Intelligent door closing control system and method for pump station main unit working doors

Abstract

This invention discloses an intelligent door closing control system and method for the working gate of a pumping station main unit, relating to the field of water conservancy engineering technology. It includes: a pre-simulation module, which dynamically maps a digital twin model based on multi-dimensional characteristic operating conditions of the source side to obtain a digital twin mirror body, performs multi-field simulation pre-simulation, and generates a pre-simulation instruction set; an allocation module, which matches the damping characteristics of the actuator and external load to the pre-simulation instruction set, generating an excitation drive current timing curve; and a correction module, which executes the door closing action based on the excitation drive current timing curve, providing real-time feedback and correction of the deviation between the working gate's falling trajectory and the pre-simulation instruction set; and synchronously interacts with the digital twin mirror body to generate a door closing motion state data stream. This invention improves the operational stability of the pumping station main unit under emergency flow interruption conditions and reduces the risk of abnormal unit reversal due to door obstruction.

Description

Technical Field

[0001] This invention relates to the field of water conservancy engineering technology, and in particular to an intelligent door closing control system and method for the working gate of a pumping station main unit. Background Technology

[0002] The operating gate of the pumping station's main unit is the core flow-cutting device in the outlet flow channel of a high-lift pumping station. Its closing accuracy directly affects the hydraulic transient characteristics and the safety of the main unit during the pump shutdown transition. If the operating gate cannot quickly cut off the water flow, the unit's reverse rotation speed can easily exceed the rated threshold, posing a serious threat to the mechanical structure. In engineering practice, winch-type or hydraulic high-speed gates combined with PLC timing logic control have become the mainstream. Some pumping stations have begun to introduce digital twin technology for offline condition monitoring and health assessment, forming a digital monitoring capability for the entire equipment lifecycle.

[0003] However, existing pump station gate control schemes still exhibit significant limitations when dealing with complex and variable unsteady flow field environments. Current digital twin models primarily focus on one-way data mapping from physical entities to virtual spaces, lacking forward-looking simulations and closed-loop support for real-time gate closing processes. When facing time-varying conditions such as sudden water level changes and flow velocity fluctuations, existing control logic largely relies on "post-event compensation" after deviations occur, failing to proactively predict future resistance risk distributions and perform dynamic impedance matching. This leads to frequent problems such as gate trajectory deviation and excessive terminal impact energy levels, resulting in insufficient dynamic adaptability of process control. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides an intelligent door closing control system for the working doors of pump station main units to solve the problems of lack of forward-looking simulation and dynamic matching capabilities, resulting in the door closing process being a post-compensation process with poor adaptability.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides an intelligent door closing control system for the working doors of a pumping station main unit, comprising: The intelligent door closing control system for the working doors of the pumping station main unit is characterized by comprising: The sensing module collects multi-physics field operating parameters of the pumping station and obtains multi-dimensional characteristic operating conditions from the source side; The pre-simulation module dynamically maps the digital twin model based on the multi-dimensional characteristic working conditions of the source side, obtains a digital twin mirror body, performs multi-field simulation pre-simulation, and generates a pre-simulation instruction set; The scheduling module matches the damping characteristics of the actuator and the external load to the pre-simulation instruction set and generates the excitation drive current timing curve. The correction module executes the door closing action based on the excitation drive current timing curve, provides real-time feedback and correction of the deviation between the working door's falling trajectory and the pre-simulated instruction set, and synchronously interacts with the digital twin mirror to generate a data stream of the door closing motion state. The control module, based on the data stream of the closed door movement state, identifies the remaining travel and instantaneous momentum of the working door from the bottom sill, and performs end-stage energy dissipation optimization and zero-impact self-locking, outputting a closed door performance evaluation file.

[0007] Preferably, the method for obtaining the source-side multidimensional feature conditions includes: The system collects real-time water levels, flow velocity, ambient temperature, lubricating oil temperature, and initial position parameters of the working gate at the inlet and outlet of the sampling pump station, gate slots, and hoist positions, and simultaneously acquires the main unit's operating status data; it also extracts the corresponding lubricating oil viscosity index based on the lubricating oil temperature and converts the real-time water level into a real-time water level difference. By associating and encapsulating real-time water level difference, flow channel velocity, ambient temperature, lubricating oil viscosity index, initial position parameters of the working gate, and operating status data of the main unit, a multi-dimensional feature set of the source side is obtained.

[0008] Preferably, the method for obtaining the digital twin mirror image includes: Load the pre-stored high-precision pump station working gate digital twin, and align the spatial pose of the pre-stored high-precision pump station working gate digital twin according to the initial pose parameters of the working gate in the source side multi-dimensional feature working condition set. The real-time water level difference, flow velocity, ambient temperature and lubricating oil viscosity index of the source side multi-dimensional characteristic working conditions are used as physical boundary parameters, and the main unit operating status data are used as external load boundaries, which are mapped to the fluid-structure coupling contact surface and fluid domain space of the pre-stored high-precision pump station working gate digital twin. Synchronize the real-time motion parameters and flow field dynamic properties of the pre-stored high-precision pump station working gate digital twin with the source side multi-dimensional feature working condition set to obtain a digital twin mirror body.

[0009] Preferably, the method for generating the pre-rehearsal instruction set includes: Drive the digital twin mirror body to perform multi-field coupling simulation at a simulation evolution speed higher than the actual operating frequency, and simulate the flow field interaction and mechanical structure response of the working gate during the future descent process; The falling position and velocity state of the working gate of the digital twin mirror body during the simulation process are obtained, the pre-simulation trajectory is generated, and the resistance fluctuation range of the digital twin mirror body in the virtual flow field is identified, and the resistance risk distribution is extracted. The simulated trajectory is associated with and encapsulated with the resistance risk distribution to generate a simulated instruction set.

[0010] Preferably, the method for matching the damping characteristics of the actuator with the external load includes: The pre-simulation instruction set is analyzed, the expected running speed of each node in the pre-simulation trajectory is extracted, and the predicted resistance defined by the resistance risk distribution is used as the external load to obtain the damping force compensation strength of each displacement node. The damping force compensation intensity is directed to the magnetorheological damping device of the actuator, and the controlled shear force generated by the magnetorheological damping device is used to counteract the external load. Based on the physical parameter requirements after offsetting at each displacement node, the corresponding excitation current value is obtained, and the excitation drive current timing curve is generated by serializing and recombining according to the time nodes of the pre-simulated trajectory.

[0011] Preferably, the method for real-time feedback correction includes: The actuator is driven to move according to the excitation drive current timing curve, and the position data of the hoist is sampled in real time to obtain the measured falling trajectory of the physical working gate. The measured falling trajectory is compared with the pre-simulated trajectory in the pre-simulated instruction set to identify the displacement and velocity deviation of the physical working gate relative to the desired pose. The proportional-integral-derivative feedback control logic is used to generate a compensation control quantity based on the displacement deviation and velocity deviation, and the compensation control quantity is superimposed on the excitation drive current timing curve. By changing the damping force output of the magnetorheological damping device through the modified excitation current, the motion state of the physical working gate is forced to approach the pre-simulated trajectory.

