Settlement system based on intelligent monitoring

Abstract

The invention provides a sedimentation system based on intelligent monitoring, which comprises a water quality monitoring device used for collecting multi-dimensional water quality parameter data of a sedimentation tank in real time and transmitting the data to a central processing platform; the digital twinborn module is used for constructing and operating a dynamic digital twinborn body of the sedimentation tank, performing state synchronization according to the received multi-dimensional water quality parameter data, and performing simulation deduction of future operation effects on different input dredging strategies to obtain predicted values of various performance indexes; the adaptive optimization control module is used for receiving the multi-dimensional water quality parameter data and driving the digital twin module to update the state to generate a plurality of candidate dredging strategies; and the candidate dredging strategies are input into the digital twin module for simulation deduction, the optimal strategy is selected from the candidate dredging strategies according to the obtained predicted values of all performance indexes and converted into a control instruction, the control instruction is sent to the dredging device, the control instruction is responded, corresponding dredging operation is executed, and the dredging efficiency and the intelligent level are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of sedimentation control, and in particular to a sedimentation system based on intelligent monitoring. BACKGROUND

[0002] In the field of municipal water supply and industrial wastewater treatment, sedimentation tanks are key structures for solid-liquid separation. Their operating efficiency directly affects the effluent quality and the load of subsequent treatment units. During the operation of the sedimentation tank, sludge will continuously deposit at the bottom of the tank and must be removed periodically, i.e., dredging, to prevent excessive sludge accumulation, which can reduce the effective volume and deteriorate the effluent quality, and even cause equipment damage. Currently, common automatic dredging control methods mainly fall into two categories: one is time-based control, i.e., starting the dredging device according to a preset period; the other is control based on a simple threshold, such as using a sludge interface instrument to monitor the sludge level and triggering dredging when the sludge level exceeds a certain set value.

[0003] However, time-based control lacks responsiveness to the actual operating conditions of the sedimentation tank, which can lead to insufficient or excessive dredging. Control based on a simple threshold, although improved, can only take action after the sludge level has become excessively high, lacking foresight. SUMMARY

[0004] The present application provides a sedimentation system based on intelligent monitoring to solve the problem of low sedimentation efficiency and poor intelligence level caused by relying on time or a simple threshold for existing sedimentation control.

[0005] In one aspect, the present application provides a sedimentation system based on intelligent monitoring, which includes a sedimentation tank, a dredging device, and a water quality monitoring device, and further includes a central processing platform, wherein the central processing platform includes a digital twin module and an adaptive optimization control module. The water quality monitoring device is configured to collect multi-dimensional water quality parameter data of the sedimentation tank in real time and transmit the data to the central processing platform. The digital twin module is configured to construct and run a dynamic digital twin of the sedimentation tank, synchronize the state according to the received multi-dimensional water quality parameter data, simulate and deduce the future operating effect of different dredging strategies inputted, and obtain predicted values of various performance indicators. The adaptive optimization control module is configured to receive the multi-dimensional water quality parameter data and drive the digital twin module to update the state, generate multiple candidate dredging strategies based on the updated system state and a preset optimization target, input the candidate dredging strategies into the digital twin module for simulation and deduction, select an optimal strategy from the candidate dredging strategies according to the predicted values of various performance indicators obtained, and convert the optimal strategy into a control instruction and send it to the dredging device. The dredging device executes a corresponding dredging operation in response to the control instruction.

[0006] According to the application, a settlement system based on intelligent monitoring is provided, the digital twin module comprises a model construction unit and a model running unit; The model construction unit is configured to establish a mechanism model based on the physical structure and the settlement mechanism of the sedimentation tank, train a data-driven model using historical operation data to learn nonlinear relationships that the mechanism model fails to describe, and fuse the mechanism model and the data-driven model to form a dynamic digital twin. The model running unit is configured to receive real-time data of the monitoring device during system operation, call the dynamic digital twin, dynamically correct the output state of the mechanism model using the data-driven model therein, and achieve high-fidelity state synchronization with the physical sedimentation tank.

