Cross-process collaborative diversion ridge type intelligent hydraulic cyclone grit chamber system

Abstract

The invention discloses a cross-process collaborative diversion ridge type intelligent hydraulic cyclone grit chamber system, and belongs to the technical field of sewage treatment. The system comprises an intelligent sensing module, an optimization regulation and control module, a dynamic regulation and control hardware module, a cross-unit cooperation interface and a predictive operation and maintenance module, through multi-module cooperation, full-process data is collected in real time, rotational flow parameters are dynamically optimized, the sand setting efficiency and the organic matter retention rate are accurately balanced, and the follow-up process carbon source requirement is met. The system is high in sand setting efficiency, low in energy consumption and intelligent in operation and maintenance, and the overall stability and economical efficiency of sewage treatment are effectively improved.

Description

Technical Field

[0001] This invention belongs to the field of wastewater treatment technology, specifically relating to a cross-process collaborative guide sill type intelligent hydraulic vortex grit chamber system. Background Technology

[0002] Traditional hydrocyclone grit chambers have limitations in their flow guidance and control design. Hardware parameters such as influent cross-sectional dimensions and effluent weir height are mostly fixed, making it difficult to dynamically respond to real-time fluctuations in operating conditions such as influent flow rate, grit concentration, and organic matter content. This results in poor cyclone field stability, uneven grit stress, incomplete fine grit separation, and significant fluctuations in grit settling efficiency after the water enters the grit separation chamber. Furthermore, these grit chambers lack a collaborative interaction mechanism with subsequent wastewater treatment units, making it impossible to precisely control the amount of organic matter retained in the effluent according to the carbon source requirements of the downstream biological processes. This leads to either insufficient downstream carbon sources due to excessive organic matter settling or excessive organic matter retention affecting grit settling efficiency, thus restricting the overall wastewater treatment system's treatment efficiency and energy consumption optimization. Summary of the Invention

[0003] To address the aforementioned problems in the existing technology, this invention provides a cross-process collaborative flow guide sill type intelligent hydraulic vortex sedimentation tank system.

[0004] The objective of this invention can be achieved through the following technical solutions: A cross-process collaborative guide sill type intelligent hydrocyclone grit chamber system is applied to a hydrocyclone grit chamber, wherein the hydrocyclone grit chamber includes: The water intake channel includes a horizontal section and a converging section along the water flow direction. The horizontal section is a straight channel, and the cross-section of the converging section narrows along the flow and the bottom slope increases accordingly. An angle-adjustable guide sill is located at the end of the inlet channel, and its cross-section is arc-shaped. The sedimentation and separation chamber is a circular pool connected to the guide sill. The sand hopper, located at the bottom of the sand separation chamber, has a frustum-shaped structure. The sand collection area is connected to the bottom of the sand hopper; The water outlet channel is connected to the upper part of the sedimentation and sorting chamber; The outlet weir is located at the beginning of the outlet channel; A gas desanding control mechanism is installed inside the cyclone sedimentation tank; The system includes: The intelligent sensing module is used to collect operating parameters in real time, including influent water quality, flow field status in the pool, equipment operating status, and effluent water quality. The optimization and control module is connected to the intelligent sensing module and dynamically generates optimized control commands based on the collected data and through a built-in algorithm model. The dynamically adjustable hardware module includes a cross-section adjustment mechanism, an angle-adjustable guide sill, a height-adjustable outlet weir, and a gas sand removal control mechanism installed in the inlet channel, for executing the optimized control commands. Cross-unit collaboration interface is used to enable data interaction with subsequent processing units.

[0005] In a further embodiment of the present invention, the dynamically adjustable hardware module includes: A cross-section adjustment mechanism is provided in the constriction section of the inlet channel and driven by a first motor. It is configured to respond to optimization control commands and dynamically adjust the inlet cross-section size and flow velocity distribution by changing the opening degree. An angle-adjustable guide sill, driven by a second motor and with a continuously adjustable angle, is configured to adjust the tilt angle according to algorithm instructions to control the initial vortex shape and intensity formed by the influent in the sedimentation chamber. The gas sand removal control mechanism is located in the lower part of the sand separation chamber. It includes a variable frequency aeration unit, which can dynamically adjust the aeration volume, frequency and distribution mode according to the command to assist in sand separation and prevent excessive settling of organic matter. The third driving structure, which is connected to the outlet weir drive, is used to adjust the weir height according to the command, thereby stabilizing the liquid level and swirling field in the pool.

[0006] In a further embodiment of the present invention, the intelligent sensing module includes: The sensor array is installed at the water inlet channel to continuously monitor the water inflow, turbidity, sand concentration and organic matter index. Pressure sensors are arrayed on the inner wall of the sedimentation chamber to monitor the dynamic pressure distribution gradient of the swirling flow field at multiple points simultaneously. The underwater imaging unit is installed in the sediment sorting chamber to acquire real-time visualization information on the sedimentation process and flow regime of sand particles; The level detection unit and vibration sensing unit are installed in the sand hopper to monitor the thickness and state of sand accumulation. The liquid level and water quality sensing unit is located at the water outlet.

[0007] In a further embodiment of the present invention, the optimization and control module includes a digital twin sub-module. The digital twin sub-module receives and synchronizes multi-dimensional data collected by the intelligent sensing module in real time, drives the virtual model to evolve dynamically, thereby mapping and visualizing the swirling flow field structure, sand particle trajectory and equipment status of the entity in the virtual space with high fidelity. Based on the current state and preset target, different control command schemes are simulated and deduced in the virtual space to predict their flow field changes and processing effects, and the deduction results are fed back to the optimization decision-making process to provide a predictive basis for generating the final control command.

[0008] In a further embodiment of the present invention, the built-in algorithm model includes: The flow field adaptive optimization algorithm takes the real-time water intake condition data and the flow field state data in the pool collected by the intelligent sensing module as input, and calculates and decides the optimal combination of guide sill angle, cross-section adjustment mechanism opening, outlet weir height and gas desanding parameters under the current working conditions through a pre-trained machine learning model or a physical rule-based optimization model, aiming to maximize the sedimentation efficiency and flow field stability. The organic matter retention rate control algorithm, which couples the subsequent process carbon source demand signal from the cross-unit collaborative interface, establishes a correlation model between swirl intensity and organic matter sedimentation loss, and inversely derives and outputs the control amount of the adjustable hardware module, aiming to achieve precise control of the organic matter content in the effluent.

