Intelligent control method and system for hydraulic engineering gate

Abstract

The invention discloses an intelligent control method and system for a water conservancy project gate, and relates to the technical field of water conservancy project automation control, and the method comprises the steps: collecting original signals of water surface height and water flow velocity, capturing an environmental noise quantum frequency spectrum, carrying out the time alignment with the original signals, and generating a signal group; constructing a quantum correlation mapping function according to the signal group, stripping noise through quantum inverse convolution operation, and generating a pure signal group; based on the pure signal group, inversely solving a theoretical gate opening degree through an improved flow equation, and generating a target gate opening degree in combination with an inertia compensation coefficient; according to the target gate opening degree and the current position of the gate, the amount needing to be adjusted is calculated, the magnetic field intensity and the magnetic field direction vector are determined through a non-linear field intensity mapping algorithm, and three-dimensional magnetic field control parameters are generated. The control stability and the adjustment efficiency under the extreme hydrological condition are improved, meanwhile, the mechanical loss and the maintenance cost are reduced, and intelligent upgrading of water conservancy project gate control is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automatic control of water conservancy projects, and in particular to an intelligent control method and system for a water conservancy project gate. BACKGROUND

[0002] Water conservancy gate control technology, as a key link in water resource management, has gradually evolved from traditional mechanical control to intelligent integrated systems. Current mainstream solutions combine multi-source sensor networks (such as ultrasonic water level meters and electromagnetic flowmeters) with adaptive control algorithms (such as model predictive control and reinforcement learning) to achieve dynamic decision-making of gate opening. In terms of noise processing, time-frequency analysis methods such as wavelet packet decomposition and empirical mode decomposition are applied to environmental noise suppression, while fluid dynamics inversion models (including improved Saint-Venant equation solutions) improve the theoretical accuracy of flow calculation.

[0003] The limitations of existing technologies mainly focus on two interrelated dimensions: first, quantum noise decoupling failure in complex fluid environments. Traditional signal processing methods cannot decouple the characteristic superposition of water flow turbulence, mechanical vibration, and electromagnetic interference in the quantum frequency domain, resulting in systematic distortion of the original signal in both time and frequency dimensions, which further deviates the inverse solution of the flow equation from the true fluid motion state, especially in extreme weather conditions; second, nonlinear response lag of the driving system. The inherent inertia delay of the mechanical transmission chain and the non-accuracy problem of the impeller angle acceleration control in the magnetic field drive superimpose each other, causing high-frequency oscillation phenomena in the gate regulation process, which not only increases mechanical loss, but also more likely leads to instability of key operating states in high-frequency regulation scenarios. SUMMARY

[0004] In view of the above existing problems, the present application is proposed.

[0005] Therefore, the present application provides an intelligent control method for a water conservancy gate to solve the problems of quantum noise coupling distortion in complex fluid environments and nonlinear response lag of the magnetic field driving system.

[0006] To solve the above technical problems, the present application provides the following technical solutions: In a first aspect, the present application provides an intelligent control method for a water conservancy gate, which comprises: collecting original signals of water surface height and water flow velocity, capturing quantum spectrum of environmental noise, time-aligning the original signals and the quantum spectrum to generate a signal group; constructing a quantum correlation mapping function based on the signal group, stripping noise through quantum inverse convolution operation to generate a pure signal group; generating a target gate opening degree based on the pure signal group, through an improved gate opening degree reverse solution theory of flow equation, and in combination with an inertia compensation coefficient; calculating an adjustment amount to be adjusted according to the target gate opening degree and a current position of the gate, and determining a magnetic field strength and a magnetic field direction vector by using a nonlinear field strength mapping algorithm to generate three-dimensional magnetic field control parameters; converting the three-dimensional magnetic field control parameters into three-phase driving currents, generating a tangential force of an impeller through a Lorentz force formula, and obtaining an angular acceleration of the impeller to control the gate movement.