[0007] According to the application, a settlement system based on intelligent monitoring is provided, the digital twin module further comprises a strategy simulation unit configured to: receive a plurality of candidate dredging strategies generated by the adaptive optimization control module; use each candidate dredging strategy as an input condition to drive the dynamic digital twin to simulate the evolution process of the operating state of the sedimentation tank in a future set period; During the simulation process, the predictive values of a plurality of performance indicators corresponding to each candidate dredging strategy are calculated and output; The plurality of performance indicators include at least two or more of water quality indicators, system energy consumption indicators, and dredging equipment load indicators.

[0008] According to the application, a settlement system based on intelligent monitoring is provided, the adaptive optimization control module comprises a strategy generation unit and a decision unit; The strategy generation unit is configured to automatically generate a plurality of candidate dredging strategies that differ in dredging timing, dredging intensity, or dredging range based on the current system state synchronized by the digital twin module and a preset optimization target; The decision unit is configured to receive the predictive values of the performance indicators output by the strategy simulation unit, and select a strategy with the optimal comprehensive utility from the candidate dredging strategies as the optimal strategy according to a preset multi-objective optimization algorithm.

[0009] According to the application, a settlement system based on intelligent monitoring is provided, the dredging device comprises an execution unit and a state feedback unit; The execution unit is configured to receive the control instruction and parse the instruction to control the moving speed, travel path of the mud scraper, and the opening degree of the mud discharge valve; The state feedback unit is configured to collect operation parameters and actual states of the dredging device in real time, and send the data to the central processing platform as actual operation data.

[0010] The water quality monitoring device comprises a multi-parameter sensing unit and a data preprocessing unit. The multi-parameter sensing unit is configured to synchronously collect physical indexes, chemical indexes and biological indexes at different depths and regions in the sedimentation tank. The data preprocessing unit is configured to perform filtering, outlier rejection and timestamp alignment processing on the physical indexes, chemical indexes and biological indexes, and generate multi-dimensional water quality parameter data meeting input requirements of the digital twin module.

[0011] The water quality monitoring device comprises a multi-parameter sensing unit and a data preprocessing unit. The actual operation data and the corresponding predicted values are compared to calculate a loss function. The parameters of the data-driven model are adjusted using the loss function to realize adaptive updating.

[0012] The central processing platform further comprises a human-computer interaction unit configured to: visually display the state of the dynamic digital twin, simulation results of candidate dredging strategies and system decision basis to the user; receive a strategy adjustment instruction or an optimization target weight modification instruction manually input by the user, and transmit the instruction to the adaptive optimization control module.

[0013] The water quality monitoring device comprises a multi-parameter sensing unit and a data preprocessing unit. The state synchronization error of the digital twin is continuously monitored, and a device calibration warning is generated when the state synchronization error exceeds a preset threshold. The state feedback data of the dredging device is analyzed, and a device fault diagnosis alarm is generated when the deviation between actual operation parameters and control instructions is continuously abnormal. The device calibration warning and the device fault diagnosis alarm are pushed to the human-computer interaction unit for visual prompting, and are recorded to a system log.

[0014] The central processing platform adopts an edge-cloud collaborative computing architecture. Among them, the tasks of real-time status synchronization and dredging control, which have high requirements, are performed by edge computing devices deployed on site; while the tasks of model training and big data analysis, which have high requirements for computing resources, are performed by cloud servers.

[0015] This invention provides a sedimentation system based on intelligent monitoring. Leveraging real-time data and digital twin simulation capabilities, it proactively predicts future operating conditions and dynamically selects the dredging scheme with the optimal overall benefit from multiple candidate strategies, solving the problems of control lag and single strategy in traditional methods. By optimizing decisions under multi-objective constraints, it significantly reduces energy consumption and equipment wear caused by unnecessary dredging operations while ensuring effluent quality, thereby achieving cost reduction, efficiency improvement, and extended equipment lifespan. Compared with existing methods, it effectively enhances the level of intelligence while maintaining dredging efficiency. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of the settlement system based on intelligent monitoring provided in this embodiment. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0019] Figure 1 This is a schematic diagram of the settlement system based on intelligent monitoring provided in this embodiment.