[0009] In a further embodiment of the present invention, the intelligent sensing module further includes a data calibration and anomaly removal unit, which performs real-time calibration on the data collected by the sensor group, pressure sensor and underwater imaging unit, and corrects sensor drift error through a preset physical constraint model; at the same time, it uses the 3σ criterion and time series mutation detection algorithm to identify and remove abnormal data; when a single sensor data is abnormal, it automatically activates redundant sensor channel data or supplements the data with predicted values ​​based on historical data.

[0010] In a further embodiment of the present invention, it also includes: The local data processing unit integrates the edge computing node and is responsible for running the logic of sensing data processing, control command generation, and preliminary fault diagnosis that have high real-time requirements. A cloud management platform is connected to the local unit via a wired or wireless network to receive and store operational data and event logs from the local unit for an extended period.

[0011] In a further embodiment of the present invention, it also includes: The predictive maintenance module analyzes the device runtime sequence data collected by the intelligent sensing module based on a machine learning model, identifies patterns of device performance degradation, potential component failures, or abnormal operation, and issues early warnings before a failure occurs. For minor anomalies that can be handled automatically, it automatically generates and issues handling instructions to the corresponding execution mechanism. For failures that require manual intervention, it automatically generates maintenance work orders that include fault location, possible causes, and handling suggestions. The data includes motor current, vibration spectrum, adjustment mechanism action feedback, and sensor readings.

[0012] In a further embodiment of the present invention, the grit chamber operates according to the following process: The intelligent sensing module continuously collects and preprocesses multi-dimensional data throughout the entire process; The optimization and control module integrates real-time data, simulation results from digital twin sub-modules, and backend process requirements obtained through cross-unit collaborative interfaces, runs a built-in algorithm model, and generates an optimized control instruction set for dynamically adjustable hardware modules. The hardware module can dynamically control the execution of instructions to achieve coordinated adjustment of inlet conditions, vortex morphology, separation environment and outlet conditions; The predictive maintenance module continuously analyzes device status data in the background.

[0013] In a further embodiment of the present invention, the built-in algorithm model also includes an energy consumption optimization algorithm. Based on the preliminary control parameters output by the flow field adaptive optimization algorithm, and combined with the equipment power model and historical energy efficiency data, under the premise of satisfying the constraints of sand settling efficiency and organic matter retention rate, a secondary optimization is performed in the parameter solution space to find the control parameter combination with the lowest total system energy consumption. The operating frequency of the gas sand removal control mechanism and the action amplitude of the drive motors of the cross-section adjustment mechanism and the guide sill are then finely adjusted.

[0014] This invention has at least the following beneficial effects: 1. By capturing all process parameters in real time through the intelligent sensing module and dynamically adjusting hardware parameters in combination with the flow field adaptive optimization algorithm, the sedimentation efficiency and swirling flow field stability are maximized, effectively solving the problem of incomplete fine sand separation.

[0015] 2. By leveraging cross-unit collaborative interfaces to obtain backend process requirements, and by using an organic matter retention rate control algorithm to reverse-regulate swirl parameters, the organic matter content in the effluent is precisely matched, ensuring the carbon source supply for subsequent biochemical processes.

[0016] 3. The predictive maintenance module can identify equipment failures and performance degradation in advance, automatically handle minor anomalies and generate maintenance work orders, reducing manual intervention costs and downtime. Attached Figure Description

[0017] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0018] Figure 1 This is a topographical diagram of a hydrocyclone sedimentation tank provided in one embodiment of the present invention; Figure 2 The following is a CFD simulation diagram (front view) of the velocity field streamlines of a hydrocyclone sedimentation tank provided in one embodiment of the present invention. Figure 3 This is a CFD simulation diagram (rear view) of the velocity field streamlines of a hydrocyclone sedimentation tank provided in one embodiment of the present invention. Figure 4 The following is a CFD simulation diagram (top view) of the velocity field streamlines of a hydrocyclone sedimentation tank provided in one embodiment of the present invention. Figure 5 This is a flowchart of the overall system architecture provided in one embodiment of the present invention; Figure 6 This is a flowchart of a digital twin submodule provided in one embodiment of the present invention. Detailed Implementation

[0019] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided.

[0020] Please refer to Figure 1-6 In one embodiment, the present invention provides a cross-process collaborative guide sill type intelligent hydrocyclone grit chamber system, applied to a hydrocyclone grit chamber, the hydrocyclone grit chamber comprising: The water intake channel includes a horizontal section and a converging section along the water flow direction. The horizontal section is a straight channel, and the cross-section of the converging section narrows along the flow and the bottom slope increases accordingly, which is used to guide and accelerate the water flow. An adjustable guide sill is located at the end of the inlet channel. Its cross-section is arc-shaped. It is used to guide the inlet water smoothly to the sedimentation and separation chamber and initially form a vortex to improve the inlet flow pattern. The sedimentation and separation chamber is a circular pool connected to the guide sill. It is used to create a stable swirling motion in the water flow, and to achieve the initial separation of sand particles and sewage under the action of centrifugal force. A sand hopper, located at the bottom of the sedimentation and separation chamber, has a frustum-shaped structure and is used to collect and concentrate the sand particles that settle down due to gravity and centrifugal force. A sand collection area, connected to the bottom of the sand hopper, is used for temporary storage of sand particles; The outlet channel is connected to the upper part of the sedimentation and separation chamber and is used to guide the wastewater after the sand particles are separated out of the pool. The outlet weir, located at the beginning of the outlet channel, is used to control the liquid level in the sedimentation tank and provide the necessary hydraulic conditions for the formation of a stable vortex. A gas-based sand removal control mechanism is installed inside the cyclone sedimentation tank to further assist in the separation and sedimentation process of sand particles. The system includes: The intelligent sensing module is used to collect operating parameters in real time, including influent water quality, flow field status in the pool, equipment operating status, and effluent water quality. The optimization and control module is connected to the intelligent sensing module and dynamically generates optimized control commands based on the collected data and through a built-in algorithm model. The dynamically adjustable hardware module includes a cross-section adjustment mechanism, an angle-adjustable guide sill, a height-adjustable outlet weir, and a gas sand removal control mechanism installed in the inlet channel, for executing the optimized control commands. Cross-unit collaboration interface is used to enable data interaction with subsequent processing units.