[0012] Preferably, the method for generating the door closing motion state data stream includes: The measured displacement, measured velocity, and corrected excitation current values ​​of the physical working gate are fed back to the digital twin mirror body, driving the virtual working gate in the digital twin mirror body to perform synchronous pose update. The virtual flow field parameters inside the digital twin mirror body are adjusted by using the corrected excitation current, and the physical contact parameters in the digital twin mirror body are corrected by comparing the deviation between the virtual simulated force and the actual load data. The system collects the real-time force distribution and motion trajectory envelope generated by the digital twin mirror during the interaction process, and encapsulates them with the measured displacement and measured velocity of the physical working door to generate a closed door motion state data stream.

[0013] Preferably, the method for identifying the remaining travel distance and instantaneous momentum of the working gate from the bottom sill includes: The real-time displacement value of the physical working door is extracted from the closed door motion state data stream and compared with the pre-stored bottom sill elevation parameters to identify the spatial distance between the bottom edge of the physical working door and the bottom sill surface, and to obtain the remaining travel distance between the working door and the bottom sill. The measured velocity of the physical working door is extracted from the closed door motion state data stream, and the corresponding working door mass attribute in the digital twin mirror is obtained. The measured velocity and the working door mass attribute are combined to identify the motion inertial impact of the physical working door at the current falling position and obtain the instantaneous momentum.

[0014] Preferably, the method for performing end-level energy dissipation optimization and zero-impact self-locking includes: When the remaining travel distance between the working gate and the bottom sill reaches the preset travel threshold, the magnetorheological damping device is driven to execute nonlinear exponential decay control logic, which converts the kinetic energy of the physical working gate into thermal energy through the shear heat generation of the magnetorheological fluid. After the physical working door contacts the bottom sill and the speed returns to zero, the locking device of the hoist is activated, and zero-impact self-locking is achieved by using mechanical interference and the gravity of the working door. The system aggregates the multi-dimensional characteristic operating conditions set, the pre-simulation instruction set, and the door closing motion state data stream from the source side to generate a door closing performance evaluation file that records the complete trajectory of the door closing process and energy efficiency indicators.

[0015] Secondly, the present invention provides an intelligent door closing control method for the working doors of a pumping station main unit, comprising: Collect multi-physics field operating parameters of the pumping station to obtain multi-dimensional characteristic operating conditions of the source side; Based on the multi-dimensional characteristic working conditions of the source side, the digital twin model is dynamically mapped to obtain a digital twin mirror body to perform multi-field simulation pre-play and generate a pre-play instruction set; The damping characteristics of the actuator and the external load are matched to the pre-trained instruction set to generate the excitation drive current timing curve. The door closing action is executed based on the excitation drive current timing curve, and the deviation between the working door falling trajectory and the pre-rehearsed instruction set is fed back and corrected in real time; and the digital twin mirror is synchronously interacted with to generate a door closing motion state data stream. Based on the data stream of the closed door motion state, the remaining travel and instantaneous momentum of the working door from the bottom sill are identified, and end-level energy dissipation optimization and zero-impact self-locking are performed to output the closed door performance evaluation file.

[0016] The beneficial effects of this invention are as follows: by driving the digital twin mirror body to perform multi-field coupling simulation at a simulation evolution speed higher than the actual operating frequency, a forward-looking simulation of the flow field interaction and mechanical structure response in the future stroke of the working gate is realized; and by combining the analytical pre-simulation instruction set to drive the magnetorheological damping device to generate controlled shear force to counteract the predicted resistance, real-time dynamic matching of the damping characteristics of the actuator and the instantaneous external load is achieved. The two work together to mitigate the deviation of the working gate's trajectory and the degree of impact at the end in complex hydraulic environments, which helps to improve the operational stability of the pump station's main unit under emergency flow interruption conditions and reduce the risk of abnormal reversal of the unit due to obstruction of the gate. At the same time, by utilizing the sensitive response characteristics of magnetorheological media, the stress distribution at the moment the gate touches the bottom is improved, which plays a role in protecting the opening and closing mechanical structure, extending the fatigue life of the equipment, and promoting the hydraulic stability of the pump station's outlet flow channel under transient flow conditions. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of the intelligent door closing control system for the working door of the pump station main unit in this invention; Figure 2 This is a schematic diagram of the intelligent door closing control method for the working door of the pump station main unit in this invention; Figure 3 This is a flowchart illustrating the generation of the pre-render instruction set in this invention; Figure 4 This is a flowchart of the real-time feedback correction process in this invention. Detailed Implementation

[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0020] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0021] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0022] Reference Figure 1 , Figure 2 , Figure 3 and Figure 4As one embodiment of the present invention, this embodiment provides an intelligent door closing control system for the working doors of a pumping station main unit, comprising the following steps: Methods for obtaining multi-dimensional feature conditions from the source side include: The system collects real-time water levels, flow velocity, ambient temperature, lubricating oil temperature, and initial position parameters of the working gate at the inlet and outlet of the sampling pump station, gate slots, and hoist positions, and simultaneously acquires the main unit's operating status data; it also extracts the corresponding lubricating oil viscosity index based on the lubricating oil temperature and converts the real-time water level into a real-time water level difference.

[0023] It should be noted that the real-time water level is obtained by installing submersible water level gauges at the pump station inlet and outlet respectively, and the flow velocity in the flow channel is sensed by using an ultrasonic flow meter embedded in the inner wall of the flow channel; the ambient temperature is measured by using a thermal resistance sensor arranged on the surface of the metal structure of the gate slot, and the lubricating oil temperature is sampled by using a temperature probe implanted in the lubricating oil tank of the hoist; the initial position parameters of the working gate are obtained by using an absolute encoder at the end of the hoist drum shaft, with an example value of 12.5m. At the same time, the operating status data of the main unit, including the real-time current, real-time torque and real-time speed of the main unit, are synchronously obtained from the communication interface of the main unit control cabinet. Based on the sampled lubricating oil temperature, the lubricating oil physical property lookup table stored in the local memory is retrieved. The lubricating oil viscosity value corresponding to the lubricating oil temperature is matched in the lubricating oil physical property lookup table, thereby extracting the lubricating oil viscosity index. For example, when the lubricating oil temperature is 40℃, the extracted lubricating oil viscosity index is 100. At the same time, the real-time water level at the pump station outlet is subtracted from the real-time water level at the pump station inlet, thus converting the real-time water level into a real-time water level difference.

