the mapping table between the power conversion coefficient of the physical dosing pump and the opening degree of the regulating valve;

[0036] By converting the virtual processing duration parameter into the actual processing cycle duration ratio, the continuous control duration and frequency switching point of the physical equipment are calculated.

[0037] Integrate power conversion coefficients, opening degree mapping tables, and frequency switching points to generate a set of control instructions for physical equipment.

[0038] Preferably, the operation steps of the real-time policy execution module are as follows:

[0039] Decompose the dosing pump power command and regulating valve opening command in the physical equipment control command set, and allocate command execution priority according to time sequence;

[0040] Send a power gradient adjustment signal to the dosing device and simultaneously send an opening gradual control signal to the flow regulating valve;

[0041] Monitor the actual power output value and valve opening feedback value during the execution of the physical equipment, and generate the execution status feedback package of the physical equipment.

[0042] Preferably, the system further includes:

[0043] The feedback closed-loop calibration module is used to receive the execution status feedback packet of the physical device, compare the deviation between the preset execution target and the actual execution effect, and calculate the execution deviation of the virtual operation strategy.

[0044] The mirror iteration module is used to correct the flow distribution benchmark parameters and equipment degradation prediction model in the digital twin mirror based on the deviation amount of the virtual operation strategy, and update the degradation evaluation rules of the virtual equipment state library.

[0045] Compared with the prior art, the beneficial effects of the present invention are:

[0046] By constructing a digital twin of the wastewater treatment facility, a global perception and precise mapping of the equipment's operational status is achieved. The operational image generation module combines various equipment operation data packages, wastewater flow distribution data, and pollutant concentration change rates to comprehensively analyze the deviation between the actual operating status and preset benchmarks. This overcomes the limitations of traditional monitoring methods, which rely on isolated data and insufficient correlation analysis. It allows managers to intuitively grasp the overall operational status of the system and promptly identify potential problems.

[0047] The dynamic updating of the virtual device status database, based on digital twin images, extracts the operational wear coefficients and energy consumption fluctuations of key equipment, deeply assesses the impact of equipment degradation on virtual operational stability, and provides detailed data for equipment maintenance and performance optimization. This continuous tracking and analysis of equipment status can predict equipment performance degradation trends in advance, avoid processing interruptions caused by sudden equipment failures, extend the effective operating cycle of equipment, and reduce losses from unplanned downtime.

[0048] The strategy optimization module relies on a virtual equipment state library to analyze the wastewater flow distribution characteristics and pollutant migration paths, dynamically adjusting the dosing points and rates of the dosing strategy to generate an optimized parameter set. This process overcomes the limitations of traditional empirical parameter setting, enabling precise matching of dosing and flow control to real-time operating condition changes. While ensuring pollutant removal efficiency, it avoids reagent waste and excessive energy consumption, achieving a balance between economy and efficiency in the treatment process.

[0049] The anomaly response engine module monitors the pressure mutation rate and abnormal flow fluctuations at key nodes to quickly identify the threshold impact of abnormal fluctuation ranges on processing efficiency. It then dynamically adjusts the dosing path and flow control parameters to generate an intervention dataset. This proactive and precise anomaly response mechanism significantly shortens the reaction time for anomaly handling, enabling the system to quickly contain negative impacts, maintain stable processing efficiency, and reduce the likelihood of environmental risks.

[0050] The efficiency enhancement module, based on the abnormal response intervention dataset, calculates the target pollutant removal efficiency change curve and virtual treatment time, optimizes the control parameters of the virtual treatment path, and forms an efficiency enhancement scheme. Through simulation and optimization of the treatment process, this module can tap into the system's potential, continuously improve the treatment path under complex operating conditions, make the pollutant removal effect more stable, adapt to the treatment needs of pollutants at different times and concentrations, and improve the overall operational efficiency of the wastewater treatment plant. Attached Figure Description

[0051] Figure 1 This is a schematic diagram illustrating the working principle of the wastewater treatment plant monitoring system based on digital twins as described in this invention.

[0052] Figure 2 A flowchart illustrating the operations for running the image generation module;

[0053] Figure 3 A flowchart for the strategy optimization module operations;

[0054] Figure 4 A flowchart illustrating the operation of the exception response engine module;

[0055] Figure 5 This is a flowchart of the twin strategy mapping and real-time execution module. Detailed Implementation

[0056] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0057] Please see Figure 1 This invention provides a wastewater treatment plant monitoring system based on digital twins, the system comprising:

[0058] The core architecture comprises a runtime image generation module, a virtual device status database construction module, a strategy optimization module, an anomaly response engine module, and an efficiency improvement module. The runtime image generation module collects real-time operational data packets from the wastewater treatment facility, combining this with wastewater flow distribution and pollutant concentration change rates to create a digital twin image reflecting the actual operating status. The virtual device status database construction module extracts equipment wear coefficients and energy consumption fluctuation values ​​from the image data and dynamically updates the virtual device status parameters. The strategy optimization module analyzes pollutant migration paths based on the virtual device status and generates optimized dosing strategy parameters. The anomaly response engine module monitors sudden pressure changes and flow fluctuations at operational nodes, dynamically adjusting control parameters to form intervention plans. Finally, the efficiency improvement module optimizes treatment path control parameters to generate a pollutant removal efficiency improvement plan.