[0020] like Figure 1 As shown in the figure, the sedimentation system based on intelligent monitoring provided in this embodiment can be widely used in various structures requiring solid-liquid separation, such as horizontal flow sedimentation tanks and radial flow sedimentation tanks in municipal water supply treatment, and vertical flow sedimentation tanks in industrial wastewater treatment. Its core achieves intelligent sludge removal control through a closed-loop process of "perception-simulation-decision-execution-feedback-optimization". The specific operation process is as follows: Before the system is officially put into use, hardware deployment, model building, and parameter configuration are completed to lay the foundation for intelligent operation throughout the entire process. The model building unit of Digital Twin Module 2 first constructs a mechanistic model describing the relationship between "water quality parameters - sludge level change - water flow state" based on the physical properties of the sedimentation tank (such as tank diameter / length, tank depth, guide wall position, sludge scraper track layout, etc.) and the core solid-liquid sedimentation mechanism (such as Stokes' law of gravity sedimentation, Terzaghi theory of sludge layer compression, etc.). At the same time, it collects historical operating data of the sedimentation tank for more than 3 years, including multi-dimensional water quality data under different influent loads (such as flow rate, pollutant concentration), climatic conditions (such as seasonal fluctuations in water temperature), sludge removal equipment operating parameters (such as sludge scraper speed, sludge removal time) and corresponding operating effects (such as effluent SS concentration, sludge accumulation rate). It uses Long Short-Term Memory (LSTM) network or random forest algorithm to train the data-driven model, focusing on learning nonlinear relationships that the mechanistic model cannot accurately describe (such as the sudden drop in sludge settling rate under low temperature conditions, and uneven sludge layer density caused by the complex composition of industrial wastewater). Finally, a weighted fusion algorithm is used to couple the mechanistic model with the data-driven model to form a dynamic digital twin that can dynamically respond to changes in operating conditions, ensuring the fidelity of its mapping to the physical sedimentation tank.

[0021] The multi-parameter sensing unit of the water quality monitoring device 3 is deployed according to a layered and zoned principle. Monitoring points are set up in the inlet, reaction, and effluent zones of the sedimentation tank. Each monitoring point has 3-5 sensor probes deployed at intervals along the water depth direction, which can simultaneously collect physical indicators (turbidity, temperature, liquid level, water flow velocity), chemical indicators (suspended solids (SS) concentration, pH value, chemical oxygen demand (COD), ammonia nitrogen concentration), and biological indicators (activated sludge concentration (MLSS), total bacterial count). The data preprocessing unit has built-in preset logic, configured with a moving average filtering algorithm to filter out sensing noise caused by water flow disturbance, adopts the 3σ principle to remove outliers caused by instantaneous probe failures, and uses a timestamp alignment algorithm to unify the acquisition timing of different probes to ensure that the output data format matches the input requirements of the digital twin. The execution unit of the dredging device 4 establishes an electrical connection with equipment such as the sludge scraper, sludge pump, and electric sludge valve. The status feedback unit is equipped with a speed sensor, torque sensor, and position encoder, which can capture equipment operating parameters (such as sludge scraper motor speed, sludge valve opening, and equipment operating current) and physical status (such as sludge scraper track position and whether there is jamming).

[0022] The central processing platform 1 adopts an edge-cloud collaborative architecture of industrial-grade edge gateways and cloud-based distributed server clusters. Tasks requiring millisecond-level response, such as status synchronization and dredging command parsing, are assigned to the edge gateways, while computationally intensive tasks such as model training and batch analysis of historical data are assigned to the cloud. Simultaneously, through the human-machine interaction unit 6, multiple optimization objectives are preset (e.g., effluent SS concentration ≤10mg / L, dredging energy consumption reduced by 20% compared to traditional methods, annual equipment wear rate ≤5%), setting digital twin status synchronization error thresholds (e.g., mud level prediction deviation ≤5cm) and equipment operation deviation judgment standards (e.g., actual rotation speed deviation from commanded rotation speed for more than 10 seconds ≥10%), thus activating the functions of the early warning and diagnosis unit and the model update unit.