[0024] By associating and encapsulating real-time water level difference, flow channel velocity, ambient temperature, lubricating oil viscosity index, initial position parameters of the working gate, and operating status data of the main unit, a multi-dimensional feature set of the source side is obtained.

[0025] Specifically, the real-time water level difference, flow velocity in the channel, ambient temperature, lubricating oil viscosity index, initial position parameters of the working gate, and operating status data of the main unit are aligned according to a unified time base. The real-time water level difference, flow channel velocity, ambient temperature, lubricating oil viscosity index, initial position parameters of the working gate, and main unit operating status data are combined into a nonlinear data vector package with associated attributes. By adding a synchronization timestamp to the nonlinear data vector package to complete the feature association encapsulation, a source-side multidimensional feature condition set reflecting the multi-physics field characteristics of the pump station operation site is obtained.

[0026] Existing pump station gate control schemes mostly rely on "post-compensation" logic. When dealing with sudden changes in water level and velocity fluctuations caused by unsteady flow fields, they suffer from limitations such as digital twin models being limited to unidirectional mapping, lacking forward-looking prediction and forecasting, and being unable to dynamically match impedance for future resistance risks. This leads to problems such as gate trajectory deviation and excessive terminal impact energy levels. Therefore, this invention constructs a digital twin closed-loop control system with accelerated simulation and feedforward compensation capabilities, achieving forward prediction and accurate matching of resistance risk distribution under complex operating conditions. The specific steps are as follows: The ways to obtain a digital twin include: Load the pre-stored high-precision pump station working gate digital twin, and align the spatial pose of the pre-stored high-precision pump station working gate digital twin according to the initial pose parameters of the working gate in the source side multi-dimensional feature working condition set.

[0027] It should be noted that a 3D laser scanner is used to perform on-site real-scene scanning of the pump station's working gate, gate slot structure, and flow channel geometry to obtain point cloud data. Based on the point cloud data, geometric reconstruction is performed in computer-aided 3D modeling software to construct a high-precision 3D solid model. The high-precision three-dimensional solid model is endowed with physical property parameters of steel, fluid dynamics calculation mesh and mechanical constraint relationship by finite element analysis software, forming a high-precision digital twin of the pump station working gate pre-stored in local memory; Extract the initial pose parameters of the working gate from the multi-dimensional feature set of the source side (for example, the initial pose parameter of the working gate is 12.5m). In the virtual coordinate system, call the virtual working gate in the digital twin of the high-precision pump station working gate, and adjust the elevation coordinates of the virtual working gate according to the initial pose parameters of the working gate (12.5m for example) so that the relative position of the virtual working gate in the virtual gate slot is consistent with the actual height of the working gate in the physical field.

[0028] The real-time water level difference, flow velocity, ambient temperature and lubricating oil viscosity index of the source side multi-dimensional characteristic working conditions are used as physical boundary parameters, and the main unit operating status data are used as external load boundaries, which are mapped to the fluid-structure interaction contact surface and fluid domain space of the pre-stored high-precision pump station working gate digital twin.

[0029] Specifically, real-time water level difference, flow velocity, ambient temperature and lubricating oil viscosity index are extracted from the multi-dimensional characteristic operating conditions of the source side and defined as physical boundary parameters to drive digital twin calculation (for example, the real-time water level difference is taken as 12.7m to define the pressure boundary). Simultaneously, the real-time speed and real-time torque of the host unit are extracted from the host unit's operating status data and defined as the external load boundary that affects the flow field fluctuation in the flow channel (for example, the real-time speed of the host unit is 150 rpm for defining the flow pulsation boundary). The values ​​corresponding to real-time water level difference, flow channel velocity, ambient temperature and lubricating oil viscosity index are written into the fluid domain spatial grid unit of the pre-stored high-precision pump station working gate digital twin in the form of a numerical array. Substitute the real-time torque and real-time speed of the main unit from the main unit's operating status data into the calculation formula based on the torque-force mapping relationship: ; Perform calculations to obtain the dynamic load vector, where... For time Changing dynamic load vector; This refers to the real-time torque of the main unit. The equivalent arm radius of the main unit impeller; The disturbance characteristic frequency is obtained by multiplying the real-time rotational speed of the main unit by the number of blades in the main unit. For example, if the real-time rotational speed of the main unit is 150 rpm and the number of blades in the main unit is 4, the calculated disturbance characteristic frequency is 10 Hz. It is a sine function.

[0030] At the same time, the dynamic load vector is applied to the fluid-structure interaction contact surface of the virtual working gate in the pre-stored high-precision pump station working gate digital twin (for example, mapping the dynamic load vector to the time-varying pressure load value on the grid node of the virtual working gate surface), so that the surface of the virtual working gate is subjected to the hydraulic excitation force and surface friction force consistent with the physical field.

[0031] Synchronize the real-time motion parameters and flow field dynamic properties of the pre-stored high-precision pump station working gate digital twin with the source side multi-dimensional feature working condition set to obtain a digital twin mirror body.

[0032] Specifically, the fluid dynamics solver and multibody dynamics solver in the pre-stored high-precision pump station working gate digital twin are driven into a coupled ready state. The two solvers are automatically encapsulated and integrated by the finite element analysis software according to the fluid domain mesh and mechanical constraint relationship, and a two-way data transmission channel is established through a memory sharing mechanism. The initial pose parameters of the working gate and the real-time rotation speed of the main unit are extracted from the multi-dimensional feature conditions of the source side and defined as real-time motion parameters. They are written into the multibody dynamics solver as the initial motion values ​​of the initial calculation step size, so that the pose and velocity state of the virtual working gate at the beginning of the simulation are consistent with the physical site. At the same time, the real-time water level difference and flow velocity of the channel are extracted from the multi-dimensional feature conditions of the source side and defined as the dynamic properties of the flow field. They are written into the fluid dynamics solver as boundary conditions and initial flow field parameters, so that the pressure distribution and velocity characteristics of the virtual flow field are aligned with the current hydraulic state of the pump station site. After the real-time motion parameters and flow field dynamic properties are injected, the virtual operating state of the pre-stored high-precision pump station working gate digital twin is established with the physical evolution process of the pump station operation site, forming a digital twin mirror body that can reflect the current force and displacement characteristics of the working gate in real time.

[0033] The methods for generating a rehearsal instruction set include: The digital twin mirror image is driven to perform multi-field coupling simulation at a simulation evolution speed higher than the actual operating frequency, simulating the flow field interaction and mechanical structure response of the working gate during its future descent.