[0059] Example 1: See Figure 2 During the process of building a digital twin of the wastewater treatment facility using the image generation module, operational data packets are acquired in real time through a distributed data acquisition network deployed throughout the wastewater treatment plant. This network integrates equipment such as electromagnetic flowmeters installed in pipelines, multispectral water quality analyzers in biological treatment tanks, and power quality analyzers in pumping stations, forming a data acquisition system covering the entire process. Wastewater flow distribution data is recorded at a second-level frequency for the instantaneous flow values ​​of each process unit, and the voltage, current, and power factor data output by energy consumption monitoring devices are collected simultaneously to form equipment energy consumption fluctuation values. The pollutant concentration change rate is derived from the synchronous monitoring results of online ammonia nitrogen, COD, and total phosphorus sensors, and the system achieves millisecond-level timestamp marking through an industrial IoT gateway.

[0060] Continuous monitoring periods are managed using a sliding window mechanism, with a default system setting of 15 minutes per complete analysis cycle. After acquiring the timestamped raw data, the time-series alignment processor performs time-axis normalization of the multi-source data via edge computing nodes. This operation employs clock calibration technology based on the NTP protocol to unify the flowmeter pulse signals, analog data acquired by the PLC, and instrument serial communication data to the same time base. Within the same time slice, the correlation analysis between the longitudinal changes in flow distribution data and equipment energy consumption fluctuations is achieved through a dynamic time warping algorithm. This algorithm constructs a time-series dynamic programming matrix to identify the offset and lag time difference between flow peaks, valleys, and energy consumption fluctuation curves.

[0061] The calculation of the coupling difference between flow deviation and energy consumption fluctuation introduces a time-weighted coefficient. The system denotes the percentage deviation of the flow rate from the standard flow rate at each monitoring point as δQ, and the percentage deviation of the energy consumption value from the standard value at the corresponding time point as δP. The coupling coefficient K is defined as the covariance of δQ and δP divided by the variance of δQ, and the calculation result is mapped to a scale value from 0 to 1. The pollutant concentration change rate is used as a dynamic correction factor. When the instantaneous change in pollutant concentration exceeds a preset threshold, the recalibration procedure of the flow distribution curve is triggered. The recalibration process reconstructs the flow distribution curve through cubic spline interpolation, where the pollutant concentration gradient change is used as an interpolation constraint. The optimized curve node positions also consider the relatively stable segment of the equipment energy consumption curve.

[0062] The three-dimensional deviation matrix between the actual operating status and the preset operating baseline includes time, space, and parameter axes. The time axis records 300 continuous data points with 1 minute as the smallest unit, the space axis marks the locations of 10 key process nodes, and the parameter axis records the composite deviation values ​​of flow rate, energy consumption, and pollutant concentration. The matrix calculation adopts a tensor operation mode, integrating in the time domain to form the cumulative deviation, and weighted averaging in the spatial domain to form the location feature deviation. An operating trend prediction model based on a long short-term memory neural network is embedded in the digital twin mirror construction process. This model uses historical 24-hour operating data as a training set and the current 15-minute sliding window data as input to predict the fluctuation trend of the facility's operating parameters within the next 30 minutes.

[0063] The state offset assessment under differentiated flow conditions employs a multi-scenario simulation mechanism. The system presets three typical operating modes: a ±5% fluctuation range under stable flow conditions, a slope change rate of ±0.2% per second under gradually changing flow conditions, and an extreme case where instantaneous changes exceed 15% under flow surge conditions. Under each condition, the state offset is calculated by an integrator using the cosine of the angle between the actual parameter curve and the standard curve, combined with the area deviation component to form a comprehensive evaluation index. The mirror stability verification standard sets dual hard constraints: flow data matching degree not less than 92%, and energy consumption error not exceeding 5%. Simultaneously, four soft indicators are set: pollutant concentration correlation coefficient ≥0.85, characteristic parameter response delay ≤8 seconds, data transmission packet loss rate <0.1%, and clock synchronization error <50 milliseconds.