[0023] Specifically, the water quality monitoring device 3 initiates the data acquisition and processing flow at a preset frequency, providing highly reliable input for the digital twin. Each probe of the multi-parameter sensing unit synchronously captures raw data of physical, chemical, and biological indicators from different depths and areas of the sedimentation tank, transmitting this data in real-time to the data preprocessing unit via industrial Ethernet. This unit first uses a Kalman filter algorithm to suppress noise in easily disturbed indicators such as turbidity and water flow velocity. Then, it uses a box plot method to remove abnormal pH fluctuations caused by probe scaling. Finally, it timestamps the data collected from different probes (e.g., aligning them to the millisecond level), generating structured multi-dimensional water quality parameter data containing "indicator type - acquisition location - acquisition time - value," which is then transmitted to the digital twin module 2 of the central processing platform 1 via an edge gateway. Layered acquisition ensures comprehensive data coverage, and multi-level preprocessing removes noise and anomalies, preventing raw data errors from distorting the digital twin's state and providing a foundation for accurate subsequent simulation and decision-making.

[0024] The edge gateway pushes the pre-processed water quality parameter data to the model running unit of the digital twin module 2 in real time, initiating the state synchronization process. The model running unit first calls the mechanistic model to calculate the theoretical operating state of the sedimentation tank based on current water quality parameters (such as influent SS concentration and water temperature), including the sludge deposition rate at the bottom of the tank, sludge layer thickness distribution, predicted effluent water quality, and flow field distribution. Then, it calls the data-driven model, dynamically correcting the parameters of the mechanistic model (such as adjusting the sludge compressibility coefficient and settling rate coefficient) based on the real-time collected deviations of "actual sludge level - theoretical sludge level" and "actual effluent turbidity - theoretical turbidity"), ensuring a high-fidelity match between the sludge level distribution and flow state output by the digital twin and the actual situation of the physical sedimentation tank (synchronization error controlled within a preset threshold). The synchronization process continues at a frequency of once every 1-5 seconds. The low-latency computing capability of the edge gateway ensures the real-time nature of state synchronization, solving the shortcomings of lagging condition perception in traditional control methods and providing an accurate digital mirror for subsequent strategy simulation.

[0025] The strategy generation unit of the adaptive optimization control module 5 receives the current system status (such as mud level in each area of ​​the pool bottom, effluent turbidity trend, and current equipment load) synchronized by the digital twin, and generates multiple differentiated candidate dredging strategies by combining the preset multi-objective optimization objectives and the genetic algorithm.

[0026] The differences in candidate strategies are mainly reflected in three dimensions: first, the timing of dredging (e.g., starting dredging immediately, starting after a 1-hour delay, starting after a 2-hour delay); second, the intensity of dredging (e.g., high / low speed of the sludge scraper, fully / partially open sludge valve, high / low frequency operation of the sludge pump); and third, the dredging range (e.g., dredging the entire pool, dredging only the high sludge level area at the center of the pool bottom, gradient dredging along the water inlet direction). For example, three typical candidate strategies are generated: Strategy 1 is "immediate full-pool dredging, high-speed operation of the sludge scraper, and full opening of the sludge valve"; Strategy 2 is "1-hour delay in dredging, cleaning only the area at the bottom of the pool with a sludge level ≥ the preset benchmark value, and medium-speed operation of the sludge scraper"; and Strategy 3 is "2-hour delay in dredging, low-intensity dredging of the entire pool, and partial opening of the sludge valve". By generating multi-dimensional candidate strategies, the limitations of the single nature of traditional control are broken, providing ample choice space for comprehensive optimal decision-making.