[0034] Specifically, by adjusting the logic frequency of the clock step inside the digital twin mirror body, the simulation time step is set to a value much smaller than the physical sampling period, so that the fluid dynamics solver and multibody dynamics solver inside the digital twin mirror body can perform iterative calculations at a speed higher than the real-time evolution of the physical world. For example, the simulation evolution speed is 10 times the actual operating frequency of the physical world, that is, in the past second in the physical world, the digital twin mirror image has completed the prediction of the working conditions in the next 10 seconds. In accelerated simulation calculations, multi-field coupled simulation refers to simultaneously solving the fluid dynamics equations and the multibody dynamics equilibrium equations within the same computational step, enabling real-time interaction between the variables of the two physical fields at the interface. The specific solution process is as follows: The fluid dynamics solver invokes the fluid dynamics equations based on the continuity equation and the Navier-Stokes equations to solve for the pressure and velocity fields within the fluid domain. The expressions are as follows: ; In the formula, This represents the fluid density, with an example value of 1000 kg / m³. This represents the fluid velocity vector, i.e., the velocity field of the fluid domain; Indicates the calculation time step; Represents partial differential operators; Represents the gradient operator; Represents the pressure field in the fluid domain; Indicates the dynamic viscosity of a fluid; Represents the Laplace operator; This represents the force per unit mass acting on a fluid element. Simultaneously, the multibody dynamics solver invokes the multibody dynamics equilibrium equations based on Newton's second law to solve the motion response of the virtual working gate, the expression of which is: ; In the formula, The mass matrix is ​​used to describe the motion inertia of the virtual working gate. This represents the damping matrix, used to describe mechanical frictional resistance and viscous damping; This represents the stiffness matrix, used to describe the ability of the virtual working gate to resist elastic deformation; Represents the displacement vector; Indicates the instantaneous velocity of the virtual working gate; This represents the instantaneous acceleration of the virtual working gate; The fluid force is used as an input load term to drive the solution of the equations; Among them, the mass matrix Damping matrix With stiffness matrix The construction method is as follows: based on the mesh generation results of the high-precision three-dimensional solid model and the pre-stored material physical property parameters, the standard matrix assembly process of the finite element analysis software is used to automatically generate the matrix, and the measured mechanical parameters such as the friction of the door slot side rail and the reference damping of the magnetorheological damping device are coupled into the matrix coefficients in the form of boundary conditions. Within each computational step, the cyclic process of multi-field coupling simulation is as follows: First, the fluid dynamics equations are discretized and iteratively calculated using the finite volume method to obtain the dynamic pressure of each grid node in the virtual flow field. Then, the dynamic pressure is integrated along the fluid-structure interaction surface of the virtual working gate to obtain the instantaneous pressure field distribution. Subsequently, the instantaneous pressure field distribution is transmitted to the multibody dynamics solver through a bidirectional data transmission channel established by the memory sharing mechanism. As an external load, it drives the virtual working gate to overcome mechanical constraints and generate instantaneous displacement and acceleration, thereby simulating nonlinear excitation phenomena. Simultaneously, the multibody dynamics solver feeds back the instantaneous pose changes generated by the virtual gate to the fluid dynamics solver. The fluid dynamics solver uses dynamic meshing technology to reconstruct the fluid domain mesh shape in real time based on the pose changes, in order to correct the flow field dynamic properties for the next step. This completes a full coupled loop of flow field interaction and mechanical structure response, and continues to evolve under the drive of an accelerating clock, simulating the flow field interaction and mechanical structure response of the gate throughout its future descent. During the accelerated simulation process, the digital twin mirror continuously outputs the pose, velocity, and force data of the virtual working gate at various future moments, providing a data foundation for subsequent trajectory extraction and resistance risk identification.

[0035] The system acquires the falling position and velocity state of the working gate of the digital twin mirror image during the simulation process, generates a pre-simulation trajectory, identifies the resistance fluctuation range of the digital twin mirror image in the virtual flow field, and extracts the resistance risk distribution.

[0036] It should be noted that the elevation coordinates and instantaneous velocity values ​​of each set of virtual working gates generated in the accelerated simulation calculation of the digital twin mirror body are extracted in real time and connected according to the time sequence to form a pre-simulated trajectory reflecting the entire process of the working gate falling from its initial pose to the bottom sill. Simultaneously, by monitoring the total pressure value of the fluid-structure interaction contact surface output by the fluid dynamics solver, the resistance fluctuation range of the digital twin mirror body in the virtual flow field is identified. Specifically, when the simulated working gate of the digital twin mirror body falls to a position close to the center of the flow channel, if the fluid dynamics solver detects that the rate of change of the hydraulic adsorption force generated at the bottom edge of the virtual working gate exceeds a preset fluctuation threshold (an example value of 15%), then the displacement coordinate of that segment is recorded and defined as the resistance risk distribution. For example, the resistance risk distribution is exemplified as a sudden increase of 20% in the hydraulic adsorption force due to the increased flow velocity in the range of 3.5m to 2.5m from the bottom sill when the gate falls.

[0037] The simulated trajectory is associated with and encapsulated with the resistance risk distribution to generate a simulated instruction set.

[0038] Specifically, the elevation coordinates and instantaneous velocity values ​​of each set contained in the pre-simulation trajectory are extracted from the accelerated simulation results of the digital twin mirror image. Combined with the displacement coordinates of the resistance fluctuation range and the predicted resistance increment value identified in the resistance risk distribution, a hash algorithm is used to establish a "displacement-expected velocity-predicted resistance" mapping table indexed by the displacement sequence to complete the association and encapsulation of trajectory and risk. The mapping table is arranged according to the time sequence of the pre-simulation trajectory and a displacement trigger tag and a synchronous execution timestamp are added to generate a pre-simulation instruction set with dual constraints of time and position.

[0039] Methods for matching the damping characteristics of the actuator with the external load include: The pre-simulation instruction set is analyzed, the expected running speed of each node in the pre-simulation trajectory is extracted, and the predicted resistance defined by the resistance risk distribution is used as the external load to obtain the damping force compensation strength of each displacement node.

[0040] It should be noted that, from the “displacement-expected velocity-predicted resistance” mapping table of the pre-simulation instruction set, each node is parsed in the order of displacement trigger labels to extract the expected running velocity and predicted resistance increment values ​​corresponding to each displacement node. The predicted resistance increment is superimposed on the basic hydraulic resistance to obtain the total external load. Based on the force balance condition corresponding to the expected speed of the gate at each displacement node, that is, the difference between the total external load and the required damping force should be equal to the product of the gate's mass attribute and the expected acceleration. Within the range where the expected speed at each node is constant, the expected acceleration is zero, and the force balance is simplified to the difference between the total external load and the required damping force being equal to zero. The required damping force compensation intensity at each displacement node is then calculated. For example, when the predicted resistance increment causes the total external load to reach 1.3 times the rated value at a certain node, the calculated damping force compensation intensity needs to be reduced by the corresponding amplitude to maintain the gate's falling speed stable near the expected operating speed.

[0041] The magnetorheological damping device that directs the damping force compensation intensity towards the actuator utilizes the controlled shear force generated by the magnetorheological damping device to counteract the external load.