[0064] When the offset exceeds the stability tolerance threshold, the system initiates a dynamic mirror update mechanism. The update frequency adjustment algorithm is controlled in stages based on the offset: for a Level 1 offset (3%-5%), the update cycle is compressed to 5 minutes; for a Level 2 offset (5%-8%), the update frequency is increased to 3 minutes; and for a Level 3 offset (over 8%), a high-frequency update is initiated every 1 minute. The update operation uses differential transmission mode, transmitting only incrementally changed data. Seven quality verification nodes are set up during the mirror construction process: a data integrity check node removes abnormal time periods with a missing rate exceeding 5%; a time-series correlation node verifies clock synchronization deviations of multi-source data; a noise filtering node applies wavelet denoising algorithms to smooth the data; a trend consistency node matches the prediction model results; a logic verification node excludes data with physical rule conflicts; a stability assessment node performs tolerance boundary checks; and a history backtracking node maintains the mirror version tracking capability. All verification results form a mirror health index, and only mirror versions that reach AA level or above are included in the system application layer call queue. The mirror storage adopts a hierarchical architecture. The latest image is stored in an in-memory database for real-time access, historical images are archived to a time-series database at 30-minute intervals, and complete images are fully backed up and stored in a distributed file system at midnight every day.

[0065] Example 2: See Figure 3 When processing digital twin mirror data, the virtual device status database construction module focuses on the operational status characterization of key equipment and system energy consumption balance analysis. The equipment status analysis unit receives real-time operating parameters from the mirror data stream and, for core equipment such as aerators, sludge return pumps, and mixers, collects multi-dimensional physical quantities including vibration acceleration spectra, bearing temperature curves, and motor current waveforms. These physical quantities are continuously input at a density of 20 sample points per second and processed by a wavelet packet decomposition program integrated into the edge computing node. Wavelet packet decomposition uses a five-layer tree structure to decompose the original signal, extracting characteristic energy values ​​representing the wear state of the equipment in specific frequency bands. These characteristic energy distributions correspond to the mechanical wear modes of different components of the equipment. The system constructs a set of twelve-dimensional feature parameters, covering key indicators such as changes in spectral peak positions, the proportion of high-frequency energy, and the rate of temperature gradient change.

[0066] The generation of initial virtual device status update requirements employs a multi-level fusion strategy. A device criticality assessment matrix prioritizes device nodes in the process chain, considering factors such as the impact of device failures on the chain disruption, procurement costs, and maintenance difficulty. Based on this prioritization, the system assigns differentiated weight coefficients to different devices in the fusion calculation. The status fusion unit normalizes and weights the twelve-dimensional feature parameters of each device, outputting a comprehensive status index. This index is cross-validated with the output of a Markov chain prediction model, which, based on the device's historical state transition probability matrix, predicts the device's degradation trend path over the next 24 hours. The fusion prediction results form a structured initial status update requirement document, containing information such as the estimated remaining useful life range, recommended maintenance time windows, and maintenance priority levels.

[0067] When the energy consumption balancing diagnostic engine starts, it first establishes a virtual energy consumption network topology map of the wastewater treatment facility. Network nodes correspond to energy-consuming equipment in each process unit, and network edges represent energy transfer or process linkage relationships between equipment. The energy consumption data acquisition device extracts real-time power data of each equipment node from the digital twin mirror at a frequency of minutes, forming a dynamically updated power distribution matrix. The load allocation analysis algorithm calculates the centrality index of equipment nodes based on the network topology. The centrality index reflects the importance position of the equipment in the overall energy consumption structure. When the system calculates the energy consumption coordination efficiency between nodes, the energy consumption correlation degree between two equipment nodes is defined as the product of the correlation coefficient of the power curve and the distance weighting factor. By traversing all equipment combinations, the set of equipment combinations with coordination efficiency lower than the preset benchmark value is identified. The energy consumption redistribution optimization program redistributes the energy consumption ratio of each equipment by adjusting the operating parameters of the virtual equipment, under the process constraint of ensuring that the dissolved oxygen concentration is not lower than 2 mg / L. The gradient descent algorithm is used for iterative solution during the optimization process, and after each iteration, it is checked whether the overall energy consumption fluctuation has been reduced to an acceptable range.

[0068] The dynamic update process of the virtual device state library performs two operations: data alignment and weight allocation. After receiving the corrected virtual device state parameters, the state parameter corrector initiates a timestamp alignment procedure to match the mirror stability verification result data stream. The alignment operation is completed based on nanosecond-level precision time reference tags, ensuring the spatiotemporal consistency between physical state parameters and system stability assessment conclusions. The allocation process of multi-device energy consumption control weights applies fuzzy control theory to handle uncertainties. The system defines three input variables: device power deviation, process stability coefficient, and device wear index, as well as an output variable: energy consumption control weight allocation coefficient. The fuzzification stage transforms the input quantities into membership function values, performs inference and decision-making through 128 preset fuzzy rules, and finally obtains precise control weight values ​​through centroid defuzzification. These weight values ​​are loaded into the state library update controller, forming the update priority order of each device's state parameters.