[0021] In this embodiment, the horizontal section of the inlet channel provides a stable initial flow pattern for the water flow, avoiding the impact of water flow disturbance on the subsequent formation of vortices. The contraction section, through the coordinated design of narrowing of the cross-section along the flow path and increasing bottom slope, achieves natural acceleration of the water flow, providing the necessary hydraulic conditions for the formation of the vortex field. The angle-adjustable arc-shaped guide sill, through its streamlined cross-section design, reduces water flow resistance, ensuring that the inlet channel smoothly enters the sedimentation and separation chamber. Simultaneously, its adjustable angle allows the initial shape and intensity of the vortex to be dynamically adjusted according to operating conditions. The sedimentation and separation chamber adopts a circular pool structure, utilizing the characteristics of a circular flow channel with no dead angles and low water flow resistance to ensure stable rotational motion of the water flow. Under the combined action of centrifugal force and gravity, efficient separation of sand particles from water is achieved. The frustum-shaped sand hopper, through its inclined sidewall design, utilizes gravity to promote the collection of sand particles to the bottom, preventing sand particles from depositing on the pool wall. The sand collection area is connected to the bottom of the sand hopper, providing temporary storage space for sand particles and preventing sand particles from flowing back into the sedimentation and separation chamber, thus affecting the separation effect. The effluent weir, located at the beginning of the effluent channel, controls the liquid level in the grit separation chamber through height adjustment, providing the necessary hydraulic boundary conditions for the stability of the vortex field. The effluent channel smoothly discharges the wastewater after sand separation, avoiding the impact of effluent disturbance on the vortex field within the pool. The gas-based sand removal control mechanism is installed in the grit separation chamber, assisting in sand separation through aeration while simultaneously inhibiting excessive settling of organic matter.

[0022] By deploying various sensing elements at key locations such as the inlet channel, sedimentation chamber, sand hopper, and outlet, a multi-dimensional sensing network is constructed throughout the entire process. This ensures the real-time capture of influent water quality parameters, dynamic changes in the flow field within the pool, sand settling process, equipment operating status, and effluent water quality indicators, providing comprehensive and reliable data support for control and decision-making.

[0023] As the "decision-making core" of the system, the optimization and control module receives multi-dimensional data collected by the intelligent sensing module in real time through a data interface. It then analyzes and processes this data using a built-in algorithm model, and dynamically generates optimized control commands for the dynamically controllable hardware modules based on preset processing targets. The core logic of the algorithm model is based on the mapping relationship between operating data and processing targets, using mathematical modeling and logical reasoning to determine the optimal combination of hardware control parameters.

[0024] Each hardware actuator employs motor drive or frequency conversion control technology, enabling precise response to control commands issued by the optimization and control module. The cross-sectional adjustment mechanism adjusts the inlet cross-sectional size by changing the opening, thereby regulating the water flow velocity distribution; the guide sill changes the direction and intensity of water flow by adjusting its angle, controlling the vortex pattern; the outlet weir stabilizes the liquid level in the pool by adjusting its height; and the gas sand removal control mechanism assists in sand separation and controls organic matter sedimentation by adjusting the aeration volume, frequency, and distribution mode.

[0025] A communication link is established between the front-end grit chamber and the subsequent treatment unit through standardized communication protocols and data formats, enabling real-time interaction of operational data between the two. The front-end grit chamber can obtain key parameters such as the carbon source requirements and treatment load of the subsequent processes, while the subsequent treatment unit can obtain information such as the effluent quality and grit separation status of the front-end grit chamber, providing a data foundation for coordinated control between the two.

[0026] In a further embodiment of the present invention, the dynamically adjustable hardware module includes: A cross-section adjustment mechanism is provided in the constriction section of the inlet channel and driven by a first motor. It is configured to respond to optimization control commands and dynamically adjust the inlet cross-section size and flow velocity distribution by changing the opening degree. An angle-adjustable guide sill, driven by a second motor and with a continuously adjustable angle, is configured to adjust the tilt angle according to algorithm instructions to control the initial vortex shape and intensity formed by the influent in the sedimentation chamber. The gas sand removal control mechanism is located in the lower part of the sand separation chamber. It includes a variable frequency aeration unit, which can dynamically adjust the aeration volume, frequency and distribution mode according to the command to assist in sand separation and prevent excessive settling of organic matter. The third driving structure, which is connected to the outlet weir drive, is used to adjust the weir height according to the command, thereby stabilizing the liquid level and swirling field in the pool.

[0027] In this embodiment, the cross-section adjustment mechanism adopts a movable shielding structure and is installed at a key position in the contraction section of the inlet channel. Its opening can be continuously adjusted within a preset range. The first motor is connected to the adjustment mechanism through a mechanical transmission structure. The rotational motion of the motor is converted into the linear or rotational motion of the adjustment mechanism, thereby changing the effective water passage area of ​​the inlet cross-section. The optimization and control module generates opening control commands based on operating data such as inlet flow rate and sand concentration. After receiving the commands, the first motor drives the adjustment mechanism to adjust to the target opening, realizing dynamic optimization of the inlet cross-section size, thereby controlling the water flow velocity distribution and ensuring that the water enters the sedimentation and separation chamber at the optimal flow rate, laying a good foundation for the formation of the vortex field. The core design of this mechanism lies in the precision and reliability of the transmission structure, ensuring the linearity and repeatability of the opening adjustment, while also possessing good waterproof and corrosion-resistant properties, adapting to long-term operation in wastewater environments.

[0028] The guide sill features an arc-shaped cross-section and is connected to the pool wall at the end of the inlet channel via a rotating shaft, allowing it to rotate continuously within a preset angle range. A second motor is connected to the guide sill's rotating shaft via a reduction gear mechanism. The motor's output torque is transmitted to the rotating shaft after reduction, driving the guide sill to adjust its angle. The optimization and control module generates angle control commands based on data such as the flow field state within the pool and the sand separation effect. Upon receiving the commands, the second motor drives the guide sill to rotate to the target angle, changing the direction and velocity distribution of the water flow into the sedimentation chamber, thereby controlling the initial shape and intensity of the vortex. The arc-shaped cross-section design aims to reduce water flow resistance and prevent the formation of vortices on the guide sill surface, ensuring smooth entry into the sedimentation chamber. The continuously adjustable angle allows for precise control of the vortex intensity over a wide range, adapting to separation requirements under different operating conditions.

[0029] The gas-based sand removal control mechanism consists of a variable frequency fan, aeration pipes, aeration heads, and control valves. The aeration pipes are located in the lower part of the sand separation chamber, and the aeration heads are evenly distributed along the pipes, forming a multi-point aeration layout. The variable frequency fan provides the air source for aeration, and its operating frequency can be dynamically adjusted through commands from the optimization and control module, thereby changing the aeration volume and frequency. The control valves can adjust the airflow in different areas of the aeration pipes, optimizing the aeration distribution pattern. The working principle of this mechanism is that the microbubbles generated by aeration slightly disturb the water flow in the tank, disrupting the flocculation of fine sand particles and promoting the separation of sand particles from the water. On the other hand, the airflow formed during the rise of the bubbles can prevent organic particles from settling with the sand particles, increasing the retention rate of organic matter in the effluent. By dynamically adjusting the aeration volume, frequency, and distribution pattern, a precise balance between auxiliary sand removal and organic matter retention can be achieved.