[0027] The strategy simulation unit of digital twin module 2 receives candidate dredging strategies and initiates future operating condition simulations using a dynamic digital twin. Each candidate strategy is used as input to drive the digital twin to simulate the evolution of the sedimentation tank's operating status over the next 24-48 hours, including the sludge level change curve with dredging operations, the fluctuation trend of effluent water quality (SS concentration, turbidity), and the energy consumption and load distribution of the dredging equipment. During the simulation, the strategy simulation unit calculates and outputs predicted values ​​of multiple performance indicators for the corresponding strategy at a minute-by-minute frequency, covering at least three core indicators: first, effluent water quality indicators (such as average effluent SS concentration and peak turbidity); second, system energy consumption indicators (such as total power consumption of the scraper and energy consumption of the sludge pump); and third, equipment load indicators (such as predicted average temperature of the scraper motor and bearing wear). Through advance simulation, a predictive assessment of the dredging effect is achieved, solving the lag problem of traditional simple threshold control that only takes action when the sludge level is too high.

[0028] The decision-making unit of the adaptive optimization control module 5 collaborates with the dredging device 4 to complete the selection and implementation of the optimal strategy. The decision-making unit receives the predicted performance indicators of each candidate strategy, calls the preset weighted TOPSIS multi-objective optimization algorithm, and performs standardized scoring and comprehensive ranking of each indicator according to preset weights (e.g., effluent quality weight 0.5, energy consumption weight 0.3, equipment load weight 0.2), selecting the optimal strategy with the lowest comprehensive utility under the premise of "effluent quality meeting the standard". For example, when the predicted effluent SS concentration of strategy 2 meets the standard, the energy consumption is reduced by 30% compared to strategy 1, and the equipment load is reduced by 15% compared to strategy 3, strategy 2 is determined to be the optimal strategy. If the influent water quality suddenly deteriorates (e.g., the influent SS concentration rises sharply), the system can automatically adjust the weights (e.g., increase the effluent water quality weight to 0.7) to prioritize water quality.

[0029] The decision-making unit transforms the optimal strategy into standardized control commands, including parameters such as the scraper's moving speed, travel path, and sludge discharge valve opening, which are transmitted to the execution unit of the dredging device 4 via an edge gateway. After parsing the commands, the execution unit adjusts the scraper motor frequency through a frequency converter to control the moving speed, drives the scraper along a preset path (such as the radial path of a radial flow sedimentation tank or the reciprocating path of a horizontal flow sedimentation tank) through a servo motor, and controls the sludge discharge valve opening through an electric regulating valve. During operation, it avoids obstacles such as the tank bottom support structure and sensor installation positions, ensuring precise and efficient dredging operations. Through multi-objective optimization decision-making, ineffective dredging actions are reduced while ensuring effluent water quality, significantly reducing energy consumption and equipment wear, directly achieving cost reduction, efficiency improvement, and extended equipment lifespan.

[0030] The dredging device 4 works in conjunction with the model update unit to achieve operational data feedback and iterative optimization of the digital twin. During equipment operation, the status feedback unit of the dredging device 4 collects operational parameters such as the actual rotation speed of the scraper, the actual opening degree of the sludge discharge valve, the motor operating current, and the equipment vibration amplitude every 0.5 seconds, as well as actual statuses such as "whether there is jamming" and "whether sludge discharge is smooth," and synchronizes the data to the cloud server via an edge gateway. The model update unit utilizes cloud computing resources to compare the actual operational data (such as the actual sludge level descent rate and actual energy consumption) with the corresponding predicted values ​​previously output by the digital twin, calculates the root mean square error as the loss function, and adjusts the weight parameters of the data-driven model in reverse using gradient descent. The model update adopts a combined "batch update + real-time update" mode: batch parameter optimization is performed every morning using the accumulated operational data of the day; if the loss function exceeds a preset threshold at a certain moment, a real-time update is triggered. Through continuous feedback and optimization, the digital twin can adapt to changes in the operating conditions of the sedimentation tank, such as seasonal fluctuations in influent water quality and efficiency decline caused by equipment aging, maintaining high fidelity and prediction accuracy in the long term, and further improving the reliability of system decision-making.