[0069] The virtual device status database employs a multi-level index architecture. The base layer stores time-series data of the original device status parameters; the feature layer stores feature vector sets processed by wavelet packets; the prediction layer retains degradation trend data output by the Markov model; and the relationship layer maintains the energy consumption correlation matrix between devices. The status retrieval interface supports six access modes: precise device ID query, status level range retrieval, prediction time window filtering, associated device status group query, historical concurrent status comparison, and abnormal status combination pattern recognition. Each status update operation triggers a three-layer verification mechanism: the data integrity check module verifies the input parameter dimensions; the logical rule verification module excludes status combinations that violate process principles; and the trend consistency detection module compares the continuity of the current update with historical status curves. Verified status records are assigned version identifiers and automatically generate incremental difference logs while being stored in the time-series database. The system has an automatic status database archiving mechanism, compressing and marking expired data every 12 hours and performing a full database data consistency check monthly. The abnormal status backtracking function supports point-to-point playback of virtual device status snapshots at any historical moment, with a maximum traceability period of 18 months of continuous operation of device condition change records. The status early warning module scans the characteristic parameter vectors of key equipment. When the wear index enters the warning threshold range, it triggers a level three alarm signal and outputs it to the central dispatch platform.

[0070] Example 3: See Figure 4 The anomaly response engine module operates based on a set of optimized parameters for virtual operation strategies, acquiring real-time operational status through a distributed sensor network deployed at key process nodes. The pressure monitoring unit employs an array of pressure sensors, each containing 16 miniature pressure probes, capturing pressure pulsation signals within pipelines and reactors at a 100 Hz sampling frequency. The signal preprocessing stage utilizes an adaptive filtering algorithm to eliminate equipment vibration and environmental noise interference, preserving effective pressure fluctuation characteristics. A pressure mutation rate calculator performs first-order difference operations on the filtered signal, takes the absolute value, and then smooths it using a moving average window with a width of 30 data points. The flow anomaly monitoring subsystem acquires instantaneous flow values ​​using an electromagnetic flowmeter, cross-validating data accuracy with an ultrasonic Doppler velocimeter. Flow fluctuation analysis employs an improved density clustering algorithm, which sets a dynamic neighborhood radius parameter ε to automatically adjust anomaly detection sensitivity based on current flow conditions.

[0071] The following are the standard format and symbol explanations for the formulas in Example 3:

[0072] The meanings of the symbols in the formula are explained below: The neighborhood radius is dynamically adjusted at time t (in meters). The preset neighborhood radius reference value (unit: meters); This is the sensitivity adjustment coefficient (dimensionless). The maximum design flow rate of the wastewater treatment facility (unit: cubic meters per hour). The measured flow rate at time t (unit: cubic meters per hour).

[0073] This mathematical model implements a dynamic adaptation mechanism for the flow anomaly detection threshold. This works when the flow rate approaches the design maximum value. hour, The term approaches zero, making Shrink to baseline value This enhances the sensitivity of anomaly detection; when the flow rate is low, this fractional term increases, leading to... Expand and appropriately relax testing standards. Adjustment coefficient. The value can be continuously adjusted from 0.5 to 2.0 through the system parameter configuration interface.

[0074] The processing efficiency assessment model constructs a coupled influence matrix between pressure mutation and flow fluctuation. The model input layer receives a standardized pressure mutation rate sequence P and flow fluctuation sequence F, extracting spatiotemporal features through a convolutional neural network. The feature fusion layer cross-correlates the frequency domain features of the pressure mutation rate with the temporal domain features of the flow fluctuation to calculate the composite influence factor for each monitoring point. The efficiency decay calculator uses a nonlinear mapping method to convert the composite influence factor into a percentage of processing efficiency loss. The model sets three warning thresholds: when the efficiency decay is less than 5%, only logging is recorded; a decay of 5%-10% triggers a yellow warning; and a decay exceeding 10% activates a red warning and triggers an intervention mechanism.

[0075] The dynamic adjustment engine performs real-time optimization of the virtual dosing path. After acquiring anomaly monitoring results, the path planner first constructs a three-dimensional spatial grid model, with the grid cell size set to 10 cm × 10 cm × 10 cm. The reagent diffusion simulator calculates the concentration field distribution within the grid, considering the combined effects of the fluid velocity field, turbulence intensity, and pollutant concentration gradient. The dosing point optimization algorithm, based on spatial grid data, evaluates the coverage efficiency of existing dosing points and identifies blank and overlapping areas in the concentration field. The calculation of new dosing point coordinates employs a multi-objective optimization method, simultaneously satisfying three indicators: maximizing reagent utilization, minimizing equipment adjustment frequency, and minimizing response time. An inertial damping factor is introduced during the coordinate update process to avoid system oscillations caused by frequent minor adjustments.

[0076] The flow control parameter adjustment unit runs the computational fluid dynamics simulation kernel. The simulation mesh uses unstructured tetrahedral elements, refined in the boundary layer region. The solver is set to transient calculation mode, with the time step automatically adjusted between 0.1 and 0.5 seconds based on the flow velocity. The boundary condition processing module dynamically receives real-time flow field data from the digital twin mirror as the initial conditions for the simulation. The control parameter optimization process employs the response surface methodology, constructing a three-dimensional response surface model that includes reagent mixing uniformity, energy consumption index, and pollutant removal rate. The parameter search algorithm searches for the Pareto optimal solution set on the response surface, and the final selected operating point must simultaneously satisfy the balance requirements of these three indices.