[0030] The effluent weir adopts a liftable structure, connected to the pool wall via guide rails, allowing it to move up and down along the rails to adjust the weir height. The third drive structure consists of a motor and a transmission mechanism. The motor drives the transmission mechanism to raise and lower the effluent weir along the guide rails to the target height. The optimization and control module generates height control commands based on pool level data and effluent flow requirements. Upon receiving the commands, the third drive structure drives the effluent weir to adjust its height, thereby controlling the liquid level in the sedimentation chamber. A stable liquid level is a key hydraulic condition for forming a stable vortex field. By adjusting the effluent weir height, the impact of influent flow fluctuations on the liquid level can be offset, ensuring the stability of the vortex field and controlling the uniformity of the effluent flow, providing stable influent conditions for subsequent treatment units. The core design of this structure lies in the smoothness and sealing of the lifting mechanism, avoiding problems such as leakage and jamming, and ensuring the accuracy of weir height adjustment.

[0031] In a further embodiment of the present invention, the intelligent sensing module includes: The sensor array is installed at the water inlet channel to continuously monitor the water inflow, turbidity, sand concentration and organic matter index. Pressure sensors are arrayed on the inner wall of the sedimentation chamber to monitor the dynamic pressure distribution gradient of the swirling flow field at multiple points simultaneously. The underwater imaging unit is installed in the sediment sorting chamber to acquire real-time visualization information on the sedimentation process and flow regime of sand particles; The level detection unit and vibration sensing unit are installed in the sand hopper to monitor the thickness and state of sand accumulation. The liquid level and water quality sensing unit is located at the water outlet.

[0032] In this embodiment, the sensor group integrates multiple sensing elements, including a flow sensor, a turbidity sensor, a sand concentration sensor, and an organic matter sensor. All elements are designed to be waterproof and corrosion-resistant for wastewater environments and are deployed at key sections of the influent channel to ensure the representativeness of the monitoring data. The flow sensor, based on fluid mechanics principles, calculates the influent flow rate by detecting parameters such as water velocity and pressure. The turbidity sensor, based on optical scattering principles, reflects wastewater turbidity by measuring the intensity of light scattering in water. The sand concentration sensor, based on acoustic or optical principles, calculates sand concentration by detecting the reflection and scattering signals of sound waves or light by sand particles. The organic matter sensor, based on chemical sensing or spectral analysis principles, reflects the organic matter content by detecting characteristic signals of organic matter in the wastewater. The output signals of each sensor are converted into standard electrical signals by a signal conditioning circuit and then transmitted to the core processing unit of the intelligent sensing module via a data bus, enabling continuous monitoring of key parameters of the influent operating conditions.

[0033] The pressure sensors employ high-precision, high-response piezoresistive or capacitive pressure sensors, arranged in an array at different heights and circumferential positions on the inner wall of the sedimentation and sorting chamber, forming a multi-point monitoring network. The pressure sensors are in direct contact with the water in the tank, detecting the static and dynamic pressure at each monitoring point in real time. Since the pressure distribution at different locations in the swirling flow field is closely related to the flow velocity, key hydraulic parameters such as the flow velocity distribution and swirling intensity of the swirling flow field can be inferred by synchronously monitoring the pressure distribution gradient at multiple points. The sensor output signals are amplified, filtered, and then transmitted to the core processing unit. Data fusion algorithms are used to calculate the dynamic characteristics of the swirling flow field, providing data support for optimizing and controlling the swirling flow morphology. The core design of this array lies in the rational layout of the monitoring points, ensuring comprehensive capture of pressure changes in the swirling flow field. Simultaneously, the sensors must possess good stability and anti-interference capabilities to adapt to the complex hydraulic environment within the tank.

[0034] The underwater imaging unit employs high-definition cameras suitable for underwater environments, equipped with a waterproof casing and anti-fog, anti-fog lenses. Installed in a suitable location within the sedimentation tank, it ensures the field of view covers the critical areas of sand settling. Based on machine vision principles, the imaging unit continuously captures image sequences of the water in the tank, real-time capturing the sand's trajectory, settling velocity, and flow regime changes. The encoded image signals are transmitted to the core processing unit via wired or wireless transmission. Image processing algorithms analyze the image sequences to extract quantitative information on the sand settling process and visualize the flow regime. This unit allows operators to directly observe the sand separation process within the tank and provides visualized data support for the flow field state optimization module, overcoming the limitations of traditional sensors that can only acquire single-point parameters.

[0035] The implementation of the sand hopper level detection and vibration sensing unit: The level detection unit uses an ultrasonic or radar level sensor, installed at the top of the sand hopper. It measures the height of sand accumulation by emitting ultrasonic or radar waves and receiving reflected waves. The vibration sensing unit uses an accelerometer or vibration sensor, installed on the side wall or bottom of the sand hopper, to detect the vibration signal in real time. Changes in the thickness of sand accumulation affect the vibration characteristics of the sand hopper. Through the coordinated monitoring of the level detection unit and the vibration sensing unit, the thickness and state of sand accumulation can be accurately determined. When the sand accumulation thickness reaches a preset threshold or the vibration signal is abnormal, the intelligent sensing module will issue an early warning signal, prompting the operator to remove sand in time. The core design of this unit lies in the installation position and detection range of the sensors, ensuring accurate monitoring of the sand accumulation state in the sand hopper, while also possessing anti-interference capabilities to avoid the influence of factors such as pool vibration and water flow disturbance.

[0036] Implementation of the effluent level and water quality sensing unit: The level sensing unit uses float-type, hydrostatic, or ultrasonic level sensors, installed in the effluent area of ​​the effluent channel or sedimentation chamber to monitor the effluent level in real time. The water quality sensing unit integrates sensors for key water quality indicators such as turbidity, suspended solids, and organic matter, deployed at key sections of the effluent channel to monitor the effluent water quality. Level data reflects the hydraulic balance within the tank, providing a basis for adjusting the effluent weir height; effluent water quality data is used to evaluate the sand separation effect, providing feedback information for optimized control. The output signals from each sensor are processed and transmitted to the core processing unit for fusion analysis with influent operating data and tank flow field data to form a complete picture of the system's operating status.

[0037] In a further embodiment of the present invention, the optimization and control module includes a digital twin sub-module. The digital twin sub-module receives and synchronizes multi-dimensional data collected by the intelligent sensing module in real time, drives the virtual model to evolve dynamically, thereby mapping and visualizing the swirling flow field structure, sand particle trajectory and equipment status of the entity in the virtual space with high fidelity. Based on the current state and preset target, different control command schemes are simulated and deduced in the virtual space to predict their flow field changes and processing effects, and the deduction results are fed back to the optimization decision-making process to provide a predictive basis for generating the final control command.