[0031] The central processing platform 1, through the human-computer interaction unit 6 and the early warning and diagnosis unit, enables user participation and ensures the safe operation of the system. The human-computer interaction unit 6 displays the real-time status of the dynamic digital twin through a 3D visualization interface, such as a 3D model of the mud distribution in the pool and dynamic simulation of the water flow field. It compares the simulation results of each candidate strategy using bar charts and line graphs, such as energy consumption curves under different strategies and comparisons of effluent water quality. It also explains the decision-making basis of the optimal strategy using text lists, such as the scores, weights, and overall scores of various indicators. Simultaneously, it receives manually input commands from the user: if the user believes a candidate strategy better meets the on-site requirements, they can input a strategy adjustment command, and the system immediately substitutes the adjusted strategy into the digital twin's weight simulation and provides feedback on the effect; if water quality control standards are upgraded, the user can modify the target weight for optimization, such as increasing the effluent water quality weight from 0.5 to 0.6, and the adaptive optimization control module 5 immediately responds to generate a new weight strategy.

[0032] The early warning and diagnostic unit continuously monitors the state synchronization error of the digital twin. When the error exceeds a preset threshold, it automatically locates the source of the error (such as abnormal data from a turbidity sensor in a certain area) and generates a "sensor calibration early warning," prompting the user to calibrate the specified sensor. Simultaneously, it analyzes the state feedback data of the sludge scraper 4. If the deviation between the actual speed and the commanded speed of the scraper is consistently ≥15% and accompanied by excessive vibration amplitude, it generates an "equipment fault diagnosis alarm" and pushes possible fault causes (such as obstruction of the track by foreign objects or wear of the motor bearings). All early warning and alarm information is displayed in a red pop-up window on the human-machine interface and automatically recorded in the system log (including the time of occurrence, fault type, and location result), facilitating timely user intervention and significantly improving the safety and maintainability of the system operation.

[0033] This embodiment achieves intelligent control of the entire sedimentation tank dredging process through the aforementioned closed-loop steps. Relying on real-time multi-dimensional monitoring and dynamic digital twin simulation capabilities, it anticipates changes in operating conditions in advance; it selects the optimal strategy through multi-objective optimization decision-making, avoiding the lag and strategy limitation of traditional control; and it ensures the system adapts to long-term changes in operating conditions through feedback and model update mechanisms. Ultimately, while ensuring effluent water quality meets standards, it reduces dredging energy consumption, minimizes equipment wear, and significantly improves the intelligence level and overall efficiency of sedimentation tank operation.

[0034] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0035] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A settlement system based on intelligent monitoring, comprising a sedimentation tank, a dredging device and a water quality monitoring device, characterized in that, The central processing platform comprises a digital twin module and an adaptive optimization control module; The water quality monitoring device is configured to collect multi-dimensional water quality parameter data of the sedimentation tank in real time and transmit the data to the central processing platform; The digital twin module is configured to construct and run a dynamic digital twin of the sedimentation tank, synchronize a state according to the received multi-dimensional water quality parameter data, and simulate and deduce future operation effects of different dredging strategies to obtain predicted values of performance indexes; The adaptive optimization control module is configured to receive the multi-dimensional water quality parameter data, drive the digital twin module to update a state, generate a plurality of candidate dredging strategies based on an updated system state and a preset optimization target, input the candidate dredging strategies into the digital twin module for simulation and deduction, select an optimal strategy from the candidate dredging strategies according to predicted values of the performance indexes, and convert the optimal strategy into a control instruction and send the control instruction to the dredging device; The dredging device is configured to perform a corresponding dredging operation in response to the control instruction.