[0042] Specifically, the damping force compensation intensity at each displacement node is converted into the target damping force output value of the magnetorheological damping device. The magnetorheological damping device adjusts the current of its internal excitation coil to change the shear yield stress of the magnetorheological fluid, thereby generating a controlled shear force that matches the target damping force to offset the disturbance of the working gate's falling speed caused by external load fluctuations. For example, in the displacement range of 3.5m to 2.5m marked by the resistance risk distribution, due to a sudden 20% increase in hydraulic adsorption force, the magnetorheological damping device simultaneously reduces the damping force output (for example, the target damping force is reduced from 500N to 400N, corresponding to a reduction in the excitation current from 0.8A to 0.65A), so that the resultant force on the working gate in this range remains dynamically balanced.

[0043] Based on the physical parameter requirements after offsetting at each displacement node, the corresponding excitation current value is obtained, and the excitation drive current timing curve is generated by serializing and recombining according to the time nodes of the pre-simulated trajectory.

[0044] Specifically, based on the preset damping force-excitation current mapping characteristic curve of the magnetorheological damping device, the target damping force value corresponding to each displacement node is converted into an excitation current value. For example, the damping force-excitation current mapping characteristic curve is obtained through the factory calibration data or bench test data of the magnetorheological damping device and stored in the local memory. The corresponding excitation current value is retrieved and matched under different damping force requirements. For example, the excitation current is 0.8A when the target damping force is 500N and 0.5A when the target damping force is 300N. The excitation current values ​​of each displacement node are arranged according to the time sequence of the pre-simulation trajectory in the pre-simulation instruction set and bound to the displacement trigger tags one by one. The arranged excitation current sequence is smoothed by interpolation to eliminate the current step change between adjacent nodes, forming a continuously controllable excitation drive current timing curve on the time axis and displacement axis. This excitation drive current timing curve is directly output to the drive power unit of the magnetorheological damping device to provide a feedforward control reference for the execution of the door closing action. By utilizing a simulated evolution speed higher than the actual frequency, the control system acquires the ability to "predict the future," successfully transforming traditional passive hysteresis regulation into feedforward precision control based on resistance risk distribution. Through real-time impedance matching between the magnetorheological damping device and the predicted load, the interference of nonlinear excitation and sudden changes in hydraulic adsorption force on the stability of the gate can be effectively eliminated, ensuring that the physical working gate always falls strictly in line with the pre-simulated expected trajectory, significantly improving the dynamic adaptability of the closing process, and laying a solid motion state foundation for achieving zero-impact self-locking at the end.

[0045] Traditional pump station gate control lacks effective correction mechanisms during execution, and digital twin models often become disconnected from the physical entity during operation, failing to perform real-time self-calibration based on field measurement data. This leads to limitations such as continuous drift in control accuracy and poor consistency between virtual and real mapping when facing environmental disturbances. Therefore, this invention constructs a feedback control logic based on four-way parallel compensation and a virtual-real synchronization parameter correction mechanism to achieve dynamic forced correction of the physical trajectory and online evolution of model accuracy. The specific steps are as follows: Methods for providing real-time feedback and correction include: The actuator is driven by the excitation drive current timing curve, and the position data of the gate is sampled in real time to obtain the measured falling trajectory of the physical working gate.

[0046] Specifically, the excitation drive current timing curve is input into the drive power supply unit of the magnetorheological damping device. The drive power supply unit outputs the excitation current according to the time node and current amplitude set by the excitation drive current timing curve, thereby controlling the magnetorheological damping device to generate the corresponding damping force. Meanwhile, the elevation position data of the working door is collected in real time by the absolute encoder at the end of the opening and closing machine drum shaft at a preset sampling frequency (100Hz for example); the discrete position data points sampled in real time are arranged according to the timestamp to form a measured falling trajectory that reflects the actual falling process of the physical working door.

[0047] The measured falling trajectory is compared with the pre-simulated trajectory in the pre-simulated instruction set to identify the displacement and velocity deviations of the physical working gate relative to the desired pose.

[0048] Specifically, based on each time node of the pre-simulation trajectory in the pre-simulation instruction set, the measured displacement value of the physical working gate and the expected displacement value of the virtual working gate at the same timestamp are extracted, and the difference between the two is calculated as the displacement deviation. Meanwhile, the measured velocity and the expected velocity are obtained by performing time differentiation on the measured displacement sequence and the expected displacement sequence respectively, and the difference between the two is calculated as the velocity deviation; the displacement deviation and the velocity deviation together constitute the error input of real-time feedback correction.

[0049] The proportional-integral-derivative feedback control logic is used to generate a compensation control quantity based on the displacement deviation and velocity deviation, and the compensation control quantity is superimposed on the excitation drive current timing curve.

[0050] Specifically, displacement deviation and velocity deviation are simultaneously introduced into a proportional-integral-derivative controller. The controller has four parallel compensation paths, and the outputs of each path are linearly superimposed to form the compensation current. The first path is a speed proportional path, which multiplies the current speed deviation by the speed proportional gain coefficient and provides an instantaneous response to the speed deviation amplitude: when the measured speed is too low (e.g., measured speed 0.18 m / s, desired speed 0.20 m / s, speed deviation is -0.02 m / s), a negative correction is output to reduce the damping force and accelerate the fall (e.g., the reference excitation current is reduced from 0.8A to 0.72A); when the speed is too high (e.g., measured speed 0.22 m / s, speed deviation is +0.02 m / s), a positive correction is output to increase the damping force and slow down the fall (e.g., the reference excitation current is increased from 0.8A to 0.88A). The second path is the speed integral path, which multiplies the time accumulation of speed deviation by the speed integral gain coefficient to eliminate the persistent steady-state speed deviation. The accumulation increases with the duration of the deviation until the speed recovers to the desired value (e.g., the desired speed of 0.20 m / s), after which the current output is maintained as a constant bias. The third path is the velocity differential path, which extracts the rate of change of velocity deviation and multiplies it by the velocity differential gain coefficient to predict the future trend of the deviation: when the deviation increases, it outputs a suppression amount, and when it converges, it outputs a reverse compensation amount to prevent overcorrection. The fourth path is the displacement proportional path, which multiplies the current displacement deviation by the displacement proportional gain coefficient to directly respond to the spatial position error: when the working gate lags, it outputs a negative correction to accelerate catching up; when it is ahead, it outputs a positive correction to slow down the fall. The displacement proportional gain coefficient is usually greater than the speed proportional gain coefficient to ensure that the spatial deviation is corrected first. All the above gain coefficients were obtained through offline co-simulation calibration with the digital twin mirror. The outputs of the four paths are linearly summed in the superposition unit to generate the total compensation current, which is then algebraically superimposed with the reference excitation current value corresponding to the current time node in the excitation drive current timing curve to obtain the corrected excitation current value. Output limiting protection is set during the superposition process: when the superposition result exceeds the allowable operating current range of the drive power supply unit (for example, the allowable operating current range of the magnetorheological damping device drive power supply is 0A to 2A; if the superposition result is -0.1A, it is clamped at 0A; if it is 2.3A, it is clamped at 2A), it is automatically clamped to the upper or lower limit value to prevent overcurrent or undercurrent.