[0038] In this embodiment, the digital twin submodule establishes real-time communication with the intelligent sensing module through a high-speed data interface, receiving multi-dimensional real-time data such as influent water quality, pressure distribution in the pool flow field, sand settling status, and equipment operating parameters. To ensure data quality, the module first preprocesses the received data, including data format conversion, outlier removal, missing value completion, and data standardization, eliminating data noise and errors and providing high-quality data input for the dynamic evolution of the virtual model. The core requirements for data synchronization are real-time performance and completeness, ensuring that the virtual model can reflect the changes in the operating status of the physical system in a timely and accurate manner.

[0039] The virtual model is constructed based on the physical structure, hydraulic characteristics, and operating mechanism of the physical grit chamber system, encompassing multiple sub-models including a geometric model, a hydraulic dynamics model, a sand particle movement model, and an equipment model. The geometric model is built according to the size and structural features of the physical grit chamber, accurately replicating the spatial morphology of key structures such as the inlet channel, guide sill, grit separation chamber, sand hopper, and outlet weir. The hydraulic dynamics model, based on computational fluid dynamics principles, simulates the flow patterns and swirling field distribution within the chamber by solving fluid dynamics control equations such as the Navier-Stokes equations. The sand particle movement model, based on the Euler-Lagrange method, combines the physical properties of sand particles with the forces of water flow to simulate the trajectory and settling process of sand particles in the swirling field. The equipment model, based on the structural parameters and operating characteristics of the physical equipment, simulates the action response and operating status of equipment such as motors and regulating mechanisms. These sub-models are interconnected through data interfaces to form a complete virtual twin model, ensuring a high-fidelity mapping of the operating status of the physical system.

[0040] Dynamic Evolution of the Virtual Model: The digital twin submodule uses pre-processed real-time data as input to drive the dynamic evolution of the virtual model. For the geometric model, the state of the virtual structure is updated in real time based on equipment operating parameters. For the hydraulic dynamics model, data such as influent flow rate and water quality are used as boundary conditions, and combined with equipment state parameters, the velocity, pressure, and other distributions of the virtual swirling field are updated in real time through numerical calculations. For the sand particle motion model, based on real-time data such as sand particle concentration and particle size distribution, combined with the dynamic changes of the virtual swirling field, the motion trajectory and sedimentation process of sand particles are simulated in real time. For the equipment model, the operating status of the virtual equipment is updated in real time based on data such as motor current and action feedback. Through the collaborative evolution of each sub-model, the virtual model can accurately and in real time reproduce the operating status of the real system in virtual space, achieving synchronous mapping between virtual and reality.

[0041] Simulation and deduction of control schemes: Based on the current operating status reflected in the virtual model and the preset processing targets, the digital twin submodule generates multiple candidate control command schemes. These candidate schemes cover different combinations of guide vane angles, cross-sectional adjustment mechanism openings, outlet weir heights, and gas desanding parameters, encompassing a range of possible control parameters. The module uses each candidate scheme as input to drive the virtual model in simulation, deduce the changing trends of the virtual vortex field, sand separation effects, organic matter retention, and equipment operating status after each scheme is executed. During the simulation, the module records the changes in key performance indicators in real time, quantitatively evaluating the execution effects of each candidate scheme.

[0042] Simulation Result Feedback and Decision Support: After the simulation is completed, the digital twin submodule compares and analyzes the performance indicators of each candidate solution, selects the optimal solution, and feeds the simulation results back to the decision-making process of the optimization and control module. The optimization and control module combines the simulation results with the real-time status of the physical system to further verify and adjust the optimal solution, generating the final optimization control command and issuing it to the dynamically controllable hardware module for execution. Simultaneously, the module stores the simulation data and execution results, providing data support for subsequent model optimization and algorithm iteration.

[0043] In a further embodiment of the present invention, the built-in algorithm model includes: The flow field adaptive optimization algorithm takes the real-time water intake condition data and the flow field state data in the pool collected by the intelligent sensing module as input, and calculates and decides the optimal combination of guide sill angle, cross-section adjustment mechanism opening, outlet weir height and gas desanding parameters under the current working conditions through a pre-trained machine learning model or a physical rule-based optimization model, aiming to maximize the sedimentation efficiency and flow field stability. The organic matter retention rate control algorithm, which couples the subsequent process carbon source demand signal from the cross-unit collaborative interface, establishes a correlation model between swirl intensity and organic matter sedimentation loss, and inversely derives and outputs the control amount of the adjustable hardware module, aiming to achieve precise control of the organic matter content in the effluent.

[0044] In this embodiment, the core objective of the flow field adaptive optimization algorithm is to dynamically adjust hardware parameters based on real-time operating conditions to ensure a stable flow field within the pool and optimal sedimentation efficiency. The algorithm's input includes influent operating condition data collected by the intelligent sensing module and flow field state data within the pool. The algorithm's implementation can be divided into two modes: data-driven and physical rule-driven. Data-driven model: An optimization model is built based on machine learning algorithms and trained using a large amount of historical operating data. During training, the model learns the mapping relationship between input and output, establishing a correlation model between operating data and the optimal combination of hardware parameters. In actual operation, the algorithm inputs real-time collected operating data into the pre-trained model, and the model quickly outputs the optimal combination of guide vane angle, cross-section adjustment mechanism opening, outlet weir height, and gas desanding parameters under the current operating conditions. To ensure the model's generalization ability and adaptability, it is necessary to periodically iterate and update the model using new operating data to optimize the model parameters.

[0045] The physical rule-driven mode constructs a mathematical model based on the principles of fluid mechanics and particle kinematics, describing the water flow and sand settling process within the pool by solving governing equations. The model includes quantitative relationships between parameters such as swirling flow field intensity, inlet velocity, and guide vane angle, and settling efficiency and flow field stability. During actual operation, the algorithm uses real-time operating data as model input, solves the model through numerical calculations, and obtains the settling efficiency and flow field stability indices corresponding to different combinations of hardware parameters, thereby selecting the optimal parameter combination. The core of this mode lies in the accuracy and efficiency of the model, requiring reasonable simplification and numerical optimization to ensure rapid output of control results. Both modes can be selected or combined according to actual needs. The optimal parameter combination output by the algorithm aims to maximize settling efficiency and flow field stability, providing fundamental parameters for subsequent organic matter retention rate control.