[0077] The generation process of the anomaly response intervention dataset undergoes rigorous quality control. The data verification module checks the completeness and rationality of each intervention measure, including coordinate range verification, parameter boundary checks, and temporal consistency detection. The version control system marks the differences for each intervention adjustment and retains a complete modification history. The dataset output adopts a hierarchical structure: the base layer contains raw monitoring data and alarm information; the intermediate layer records the analysis process and decision-making basis; and the application layer stores the final determined intervention measure parameters. The system has an automatic archiving mechanism, backing up the complete dataset to a distributed storage system every 24 hours and generating metadata indexes for rapid retrieval. The data interface supports standardized access protocols, allowing higher-level systems to query historical intervention records by time range, process unit, or anomaly type. Real-time data streams are pushed to relevant subsystems using a publish-subscribe model to ensure timely execution and feedback of intervention measures. The anomaly response knowledge base continuously accumulates typical cases, forming reusable response strategy templates through feature extraction and pattern classification, providing a reference for handling similar anomalies in the future.

[0078] Example 4: See Figure 5 This involves the specific process of converting virtual optimization schemes into control commands for physical equipment. When the system receives a pollutant removal efficiency improvement scheme, the digital twin strategy mapping module initiates the parameter conversion process. Taking a virtual optimization scheme for the AAO process section of a wastewater treatment plant as an example, this scheme includes dosing intensity adjustment parameters and flow field control parameters for the anaerobic and anoxic tanks. The parameter parser first extracts the virtual dosing intensity values ​​marked in the scheme; these values ​​represent the optimal reagent dosage simulated in the digital twin environment. The system searches its built-in reagent type database to match the actual chemical reagents used in the current process section, including the physicochemical properties of common water treatment reagents such as polyaluminum chloride and polyacrylamide.

[0079] The power conversion unit calls upon the corresponding conversion coefficient matrix based on the characteristics of the reagent. For example, for 10% liquid polyaluminum chloride, the system loads a pre-calibrated power-dosage relationship curve. This curve records the correspondence between the dosing pump motor power and the actual reagent delivery rate under different flow conditions. The conversion process considers factors such as reagent viscosity, pipeline resistance, and ambient temperature, and calculates an accurate power setpoint through interpolation. For powdered reagent systems, an additional dissolution rate compensation coefficient is loaded to ensure that the residence time of the solid reagent in the dissolution tank is synchronized with the adjustment of the dosing pump power.

[0080] The conversion process of flow field control parameters involves the mapping relationship of the valve opening. The system accesses the valve characteristic database to query the flow characteristic curve of the currently installed electric control valve. Taking a DN200 butterfly valve as an example, the database stores the flow coefficient Kv value corresponding to every 5° interval within the valve's opening range of 0° to 90°. The control parameter converter calculates the required flow value to be adjusted according to the flow field control requirements in the virtual scheme, and then looks up the corresponding opening angle in reverse on the characteristic curve. For valves with nonlinear flow characteristics, the system automatically processes the opening-flow relationship in segments, increasing the mapping accuracy at key inflection points. Simultaneously, considering the impact of upstream and downstream pressure changes on the actual flow rate, a real-time pressure compensation algorithm is introduced to correct the final opening command.

[0081] The conversion of processing time parameters employs a relative time scaling method. Assuming the simulated optimization processing cycle in the virtual environment is 2 hours, while the actual process requires a processing cycle of 4 hours, the system establishes a conversion relationship with a time axis scaling factor of 0.5. The timing generator scales the operation nodes in the virtual environment onto the actual time axis while maintaining the relative timing relationships of each operation step. The calculation of frequency switching points considers the mechanical response characteristics of the equipment. For example, the power adjustment of the dosing pump needs to avoid sudden and large changes. The system automatically inserts a gradual transition section during the conversion process to ensure a smooth power adjustment process.

[0082] The encapsulation of physical equipment control instruction sets adopts a standardized industrial protocol format. Each instruction includes fields such as equipment identification code, operation type, target parameter value, execution timestamp, and checksum. Taking the dosing pump control instruction as an example, the instruction structure includes: pump station number, motor power setpoint, gradual rate limit, execution start time, and CRC checksum. The regulating valve control instruction includes valve position code, target opening angle, movement speed, and acceleration parameters. The system performs topological sorting of the instruction set to ensure that equipment operations with process correlations are arranged in the correct order, such as the operation logic of adjusting flow rate before adjusting dosing quantity.

[0083] The real-time strategy execution module is deployed at the industrial control layer and connects to the equipment via a fieldbus network. After parsing the control command set, the command distributor sends the dosing pump control command to the frequency converter control unit. Upon receiving the power setpoint, the frequency converter adjusts the output frequency according to the preset acceleration curve, while simultaneously monitoring motor current feedback to ensure safe operation. Valve control commands are transmitted to the electric actuator via the PROFIBUS-DP network. The actuator has a built-in position closed-loop control algorithm that converts the opening angle setpoint into precise valve stem displacement. The system is equipped with a command execution status monitoring loop, which collects equipment feedback signals at 100-millisecond intervals to form a status data packet containing parameters such as actual power output, valve position, and operating current.