[0051] By changing the damping force output of the magnetorheological damping device through the modified excitation current, the motion state of the physical working gate is forced to approach the pre-simulated trajectory.

[0052] It should be noted that the corrected excitation current value is written into the drive power supply unit of the magnetorheological damping device in real time. The drive power supply unit adjusts the output current to change the shear yield stress of the magnetorheological fluid inside the magnetorheological damping device, thereby adjusting the output amplitude of the damping force. When the measured trajectory lags behind the pre-simulated trajectory, the damping force is reduced to accelerate the fall. When the measured trajectory leads the pre-simulated trajectory, the damping force is increased to decelerate the fall. Through the dynamic adjustment of the damping force, the motion state of the physical working gate is forced to gradually converge to the vicinity of the pre-simulated trajectory.

[0053] The methods for generating a data stream of the closed door motion state include: The measured displacement, measured velocity, and corrected excitation current values ​​of the physical working gate are fed back to the digital twin mirror body, driving the virtual working gate in the digital twin mirror body to perform synchronous pose update.

[0054] It should be noted that within each physical sampling period, the measured displacement, measured velocity, and currently corrected excitation current value of the physical working gate are sent to the multibody dynamics solver of the digital twin mirror body via the data transmission bus. The multibody dynamics solver uses the measured displacement and measured velocity as the pose constraint of the virtual working gate, covering the pose state obtained from the original simulation calculation, so that the virtual working gate and the physical working gate remain synchronized in spatial position.

[0055] The virtual flow field parameters inside the digital twin mirror body are adjusted by using the modified excitation current, and the physical contact parameters in the digital twin mirror body are corrected by comparing the deviation between the virtual simulated force and the actual load data.

[0056] Specifically, the corrected excitation current value is written into the digital twin mirror body, which drives the multibody dynamics solver to update the damping force boundary conditions of the virtual working gate, so that the damping force setting value of the virtual working gate is consistent with the actual output of the magnetorheological damping device in the physical field, thereby adjusting the hydrodynamic response coupled with the motion of the working gate in the virtual flow field. Simultaneously, the actual damping force corresponding to the corrected excitation current value is compared with the theoretical damping force calculated by the multibody dynamics solver in the digital twin mirror body based on the current virtual working gate motion state to calculate the damping force deviation; the pressure distribution integral result on the fluid-structure interaction contact surface of the virtual working gate is compared with the measured local water pressure data obtained by an exemplary pressure sensor array installed on the gate surface to calculate the water pressure deviation. By using the damping force deviation and water pressure deviation as correction factors, the damping characteristic parameters corresponding to the damping matrix in the digital twin mirror body and the friction coefficient of the fluid-structure interaction contact surface are updated online, so that the model accuracy of the digital twin mirror body continues to improve as the door closing process progresses.

[0057] The system collects the real-time force distribution and motion trajectory envelope generated by the digital twin mirror during the interaction process, and encapsulates them with the measured displacement and measured velocity of the physical working door to generate a closed door motion state data stream.

[0058] Specifically, during the virtual-real synchronous interaction process, the real-time pressure distribution cloud map and hydrodynamic load resultant force vector of the virtual working gate surface output by the fluid dynamics solver of the digital twin mirror body, the upper and lower envelope lines of the virtual working gate motion trajectory output by the multibody dynamics solver, and the contact force data of each mechanical constraint pair are collected. These data are then encapsulated with the measured displacement sequence, measured velocity sequence, and corrected excitation current sequence of the physical working gate according to a unified timestamp to form a closed gate motion state data stream containing virtual-real bidirectional calibration information, which serves as the basis for determining the end-of-line locking control in the next stage. By utilizing a four-way parallel PID compensation algorithm that incorporates both velocity and displacement constraints, the sensitivity to deviation response under complex excitation environments is significantly enhanced, ensuring that the physical working gate can still quickly converge to the pre-simulated trajectory under strong disturbances. At the same time, by feeding back the measured load and correction current to the mirror body to perform "secondary calibration", the digital twin model is transformed from "static preset" to "dynamic adaptive", which not only eliminates the model accumulation error in the virtual-real interaction process, but also provides high-fidelity full-dimensional data stream support for subsequent identification of the remaining stroke and precise control of instantaneous momentum.

[0059] Methods for identifying the remaining travel and instantaneous momentum of the working gate from the bottom sill include: The real-time displacement value of the physical working door is extracted from the closed door motion state data stream and compared with the pre-stored bottom sill elevation parameters to identify the spatial distance between the bottom edge of the physical working door and the bottom sill surface, thus obtaining the remaining travel distance between the working door and the bottom sill.

[0060] Specifically, the elevation coordinates of the physical working door at the current moment are extracted from the measured displacement sequence of the closed door movement state data stream. The pre-stored bottom sill elevation parameter is read from the local memory. This bottom sill elevation parameter is obtained by calibrating the pump station flow channel on-site during a 3D laser scanner scan. An exemplary value of 0.0m (with the bottom sill top surface as the elevation reference zero point) corresponds to the elevation of the bottom sill top surface. The current elevation coordinates of the bottom edge of the physical working door are subtracted from the bottom sill elevation parameter, and the compression allowance of the bottom edge seal of the working door is deducted. An exemplary value of 0.02m is taken to calculate the remaining travel distance between the working door and the bottom sill.

[0061] The measured velocity of the physical working door is extracted from the closed door motion state data stream, and the corresponding working door mass attribute in the digital twin mirror is obtained. The measured velocity and the working door mass attribute are combined to identify the motion inertial impact of the physical working door at the current falling position and obtain the instantaneous momentum.

[0062] Specifically, the measured velocity of the physical working gate at the current moment is extracted from the measured velocity sequence of the closed-door motion state data stream, and the mass attribute of the working gate is read from the multibody dynamics solver of the digital twin mirror body. The measured velocity is multiplied by the mass attribute of the working gate to obtain the instantaneous momentum of the physical working gate at the current falling position. This instantaneous momentum reflects the potential impact energy level of the working gate on the bottom sill in the current motion state, providing a quantitative basis for the selection of end-level energy dissipation strategy.

[0063] Methods for performing end-level energy dissipation optimization and zero-impact self-locking include: When the remaining travel distance between the working gate and the bottom sill reaches the preset travel threshold, the magnetorheological damping device is driven to execute nonlinear exponential decay control logic, which converts the kinetic energy of the physical working gate into thermal energy through the shear heat generation of the magnetorheological fluid.