[0046] The core objective of the organic matter retention rate control algorithm is to precisely control the organic matter content in the effluent based on the carbon source requirements of subsequent processes, thereby achieving synergy between front-end grit removal and back-end biological processes. The algorithm's inputs include the carbon source requirement signal for subsequent processes obtained from the cross-unit collaborative interface, the influent organic matter content data collected by the intelligent sensing module, and the preliminary hardware parameter combination output by the flow field adaptive optimization algorithm. Specifically, these include: A correlation model was established between swirling intensity and organic matter settling loss. Swirling intensity is a key factor affecting organic matter settling; excessive swirling intensity leads to organic matter settling easily with sand particles, while insufficient swirling intensity results in incomplete sand particle separation. Through extensive experimental data and numerical simulation results, the settling behavior of organic matter under different swirling intensities was analyzed, and a quantitative mathematical model was established between swirling intensity and organic matter settling loss rate, clarifying the mapping relationship between the two.

[0047] Based on the carbon source demand signal of subsequent processes, determine the target range of organic matter content in the effluent. If the carbon source of subsequent processes is insufficient, it is necessary to increase the organic matter retention rate and reduce organic matter sedimentation loss; if the carbon source of subsequent processes is abundant, it is appropriate to increase the organic matter sedimentation loss and enhance the sand separation effect.

[0048] Based on the correlation model and the target organic matter content, the correction amount of the hardware control parameters is derived in reverse. Using the preliminary parameters output by the flow field adaptive optimization algorithm as a foundation, the organic matter settling loss rate corresponding to the current parameters is calculated through the correlation model and compared with the target loss rate. If the requirements are not met, key parameters affecting the swirling intensity, such as the guide vane angle and gas desanding parameters, are adjusted until the organic matter settling loss rate reaches the target range, at which point the final hardware control amount is output. This algorithm, through coupling with the cross-unit collaborative interface, achieves coordinated linkage between front-end processing and subsequent processes, ensuring that the organic matter retention rate accurately matches the back-end carbon source requirements.

[0049] In a further embodiment of the present invention, the intelligent sensing module further includes a data calibration and anomaly removal unit, which performs real-time calibration on the data collected by the sensor group, pressure sensor and underwater imaging unit, and corrects sensor drift error through a preset physical constraint model; at the same time, it uses the 3σ criterion and time series mutation detection algorithm to identify and remove abnormal data; when a single sensor data is abnormal, it automatically activates redundant sensor channel data or supplements the data with predicted values ​​based on historical data.

[0050] In this embodiment, the core of real-time calibration is to dynamically correct sensor drift errors based on a physical constraint model. First, for different types of sensors, corresponding physical constraint models are established according to their working principles and measurement characteristics. These physical constraint models are based on the physical nature and objective laws of the sensor measurement parameters, clarifying the reasonable range of parameter values, their changing trends, and their correlation with other parameters. For example, the physical constraint model for a flow sensor can be based on the law of conservation of mass, establishing the correlation between influent flow rate, effluent flow rate, and the rate of change of liquid level in the tank; the physical constraint model for a pressure sensor can be based on the principle of hydrostatics, establishing the gradient relationship between sensor measurements at different heights. During actual operation, the unit receives measurement data from each sensor in real time and inputs the data into the corresponding physical constraint model for verification. If the deviation between the measured data and the model's predicted value is within the allowable range, the data is considered valid; if the deviation exceeds the allowable range, it is determined that the sensor has a drift error, and the deviation compensation amount is calculated through the model to correct the measurement data. For example, when the measured value of a pressure sensor deviates from the theoretical value calculated based on the physical constraint model, the compensation coefficient is dynamically adjusted according to the magnitude and changing trend of the deviation to correct the measurement data and ensure its accuracy. Real-time calibration requires ensuring both model accuracy and computational efficiency. This can be achieved by periodically updating model parameters to adapt to sensor aging and environmental changes, thereby ensuring the stability of the calibration results.

[0051] The identification of abnormal data adopts a combination of the 3σ criterion and the temporal mutation detection algorithm, taking into account both statistical characteristics and temporal change features.

[0052] First, statistical analysis is performed on the historical valid data of each sensor to calculate the mean and standard deviation. Based on the characteristics of a normal distribution, approximately 99.73% of the data should fall within the range of the mean ± 3σ; data outside this range can be considered outlier. During actual operation, the unit calculates the deviation between the current measurement data and the historical mean in real time. If the absolute value of the deviation is greater than 3σ, it is initially determined to be outlier.

[0053] The temporal abrupt change detection algorithm identifies sudden jumps or abnormal fluctuations in data based on the temporal variation patterns of the data. By calculating the rate of change of data between adjacent time points and setting a reasonable threshold, when the rate of change exceeds the threshold, it is determined to be an abnormal temporal abrupt change. For example, if the measured value of a water quality sensor fluctuates significantly within a short period of time and exceeds the range of change under normal operating conditions, it is determined to be abnormal data. Two algorithms work together to perform a secondary verification of the initially identified abnormal data, avoiding misjudging normal fluctuations as abnormal data. After confirming the anomaly, the unit removes the abnormal data from the data sequence and records information such as the time of the abnormal event and the sensor number, providing a basis for equipment operation and maintenance.

[0054] When data loss occurs due to an anomaly in data from a single sensor, the unit uses redundant sensor channel data or predicted values ​​based on historical data to complete the data, ensuring data integrity.

[0055] For key parameters, the system deploys multiple sensors of the same type to form redundant sensing channels. When the data from one sensor is abnormal, the unit automatically switches to the redundant channel, using the measurement data from the redundant sensor to replace the abnormal data. To ensure the reliability of the redundant data, the data from the redundant sensors needs to be consistent, and the sensor data with the smallest deviation is selected as the supplementary data.

[0056] For sensors without redundant channels or when all redundant channels fail, a time series prediction algorithm is used to generate supplementary predicted values ​​based on historical data. A prediction model is established by analyzing the temporal characteristics of historical sensor data. During actual operation, the model predicts the reasonable data value for the current moment based on historical data, which serves as the supplementary data. To improve prediction accuracy, the prediction model needs to be periodically trained and updated using new, effective data to adapt to changes in data characteristics.

[0057] In a further embodiment of the present invention, it also includes: The local data processing unit integrates the edge computing node and is responsible for running the logic of sensing data processing, control command generation, and preliminary fault diagnosis that have high real-time requirements. A cloud management platform is connected to the local unit via a wired or wireless network to receive and store operational data and event logs from the local unit for an extended period.

[0058] In this embodiment, the local data processing unit uses an edge computing node as its core hardware carrier. The edge computing node has strong computing power, data storage capacity and interface expansion capability, which can meet the needs of local real-time data processing.