2. The smart monitoring based sedimentation system of claim 1, wherein, The digital twin module comprises a model construction unit and a model running unit; The model construction unit is configured to establish a mechanism model based on a physical structure and a sedimentation mechanism of the sedimentation tank, train a data-driven model using historical operation data to learn non-linear relationships that the mechanism model fails to describe, and fuse the mechanism model and the data-driven model to form a dynamic digital twin; The model running unit is configured to receive real-time data of the monitoring device during system operation, call the dynamic digital twin, dynamically correct an output state of the mechanism model using the data-driven model in the dynamic digital twin, and achieve high-fidelity state synchronization with the physical sedimentation tank. The digital twin module further comprises a strategy simulation unit configured to:

3. The smart monitoring based sedimentation system of claim 2, wherein, receive a plurality of candidate dredging strategies generated by the adaptive optimization control module; drive the dynamic digital twin to simulate an evolution process of an operation state of the sedimentation tank in a future set period of time using each candidate dredging strategy as an input condition; calculate and output predicted values of a plurality of performance indexes corresponding to each candidate dredging strategy during the simulation process; The plurality of performance indexes at least include two or more of water quality indexes, system energy consumption indexes, and dredging equipment load indexes. The adaptive optimization control module comprises a strategy generation unit and a decision unit; 4. The smart monitoring based sedimentation system of claim 1, wherein, The strategy generation unit is configured to automatically generate a plurality of candidate dredging strategies that differ in dredging timing, dredging intensity, or dredging range based on a current system state synchronized by the digital twin module and a preset optimization target; The decision unit is configured to receive predicted values of the performance indexes output by the strategy simulation unit and select a strategy with the optimal comprehensive utility from the candidate dredging strategies as the optimal strategy according to a preset multi-objective optimization algorithm. The dredging device comprises an execution unit and a state feedback unit; 5. The smart monitoring based sedimentation system of claim 1, wherein, The execution unit is configured to receive the control instruction and parse the instruction to control a moving speed, a travel path of a mud scraper, and an opening degree of a mud discharge valve. ​ The state feedback unit is configured to collect operation parameters and actual states of the dredging device in real time, and send the data to the central processing platform as actual operation data.

6. The smart monitoring based sedimentation system of claim 1, wherein, The water quality monitoring device comprises a multi-parameter sensing unit and a data preprocessing unit. The multi-parameter sensing unit is configured to synchronously collect physical indexes, chemical indexes and biological indexes at different depths and regions in the sedimentation tank. The data preprocessing unit is configured to filter, remove outliers and align timestamps of the physical indexes, chemical indexes and biological indexes, and generate multi-dimensional water quality parameter data meeting input requirements of the digital twin module.

7. The smart monitoring based sedimentation system of claim 5, wherein, Further comprising: A model updating unit configured to: compare the actual operation data with corresponding predicted values, calculate a loss function, and adjust parameters of the data-driven model using the loss function to realize adaptive updating. The central processing platform further comprises a human-computer interaction unit configured to:

8. The smart monitoring based sedimentation system of claim 1, wherein, visually display states of the dynamic digital twin, simulation results of candidate dredging strategies and system decision basis to a user, receive manually input strategy adjustment instructions or optimization target weight modification instructions from the user, and transmit the instructions to the adaptive optimization control module. Further comprising a warning and diagnosis unit configured to: continuously monitor state synchronization errors of the digital twin, generate a device calibration warning when the state synchronization errors exceed a preset threshold, analyze state feedback data of the dredging device, generate a device fault diagnosis warning when deviations between actual operation parameters and control instructions are continuously abnormal, and push the device calibration warning and the device fault diagnosis warning to the human-computer interaction unit for visual prompting and record the warnings to a system log.

9. The smart monitoring based sedimentation system of claim 8, wherein, The central processing platform adopts an edge-cloud collaborative computing architecture; wherein state synchronization and dredging control tasks with high real-time requirements are executed by edge computing devices deployed on site, and model training and big data analysis tasks with high computing resource requirements are executed by cloud servers. ​ ​ 10. The smart monitoring based sedimentation system according to any one of claims 1-9, characterized in that, ​ ​

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