[0064] Specifically, the preset travel threshold is set to 5% of the full travel of the working door. For example, when the full travel of the working door is 15m, the preset travel threshold is 0.75m from the bottom sill. When the remaining travel is less than or equal to the preset travel threshold, the end-level energy dissipation mode is activated.

[0065] In the end-level dissipation mode, the excitation current is adjusted according to the following nonlinear exponential decay control law: ; in, It is the current moment. The excitation current applied to the magnetorheological damping device, It is the fundamental sustaining current of the magnetorheological damping device. It is the current excitation current value at the moment of triggering the end control. It is the base of the natural logarithm (approximately 2.71828). It is the steepness coefficient, dimensionless (example value 3.0), determined through offline simulation optimization using a digital twin mirror, and determines the rate at which the excitation current increases as the remaining stroke decreases. It is the current moment. The remaining travel distance between the working gate and the bottom sill. It is a preset travel threshold; As the remaining stroke gradually decreases from the preset stroke threshold to zero, the excitation current continues to rise exponentially, driving the shear yield stress of the magnetorheological fluid to increase rapidly, causing the remaining kinetic energy of the working gate to be converted into heat energy and dissipated through the shear heat generation effect of the magnetorheological fluid.

[0066] After the physical working door contacts the bottom sill and the speed returns to zero, the locking device of the hoist is activated, and zero-impact self-locking is achieved by using mechanical interference and the gravity of the working door.

[0067] Specifically, when the measured speed of the physical working door remains zero and the calculated value of the remaining stroke is zero, it is determined that the working door has reached the bottom sill and achieved zero impact contact. Immediately send a locking command to the hoist locking device. The hoist locking device applies mechanical interference constraint to the hoist drum through a ratchet-pawl mechanism or a hydraulic brake device to prevent the drum from rotating in the opposite direction under the action of gravity. Self-locking is achieved by using the gravity of the working gate itself and the mechanical constraint of the locking device. After the self-locking is completed, the energy supply to the magnetorheological damping device and the gate hoist drive unit is cut off, completing the full power execution of the closed-loop cycle.

[0068] The system aggregates the multi-dimensional characteristic operating conditions set, the pre-simulation instruction set, and the door closing motion state data stream from the source side to generate a door closing performance evaluation file that records the complete trajectory of the door closing process and energy efficiency indicators.

[0069] Specifically, based on all initial operating parameters in the multi-dimensional characteristic operating condition set of the source side, the pre-simulation trajectory and resistance risk distribution data in the pre-simulation instruction set, and the full-process measured trajectory and excitation current adjustment records recorded in the door closing motion state data stream; and fully aligned and fused according to the time axis to generate a complete trajectory data table of the door closing process, and calculate the door closing performance evaluation index, including the total door closing time, the maximum deviation between the pre-simulation trajectory and the actual trajectory, the peak residual velocity at the end contact moment, and the cumulative energy consumption of the magnetorheological damping device during the door closing process; The complete trajectory data table and the door closing performance evaluation index are packaged into a structured data file and written to a dedicated partition of local storage in read-only format. This generates a door closing performance evaluation file that records the backtracking data of the entire door closing process and the energy efficiency evaluation results, serving as a permanent record for pump station operation, maintenance, and accident tracing.

[0070] This embodiment also provides an intelligent door closing control method for the working doors of the pump station main unit, including: Collect multi-physics field operating parameters of the pumping station to obtain multi-dimensional characteristic operating conditions of the source side; Based on the multi-dimensional characteristic working conditions of the source side, the digital twin model is dynamically mapped to obtain a digital twin mirror body to perform multi-field simulation pre-play and generate a pre-play instruction set; The damping characteristics of the actuator and the external load are matched to the pre-trained instruction set to generate the excitation drive current timing curve. The door closing action is executed based on the excitation drive current timing curve, and the deviation between the working door falling trajectory and the pre-rehearsed instruction set is fed back and corrected in real time; and the digital twin mirror is synchronously interacted with to generate a door closing motion state data stream. Based on the data stream of the closed door motion state, the remaining travel and instantaneous momentum of the working door from the bottom sill are identified, and end-level energy dissipation optimization and zero-impact self-locking are performed to output the closed door performance evaluation file.

[0071] This embodiment also provides a computer device applicable to the intelligent door closing control system for the working gate of a pumping station main unit, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the intelligent door closing control system for the working gate of the pumping station main unit as proposed in the above embodiment.

[0072] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0073] This embodiment also provides a storage medium storing a computer program. When executed by a processor, the program implements the intelligent door closing control system for the working gate of the pump station main unit as proposed in the above embodiment. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0074] In summary, this invention achieves forward-looking simulation of flow field interaction and mechanical structure response during the future travel of the working gate by driving the digital twin mirror body to perform multi-field coupling simulation at a simulation evolution speed higher than the actual operating frequency; and by combining the analytical pre-simulation instruction set to drive the magnetorheological damping device to generate controlled shear force to counteract the predicted resistance, thereby achieving real-time dynamic matching between the damping characteristics of the actuator and the instantaneous external load. The two work together to mitigate the deviation of the working gate's trajectory and the degree of impact at the end in complex hydraulic environments, which helps to improve the operational stability of the pump station's main unit under emergency flow interruption conditions and reduce the risk of abnormal reversal of the unit due to obstruction of the gate. At the same time, by utilizing the sensitive response characteristics of magnetorheological media, the stress distribution at the moment the gate touches the bottom is improved, which plays a role in protecting the opening and closing mechanical structure, extending the fatigue life of the equipment, and promoting the hydraulic stability of the pump station's outlet flow channel under transient flow conditions.