[0059] Edge computing nodes typically employ industrial-grade embedded processors, equipped with adequate memory, local storage modules, and various communication interfaces. The processors feature low power consumption and high reliability, making them suitable for the harsh environments of wastewater treatment plants. Local storage modules are used for temporary storage of real-time data, control commands, and fault logs. Communication interfaces facilitate data exchange with intelligent sensing modules, dynamically adjustable hardware modules, and cloud management platforms.

[0060] Software Function Implementation: The software system of the local data processing unit is built on a real-time operating system and runs logic with high real-time requirements. Sensing data processing: Receives multi-dimensional data collected by the intelligent sensing module, performs preprocessing operations such as real-time calibration, anomaly removal, and data format conversion to ensure data quality and provide high-quality input for control command generation; Control command generation: The core algorithm of the running optimization and control module quickly generates optimized control commands based on preprocessed real-time data, and sends them to the dynamically controllable hardware module for execution to ensure the real-time performance of the control. Preliminary fault diagnosis: Real-time monitoring of equipment operating parameters, using preset fault diagnosis rules or simple machine learning models to preliminarily determine whether there are any abnormalities in the equipment. If a minor abnormality is found, a handling instruction can be generated directly. If a serious abnormality is found, it can be promptly reported to the cloud management platform and an early warning can be issued.

[0061] Local data management: Temporarily store real-time data, control commands, fault logs, etc., and adopt a cyclic overwrite storage strategy to ensure that critical data is not lost, while avoiding the exhaustion of local storage resources.

[0062] The cloud management platform is built on cloud computing technology and has functions such as massive data storage, high-performance computing, and remote access, providing support for the long-term operation and optimization of the system.

[0063] The cloud management platform is deployed on a cloud server cluster, which boasts powerful computing capabilities, massive storage capacity, and high reliability. Load balancing technology ensures efficient resource allocation and fault-tolerant backup. The storage system employs a distributed storage architecture to guarantee secure storage and rapid access to massive amounts of operational data and event logs.

[0064] The cloud management platform's software system includes modules for data reception, data storage, data analysis, model optimization, remote monitoring, and user interaction. The system receives operational data and event logs uploaded by the local data processing unit via wired or wireless networks, employs an incremental synchronization strategy to transmit only changed data, thereby reducing network bandwidth consumption, and establishes a data consistency verification mechanism to ensure the consistency between cloud data and local data.

[0065] A combination of distributed database and file storage system is used to store massive amounts of operational data and event logs long-term. Structured data is stored in the distributed database for fast querying and statistical analysis; unstructured data is stored in the file storage system to ensure data integrity.

[0066] Data analysis and model optimization: Leveraging the powerful computing capabilities of the cloud, we conduct in-depth analysis of historical operating data to uncover the correlation between changes in operating conditions, equipment operating characteristics, and processing effects; based on the analysis results, we iteratively optimize the algorithm model of the optimization and control module to improve the model's prediction accuracy and control effect.

[0067] It provides a visual remote monitoring interface, allowing operators to access the platform via computers, mobile devices, and other terminals to view real-time information such as system operation status, processing effects, and equipment status; it supports remotely issuing control commands, modifying system parameters, and viewing historical data reports; and it provides a fault alarm function, so that when a local unit reports a serious fault, the platform will promptly send alarm information to the operator.

[0068] The local data processing unit and the cloud management platform establish a stable communication connection through a standardized communication protocol. During normal operation, the local unit processes data in real time and generates control commands, while periodically uploading operational data and event logs to the cloud platform. The cloud platform receives the data, stores and performs in-depth analysis, and then sends model optimization results and remote control commands back to the local unit, achieving bidirectional data interaction and collaborative optimization. When the network is interrupted, the local unit automatically switches to independent operation mode, relying on local storage and processing capabilities to maintain basic system operation. Once the network is restored, it automatically uploads the operational data and event logs from the interruption period to the cloud platform, ensuring data continuity and integrity.

[0069] In a further embodiment of the present invention, it further includes: a predictive operation and maintenance module, which analyzes the device runtime sequence data collected by the intelligent sensing module based on a machine learning model, identifies the mode of device performance degradation, potential component failure or abnormal operation, and issues an early warning before the failure occurs; for minor anomalies that can be handled automatically, it automatically generates and issues handling instructions to the corresponding execution mechanism; for faults that require manual intervention, it automatically generates an operation and maintenance work order containing fault location, possible causes and handling suggestions. The data includes motor current, vibration spectrum, adjustment mechanism action feedback, and sensor readings.

[0070] In a further embodiment of the present invention, the grit chamber operates according to the following process: The intelligent sensing module continuously collects and preprocesses multi-dimensional data throughout the entire process; The optimization and control module integrates real-time data, simulation results from the digital twin sub-module, and backend process requirements obtained through cross-unit collaborative interfaces, runs a built-in algorithm model, and generates an optimized control instruction set for dynamically adjustable hardware modules. The hardware module can dynamically control the execution of instructions to achieve coordinated adjustment of inlet conditions, vortex morphology, separation environment and outlet conditions; The predictive maintenance module continuously analyzes device status data in the background.

[0071] In a further embodiment of the present invention, the built-in algorithm model also includes an energy consumption optimization algorithm. Based on the preliminary control parameters output by the flow field adaptive optimization algorithm, and combined with the equipment power model and historical energy efficiency data, under the premise of satisfying the constraints of sand settling efficiency and organic matter retention rate, a secondary optimization is performed in the parameter solution space to find the control parameter combination with the lowest total system energy consumption. The operating frequency of the gas sand removal control mechanism and the action amplitude of the drive motors of the cross-section adjustment mechanism and the guide sill are then finely adjusted.

[0072] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A cross-process collaborative flow-guiding sill type intelligent hydraulic vortex grit chamber system, applied to a hydraulic vortex grit chamber, wherein the hydraulic vortex grit chamber comprises: The water intake channel includes a horizontal section and a converging section along the water flow direction. The horizontal section is a straight channel, and the cross-section of the converging section narrows along the flow and the bottom slope increases accordingly. An angle-adjustable guide sill is located at the end of the inlet channel, and its cross-section is arc-shaped. The sedimentation and separation chamber is a circular pool connected to the guide sill. The sand hopper, located at the bottom of the sand separation chamber, has a frustum-shaped structure. The sand collection area is connected to the bottom of the sand hopper; The water outlet channel is connected to the upper part of the sedimentation and sorting chamber; The outlet weir is located at the beginning of the outlet channel; A gas desanding control mechanism is installed inside the cyclone sedimentation tank; The system is characterized by comprising: The intelligent sensing module is used to collect operating parameters in real time, including influent water quality, flow field status in the pool, equipment operating status, and effluent water quality. The optimization and control module is connected to the intelligent sensing module and dynamically generates optimized control commands based on the collected data and through a built-in algorithm model. The dynamically adjustable hardware module includes a cross-section adjustment mechanism, an angle-adjustable guide sill, a height-adjustable outlet weir, and a gas sand removal control mechanism installed in the inlet channel, for executing the optimized control commands. Cross-unit collaboration interface is used to enable data interaction with subsequent processing units.