[0084] Status feedback data is transmitted back to the system monitoring center via an industrial IoT gateway. The data verification module compares the deviation between the target value of the instruction and the actual execution result, and calculates the execution accuracy index. When the deviation exceeds the allowable range, the system initiates a compensation control process. For example, if the actual power of the dosing pump deviates from the set value by more than 3%, a fine-tuning instruction is automatically sent. Historical execution data is stored in a time-series database to form a knowledge base of equipment response characteristics, which is used to optimize subsequent instruction generation strategies. The system maintains a complete operation log, recording the sending time, execution result, and environmental parameters of each control instruction, and supports replaying the entire control process along a timeline.

[0085] Taking a specific operational example, the system processed the dosing control adjustments for the secondary sedimentation tank in the virtual optimization scheme. The twin strategy mapping module identified the scheme's requirement to increase the dosage of polyferric sulfate by 15%. The conversion unit queried the viscosity compensation coefficient of the agent at the current temperature and calculated that the power of dosing pump No. 3 needed to be increased from 55kW to 63kW. The flow field control parameter converter simultaneously converted the flow rate adjustment requirement in the virtual scheme into an increase in the opening of regulating valve No. 4 from the current 45° to 52°. The timing processor scheduled the valve adjustment to be executed first, and after an 8-second delay, started the dosing pump power increase. The instruction set was transmitted to the field via a fiber optic ring network. The execution process monitoring showed that the valve smoothly reached the new position within 12 seconds, and the dosing pump power ramped up to the target value within 25 seconds. Status feedback data confirmed that the actual dosage increased by 14.8% and the flow rate increased by 9.2%, which was basically consistent with the expected effect of the virtual scheme.

[0086] Example 5: The operation of the real-time strategy execution module begins with the reception and parsing of the physical equipment control instruction set. After the instruction decomposer loads the control instruction set, it starts the process logic analysis program to identify the operational dependencies between instructions. Taking the aeration system control as an example, the system identifies a temporal correlation between the blower frequency increase instruction and the dissolved oxygen regulating valve opening instruction. Based on this, the priority allocation engine establishes an operation sequence queue: first, the valve opening adjustment is executed, and then the blower frequency is adjusted after the flow rate stabilizes. The instruction sequence generator sets a time window for each operation, and sets a time tolerance range for critical operations, such as allowing a time deviation of ±500 milliseconds for valve opening adjustment.

[0087] The control signal output unit sends operation commands to field equipment via an industrial-grade digital output module. The control of the dosing device employs a four-signal parallel output mechanism: power setpoints are transmitted via a 4-20mA analog signal; gradual gradient parameters are written to the MODBUS register; state switching commands are triggered by the DO signal; and safety interlock signals are transmitted via hard-wired connections. The gradual control algorithm plays a crucial role in signal transmission; for example, when adjusting the dosing pump power, the system generates a stepped output signal at a rate of ±2% per second. This algorithm incorporates an overshoot protection mechanism, automatically inserting a buffer step when the detected motor current change rate exceeds a threshold.

[0088] The valve control unit employs a three-stage control strategy when processing control valve commands. In the startup phase, it overcomes static friction with a low acceleration (e.g., 15° / s²). In the main motion phase, it maintains a constant speed (e.g., 5° / s) to approach the target position. In the fine-tuning phase, it switches to PID closed-loop control mode (proportional coefficient 0.8, integral time 1.2 seconds), ultimately controlling the positioning accuracy within ±0.3°. The positioning process compensates for valve hysteresis in real time, correcting the mapping relationship between the actual opening degree and position feedback using historical data.

[0089] The condition monitoring system deploys a multi-source heterogeneous sensor network to collect execution feedback. Current transformers capture real-time three-phase current fluctuations in the dosing pump motor; absolute encoders record valve shaft positions; pressure transmitters monitor pipeline system impedance changes; and thermal imagers scan the surface temperature field distribution of the equipment. The data acquisition period is set to 100 milliseconds, with each acquisition containing a waveform segment of 64 sampling points. The raw signal undergoes FIR filtering and power frequency interference suppression processing, and effective feature values ​​are extracted to generate the equipment execution status feedback packet.

[0090] The feedback data packets use a structured storage format. Each data unit contains a time stamp accurate to the microsecond level, a device identification code, a parameter type code, the original value, and a status quality indicator. Key parameters, such as the instantaneous deviation between the motor power setpoint output and the actual feedback value, are recorded in floating-point form. The system automatically marks data segments with abnormal quality; for example, when the detection signal is missing for more than three consecutive sampling cycles, an invalid data marker is triggered.