[0075] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A pump station host group working door intelligent closing control system, characterized in that, include: The sensing module collects multi-physics field operating parameters of the pumping station and obtains multi-dimensional characteristic operating conditions from the source side; The methods for obtaining source-side multidimensional feature conditions include: The system collects real-time water levels, flow velocity, ambient temperature, lubricating oil temperature, and initial position parameters of the working gate at the inlet and outlet of the sampling pump station, gate slots, and hoist positions, and simultaneously acquires the main unit's operating status data; it also extracts the corresponding lubricating oil viscosity index based on the lubricating oil temperature and converts the real-time water level into a real-time water level difference. The real-time water level difference, flow channel velocity, ambient temperature, lubricating oil viscosity index, initial position parameters of the working gate, and main unit operating status data are correlated and encapsulated to obtain a multi-dimensional feature set of the source side. The pre-simulation module dynamically maps the digital twin model based on the multi-dimensional characteristic working conditions of the source side, obtains a digital twin mirror body, performs multi-field simulation pre-simulation, and generates a pre-simulation instruction set; The methods for obtaining a digital twin mirror image include: Load the pre-stored high-precision pump station working gate digital twin, and align the spatial pose of the pre-stored high-precision pump station working gate digital twin according to the initial pose parameters of the working gate in the source side multi-dimensional feature working condition set. The real-time water level difference, flow velocity, ambient temperature and lubricating oil viscosity index of the source side multi-dimensional characteristic working conditions are used as physical boundary parameters, and the main unit operating status data are used as external load boundaries, which are mapped to the fluid-structure coupling contact surface and fluid domain space of the pre-stored high-precision pump station working gate digital twin. Synchronize the real-time motion parameters and flow field dynamic properties of the pre-stored high-precision pump station working gate digital twin with the source side multi-dimensional feature working condition set to obtain a digital twin mirror body; The methods for generating the pre-rehearsal instruction set include: Drive the digital twin mirror body to perform multi-field coupling simulation at a simulation evolution speed higher than the actual operating frequency, and simulate the flow field interaction and mechanical structure response of the working gate during the future descent process; The falling position and velocity state of the working gate of the digital twin mirror body during the simulation process are obtained, the pre-simulation trajectory is generated, and the resistance fluctuation range of the digital twin mirror body in the virtual flow field is identified, and the resistance risk distribution is extracted. The simulated trajectory is associated with and encapsulated with the distribution of resistance risks to generate a simulated instruction set; The scheduling module matches the damping characteristics of the actuator and the external load to the pre-simulation instruction set and generates the excitation drive current timing curve. The correction module executes the door closing action based on the excitation drive current timing curve, provides real-time feedback and correction of the deviation between the working door's falling trajectory and the pre-simulated instruction set, and synchronously interacts with the digital twin mirror to generate a data stream of the door closing motion state. The control module, based on the data stream of the closed door movement state, identifies the remaining travel and instantaneous momentum of the working door from the bottom sill, and performs end-stage energy dissipation optimization and zero-impact self-locking, outputting a closed door performance evaluation file.

2. The intelligent door closing control system for the master group of pump station doors as defined in claim 1, wherein, The methods for matching the damping characteristics of the actuator with the external load include: The pre-simulation instruction set is analyzed, the expected running speed of each node in the pre-simulation trajectory is extracted, and the predicted resistance defined by the resistance risk distribution is used as the external load to obtain the damping force compensation strength of each displacement node. The damping force compensation intensity is directed to the magnetorheological damping device of the actuator, and the controlled shear force generated by the magnetorheological damping device is used to counteract the external load. Based on the physical parameter requirements after offsetting at each displacement node, the corresponding excitation current value is obtained, and the excitation drive current timing curve is generated by serializing and recombining according to the time nodes of the pre-simulated trajectory.

3. The intelligent door closing control system for the working gate of the pump station main unit as described in claim 1, characterized in that, The methods for real-time feedback and correction include: The actuator is driven to move according to the excitation drive current timing curve, and the position data of the hoist is sampled in real time to obtain the measured falling trajectory of the physical working gate. The measured falling trajectory is compared with the pre-simulated trajectory in the pre-simulated instruction set to identify the displacement and velocity deviation of the physical working gate relative to the desired pose. The proportional-integral-derivative feedback control logic is used to generate a compensation control quantity based on the displacement deviation and velocity deviation, and the compensation control quantity is superimposed on the excitation drive current timing curve. By changing the damping force output of the magnetorheological damping device through the modified excitation current, the motion state of the physical working gate is forced to approach the pre-simulated trajectory.

4. The intelligent door closing control system for the working door of the pump station main unit as described in claim 1, characterized in that, The methods for generating the closed-door motion state data stream include: The measured displacement, measured velocity, and corrected excitation current values ​​of the physical working gate are fed back to the digital twin mirror body, driving the virtual working gate in the digital twin mirror body to perform synchronous pose update. The virtual flow field parameters inside the digital twin mirror body are adjusted by using the corrected excitation current, and the physical contact parameters in the digital twin mirror body are corrected by comparing the deviation between the virtual simulated force and the actual load data. The system collects the real-time force distribution and motion trajectory envelope generated by the digital twin mirror during the interaction process, and encapsulates them with the measured displacement and measured velocity of the physical working door to generate a closed door motion state data stream.

5. The intelligent door closing control system for the working door of the pump station main unit as described in claim 1, characterized in that, The methods for identifying the remaining travel distance and instantaneous momentum of the working gate from the bottom sill include: The real-time displacement value of the physical working door is extracted from the closed door motion state data stream and compared with the pre-stored bottom sill elevation parameters to identify the spatial distance between the bottom edge of the physical working door and the bottom sill surface, and to obtain the remaining travel distance between the working door and the bottom sill. The measured velocity of the physical working door is extracted from the closed door motion state data stream, and the corresponding working door mass attribute in the digital twin mirror is obtained. The measured velocity and the working door mass attribute are combined to identify the motion inertial impact of the physical working door at the current falling position and obtain the instantaneous momentum.

6. The intelligent door closing control system for the working door of the pump station main unit as described in claim 1, characterized in that, The methods for performing end-level energy dissipation optimization and zero-impact self-locking include: When the remaining travel distance between the working gate and the bottom sill reaches the preset travel threshold, the magnetorheological damping device is driven to execute nonlinear exponential decay control logic, which converts the kinetic energy of the physical working gate into thermal energy through the shear heat generation of the magnetorheological fluid. After the physical working door contacts the bottom sill and the speed returns to zero, the locking device of the hoist is activated, and zero-impact self-locking is achieved by using mechanical interference and the gravity of the working door. The system aggregates multi-dimensional characteristic operating condition sets, pre-simulation instruction sets, and door closing motion state data streams from the source side to generate a door closing performance evaluation file that records the complete trajectory of the door closing process and energy efficiency indicators.

7. A method for intelligent closing control of the working doors of a pumping station main unit, based on the intelligent closing control system for the working doors of a pumping station main unit as described in any one of claims 1-6, characterized in that, include: Collect multi-physics field operating parameters of the pumping station to obtain multi-dimensional characteristic operating conditions of the source side; Based on the multi-dimensional characteristic working conditions of the source side, the digital twin model is dynamically mapped to obtain a digital twin mirror body to perform multi-field simulation pre-play and generate a pre-play instruction set; The damping characteristics of the actuator and the external load are matched to the pre-trained instruction set to generate the excitation drive current timing curve. The door closing action is executed based on the excitation drive current timing curve, and the deviation between the working door falling trajectory and the pre-rehearsed instruction set is fed back and corrected in real time; and the digital twin mirror is synchronously interacted with to generate a door closing motion state data stream. Based on the data stream of the closed door motion state, the remaining travel and instantaneous momentum of the working door from the bottom sill are identified, and end-level energy dissipation optimization and zero-impact self-locking are performed to output the closed door performance evaluation file.

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