2. The cross-process collaborative flow-guiding sill type intelligent hydraulic vortex grit chamber system according to claim 1, characterized in that, The dynamically adjustable hardware module includes: A cross-section adjustment mechanism is provided in the constriction section of the inlet channel and driven by a first motor. It is configured to respond to optimization control commands and dynamically adjust the inlet cross-section size and flow velocity distribution by changing the opening degree. An angle-adjustable guide sill, driven by a second motor and with a continuously adjustable angle, is configured to adjust the tilt angle according to algorithm instructions to control the initial vortex shape and intensity formed by the influent in the sedimentation chamber. The gas sand removal control mechanism is located in the lower part of the sand separation chamber. It includes a variable frequency aeration unit, which can dynamically adjust the aeration volume, frequency and distribution mode according to the command to assist in sand separation and prevent excessive settling of organic matter. The third driving structure, which is connected to the outlet weir drive, is used to adjust the weir height according to the command, thereby stabilizing the liquid level and swirling field in the pool.

3. The cross-process collaborative flow-guiding sill type intelligent hydraulic vortex grit chamber system according to claim 1, characterized in that, The intelligent sensing module includes: The sensor array is installed at the water inlet channel to continuously monitor the water inflow, turbidity, sand concentration and organic matter index. Pressure sensors are arrayed on the inner wall of the sedimentation chamber to monitor the dynamic pressure distribution gradient of the swirling flow field at multiple points simultaneously. The underwater imaging unit is installed in the sediment sorting chamber to acquire real-time visualization information on the sedimentation process and flow regime of sand particles; The level detection unit and vibration sensing unit are installed in the sand hopper to monitor the thickness and state of sand accumulation. The liquid level and water quality sensing unit is located at the water outlet.

4. The cross-process collaborative flow-guiding sill type intelligent hydraulic vortex grit chamber system according to claim 1, characterized in that, The optimization and control module includes a digital twin sub-module, which receives and synchronizes multi-dimensional data collected by the intelligent sensing module in real time, driving the virtual model to evolve dynamically. This enables high-fidelity mapping and visualization of the entity's swirling flow field structure, sand particle trajectory, and equipment status in the virtual space. Based on the current state and preset targets, different control command schemes are simulated and deduced in the virtual space to predict their flow field changes and processing effects. The simulation results are then fed back to the optimization decision-making process, providing a predictive basis for generating the final control commands.

5. The cross-process collaborative flow-guiding sill type intelligent hydraulic vortex grit chamber system according to claim 1, characterized in that, The built-in algorithm model includes: The flow field adaptive optimization algorithm takes the real-time water intake condition data and the flow field state data in the pool collected by the intelligent sensing module as input, and calculates and decides the optimal combination of guide sill angle, cross-section adjustment mechanism opening, outlet weir height and gas desanding parameters under the current working conditions through a pre-trained machine learning model or a physical rule-based optimization model, aiming to maximize the sedimentation efficiency and flow field stability. The organic matter retention rate control algorithm, which couples the subsequent process carbon source demand signal from the cross-unit collaborative interface, establishes a correlation model between swirl intensity and organic matter sedimentation loss, and inversely derives and outputs the control amount of the adjustable hardware module, aiming to achieve precise control of the organic matter content in the effluent.

6. The cross-process collaborative flow-guiding sill type intelligent hydraulic vortex grit chamber system according to claim 1, characterized in that, The intelligent sensing module also includes a data calibration and anomaly removal unit, which performs real-time calibration on the data collected by the sensor group, pressure sensor, and underwater imaging unit, and corrects sensor drift errors through a preset physical constraint model; at the same time, it uses the 3σ criterion and a time-series mutation detection algorithm to identify and remove abnormal data. When data from a single sensor is abnormal, redundant sensor channel data or predicted values ​​based on historical data are automatically activated to complete the data.

7. The cross-process collaborative flow-guiding sill type intelligent hydraulic vortex grit chamber system according to claim 1, characterized in that, Also includes: The local data processing unit integrates the edge computing node and is responsible for running the logic of sensing data processing, control command generation, and preliminary fault diagnosis that have high real-time requirements. A cloud management platform is connected to the local unit via a wired or wireless network to receive and store operational data and event logs from the local unit for an extended period.

8. The cross-process collaborative flow-guiding sill type intelligent hydraulic vortex grit chamber system according to claim 1, characterized in that, Also includes: The predictive maintenance module analyzes the device runtime sequence data collected by the intelligent sensing module based on a machine learning model, identifies patterns of device performance degradation, potential component failures, or abnormal operation, and issues early warnings before a failure occurs. For minor anomalies that can be handled automatically, it automatically generates and issues handling instructions to the corresponding execution mechanism. For failures that require manual intervention, it automatically generates maintenance work orders that include fault location, possible causes, and handling suggestions. The data includes motor current, vibration spectrum, adjustment mechanism action feedback, and sensor readings.

9. The cross-process collaborative flow-guiding sill type intelligent hydraulic vortex grit chamber system according to claim 1, characterized in that, The sedimentation tank operates according to the following process: The intelligent sensing module continuously collects and preprocesses multi-dimensional data throughout the entire process; The optimization and control module integrates real-time data, simulation results from the digital twin sub-module, and backend process requirements obtained through cross-unit collaborative interfaces, runs a built-in algorithm model, and generates an optimized control instruction set for dynamically adjustable hardware modules. The hardware module can dynamically control the execution of instructions to achieve coordinated adjustment of inlet conditions, vortex morphology, separation environment and outlet conditions; The predictive maintenance module continuously analyzes device status data in the background.

10. The cross-process collaborative flow-guiding sill type intelligent hydraulic vortex grit chamber system according to claim 1, characterized in that, The built-in algorithm model also includes an energy consumption optimization algorithm. Based on the preliminary control parameters output by the flow field adaptive optimization algorithm, and combined with the equipment power model and historical energy efficiency data, under the premise of satisfying the constraints of sand settling efficiency and organic matter retention rate, a secondary optimization is performed in the parameter solution space to find the control parameter combination with the lowest total system energy consumption. The operating frequency of the gas sand removal control mechanism and the action amplitude of the drive motors of the cross-section adjustment mechanism and the guide sill are then finely adjusted.

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