[0091] After the feedback closed-loop calibration module is activated, a spatiotemporal alignment operation is performed first. The system reorders the actual equipment execution parameters with the preset target along the time axis, and compensates for timing deviations caused by equipment response delays using a dynamic time warping algorithm. The deviation is calculated using a window sliding comparison method, with each analysis window containing data from ten consecutive execution cycles. Process parameter deviations are expressed as absolute deviations, while equipment status deviations are expressed as relative percentage deviations.

[0092] The execution deviation of the virtual strategy is represented by a three-dimensional vector model: the time dimension deviation records the operation delay segment; the spatial dimension deviation identifies the location of inconsistency in the execution of the process unit; and the parameter dimension deviation quantifies the dispersion between the set value and the actual value. The system sets an influence weight factor for each type of deviation. For example, in the biochemical treatment unit, the deviation weight of the dissolved oxygen parameter is significantly higher than that of the flow parameter.

[0093] The mirror iterative processing flow initiates a deep correction mechanism. The update of the flow distribution baseline parameters adopts an incremental iterative method: extracting the baseline velocity distribution curve from the latest digital twin mirror; calculating the velocity field correction matrix based on feedback data from the physical pipeline flowmeter; and adjusting the turbulence model parameters by region. The equipment degradation prediction model correction focuses on the calculation of the wear coefficient. The system adds motor current harmonic distortion characteristics as a new evaluation index and adjusts the weighting ratio of vibration and temperature indices in the wear coefficient calculation formula.

[0094] The degradation assessment rules for the virtual device state database are updated according to a version control protocol. The rule database retains the three most recent valid versions, and each update generates a new version number and creates an incremental change log. The revisions to the assessment rules include: optimizing the wear stage classification threshold, adding a bearing temperature transient change rate indicator, and improving the equipment collaborative degradation assessment model. The rule verification subsystem automatically initiates regression testing after an update, using three months of historical data for compatibility checks.

[0095] The ability to analyze abnormal states was applied during system operation. During one operation, the aeration system experienced a dissolved oxygen control deviation. The system retrieved virtual equipment status records from the eight hours prior to the fault. The analysis engine compared the virtual state predictions with the actual degradation trends and found that the fan bearing wear index was underestimated by 15% in the virtual environment. Based on this, the system corrected the temperature sensitivity coefficient of the wear prediction model and automatically deployed the corrected model to the new version of the status database evaluation rules. Each correction operation generated a technical specification document, recording the basis for the correction, the scope of impact, and the verification methods, which was archived in the system knowledge base for future reference.

[0096] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0097] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A digital-twin-based sewage plant supervisory system, characterized by, The system comprises: A running image generation module for collecting running data packets of various types of equipment in the sewage treatment facility in real time, analyzing the deviation degree of the actual running state from the preset running benchmark in combination with the sewage flow distribution data and the pollutant concentration change rate, and generating a digital twin mirror image of the sewage treatment facility; A virtual equipment state library construction module for extracting the running wear coefficient and energy consumption fluctuation value of the key equipment based on the digital twin mirror image, evaluating the influence of equipment degradation on virtual running stability, and dynamically updating the virtual equipment state library; A strategy optimization module for analyzing the sewage flow state distribution characteristics and pollutant migration path based on the virtual equipment state library, adjusting the distribution of dosing points and the dosage rate of the virtual dosing strategy, and generating a virtual running strategy optimization parameter set; An abnormal response engine module for monitoring the pressure mutation rate and flow abnormal fluctuation of the key nodes in the virtual running process based on the virtual running strategy optimization parameter set, identifying the influence threshold of the abnormal fluctuation range on the treatment efficiency, dynamically correcting the virtual dosing path and flow state control parameters, and generating an abnormal response intervention data set; An efficiency improvement module for calculating the removal efficiency change curve of the target pollutant and the virtual treatment time based on the abnormal response intervention data set, optimizing the regulation and control parameters of the virtual treatment path, and generating a pollutant removal efficiency improvement scheme.

2. The digital-twin-based sewage plant supervisory system according to claim 1, characterized in that, The operation steps of the running image generation module are specifically: Real-time access to the running data packets of the sewage treatment facility, extraction of sewage flow distribution data, equipment energy consumption fluctuation value and pollutant concentration change rate, setting of a continuous monitoring period, and matching of running parameters at the same time; Comparing the dynamic correlation of sewage flow distribution data and equipment energy consumption fluctuation, calculating the coupling difference between flow deviation and energy consumption fluctuation, correcting the flow distribution curve and energy consumption distribution curve in combination with the pollutant concentration change rate, and obtaining the deviation amount of the actual running state from the preset running benchmark; According to the deviation amount, perform the digital twin mirror image construction process, set the mirror image stability verification standard, evaluate the state deviation under different flow conditions, compare the stability tolerance threshold and optimize the mirror image update frequency, and generate the digital twin mirror image of the sewage treatment facility.

3. The digital-twin-based sewage plant supervisory system according to claim 2, characterized in that, The operation steps of the virtual equipment state library construction module are specifically: According to the digital twin mirror image, analyze the equipment wear transmission path and virtual stability attenuation trend of the sewage treatment facility under dynamic running conditions, perform weighted fusion calculation on the running wear coefficient of the key equipment, and generate the initial virtual equipment state update demand; Based on the initial virtual equipment state update demand, diagnose the virtual energy consumption balance state among multiple devices, identify the correlation between energy consumption transmission efficiency and load distribution among devices, and correct the virtual equipment state parameters; Integrate the corrected virtual equipment state parameters and mirror image stability verification results, allocate the virtual energy consumption regulation and control weight among multiple devices, match the virtual running stability constraint conditions, and generate a dynamically updated virtual equipment state library.

4. The digital-twin-based sewage plant supervisory system according to claim 3, characterized in that, The operation steps of the strategy optimization module are specifically: Call the flow state distribution feature data set in the virtual device state library, monitor the adsorption rate and diffusion trajectory of pollutants in the virtual processing unit, combine the virtual running time and environmental variables to speculate the migration path offset, and define the pollutant migration path correction coefficient; Analyze the influence degree of the pollutant migration path correction coefficient on the virtual dosing strategy, adjust the virtual dosing point distribution density and the proportion of the reagent injection rate according to the removal demand of the target pollutant, and generate a virtual running strategy optimization parameter set.

5. The digital-twin-based sewage plant supervisory system according to claim 4, characterized in that, The operation steps of the abnormal response engine module are specifically: According to the virtual running strategy optimization parameter set, real-time capture the pressure mutation rate time sequence data and flow abnormal fluctuation amount distribution diagram of the virtual running node, filter the device fault interference signal, and calculate the average fluctuation intensity; Analyze the superimposed influence of the pressure mutation rate and flow abnormal fluctuation amount on the virtual processing efficiency, and calculate the efficiency attenuation amount under the differential fluctuation intensity by using the preset processing efficiency model; According to the efficiency attenuation amount, dynamically adjust the spatial coordinates of the virtual dosing path and the threshold value of the flow state control parameter, re-distribute the reagent injection rate and the virtual flow field control range, and generate an abnormal response intervention data set.

6. The digital-twin-based sewage plant supervisory system according to claim 5, characterized in that, The operation steps of the performance improvement module are specifically: Based on the abnormal response intervention data set, collect the concentration decay curve of the target pollutant in the virtual processing process, integrate the removal efficiency distribution characteristics of different virtual processing periods, and classify the processing efficiency data; According to the removal efficiency distribution characteristics, optimize the regulation and control parameters of the virtual processing path, analyze the correlation between the optimal removal efficiency interval of the target pollutant and the virtual processing time, and adjust the operation boundary of the virtual dosing strength, processing time and flow field control parameter; According to the adjusted operation boundary, execute the virtual processing process, synchronously control the variable constraint relationship among the processing time, flow field control parameter and dosing strength, and generate a pollutant removal performance improvement scheme.

7. The digital-twin-based sewage plant supervisory system according to claim 1, characterized in that, The system further comprises: A twin strategy mapping module is configured to receive the pollutant removal performance improvement scheme, analyze the conversion relationship between the virtual processing path parameters and the actual device control instructions, and generate an entity device regulation and control instruction set; A real-time strategy execution module is configured to drive the dosing device and flow regulating valve in the sewage treatment facility to perform dynamic control actions based on the entity device regulation and control instruction set.

8. The digital-twin-based sewage plant supervisory system according to claim 7, characterized in that, The operation steps of the twin strategy mapping module are specifically: Extract the virtual dosing strength parameter and virtual flow field control parameter in the pollutant removal performance improvement scheme, match the power conversion coefficient of the entity dosing pump and the opening mapping table of the regulating valve; Through the length ratio conversion relationship between the virtual processing time parameter and the actual processing period, calculate the continuous regulation and control time length and frequency switching point of the entity device; Integrate the power conversion coefficient, opening mapping table and frequency switching point to generate an entity device regulation and control instruction set.

9. The digital-twin-based sewage plant supervisory system according to claim 8, characterized in that, The operation steps of the real-time strategy execution module are specifically: Decompose the dosing pump power instruction and regulating valve opening instruction in the entity device regulation and control instruction set, and allocate the instruction execution priority according to the time sequence; Send the power gradient adjustment signal to the dosing device, and synchronously send the opening gradual control signal to the flow regulating valve; The monitoring entity device monitors actual power output values and valve opening feedback values during execution of the physical device to generate a physical device execution state feedback package.

10. The digital-twin-based sewage plant supervisory system according to claim 9, characterized in that, The system further comprises: A feedback closed-loop calibration module receives the physical device execution state feedback package, compares a preset execution target with an actual execution effect deviation, and calculates a virtual execution strategy execution deviation. A mirror iteration module corrects flow state distribution benchmark parameters and device degradation prediction models in the digital twin mirror based on the virtual execution strategy execution deviation, and updates degradation evaluation rules of the virtual device state library.

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