[0007] As a preferred scheme of the intelligent control method for the water conservancy gate, the collecting of the original signals of the water surface height and the water flow velocity, the capturing of the quantum spectrum of the environmental noise, the time-aligning of the original signals and the quantum spectrum to generate the signal group specifically comprises the following steps: collecting original signals of water surface height and water flow velocity; capturing noise frequencies and amplitudes in the environmental noise, and combining to generate a quantum spectrum of the environmental noise in time sequence; time-aligning the original signals of the water surface height, the original signals of the water flow velocity and the quantum spectrum of the environmental noise to generate the signal group.

[0008] As a preferred scheme of the intelligent control method for the water conservancy gate, the constructing of the quantum correlation mapping function based on the signal group, and the stripping of the noise through the quantum inverse convolution operation to generate the pure signal group specifically comprises the following steps: generating the quantum correlation mapping function through integral operation based on the noise frequencies and amplitudes in the quantum spectrum of the environmental noise; performing fast Fourier transform on the quantum correlation mapping function to generate a frequency domain representation; performing quantum inverse convolution operation on the original signals of the water surface height and the water flow velocity according to the frequency domain representation, and combining to generate the pure signal group.

[0009] As a preferred scheme of the intelligent control method for the water conservancy gate, the generating of the target gate opening degree based on the pure signal group, through the improved gate opening degree reverse solution theory of flow equation specifically comprises the following steps: calculating an instantaneous flow rate through the improved flow equation based on the pure signal group; solving the gate opening degree according to a preset flow rate and the instantaneous flow rate.

[0010] As a preferred scheme of the intelligent control method of the water conservancy gate, the inertial compensation coefficient is combined to generate the target gate opening, and the specific steps are as follows, The inertial compensation coefficient is calculated through the water flow velocity original signal in the pure signal group. The product of the theoretical gate opening and the inertial compensation coefficient is taken as the target gate opening.

[0011] As a preferred scheme of the intelligent control method of the water conservancy gate, the inertial compensation coefficient is combined to generate the target gate opening, and the specific steps are as follows, The target gate opening is combined with the current gate position to obtain the adjustment amount. Based on the adjustment amount, the magnetic field strength is calculated by using the nonlinear field strength mapping algorithm, and the magnetic field direction vector is determined by the sign of the adjustment amount. The magnetic field strength and the magnetic field direction vector are combined into three-dimensional magnetic field control parameters.

[0012] As a preferred scheme of the intelligent control method of the water conservancy gate, the inertial compensation coefficient is combined to generate the target gate opening, and the specific steps are as follows, The three-dimensional magnetic field control parameters are Park-Clark inverse transformed to generate three-phase drive currents. The tangential force acting on the impeller is obtained by the Lorentz force formula according to the three-phase drive current to generate the impeller tangential force. The impeller angular acceleration is obtained according to the impeller tangential force, and the gate movement is controlled by the impeller angular acceleration.

[0013] In a second aspect, the present application provides an intelligent control system for a water conservancy gate, comprising: a signal acquisition module, configured to acquire original signals of water surface height and water flow velocity, and capture environmental noise quantum spectrum, time-align the original signals with the environmental noise quantum spectrum, and generate a signal group; a signal purification module, configured to construct a quantum correlation mapping function according to the signal group, strip noise through quantum inverse convolution operation, and generate a pure signal group; an inverse opening degree module, configured to inversely solve gate opening degree based on the pure signal group through an improved flow equation inverse solution theory, and generate target gate opening degree in combination with an inertia compensation coefficient; a magnetic field control module, configured to calculate an adjustment amount according to the target gate opening degree and a current position of the gate, determine magnetic field strength and a magnetic field direction vector by using a nonlinear field strength mapping algorithm, and generate three-dimensional magnetic field control parameters; and a gate control module, configured to convert the three-dimensional magnetic field control parameters into three-phase driving current, generate impeller tangential force through a Lorentz force formula, and obtain impeller angular acceleration to control gate movement.

[0014] In a third aspect, the present application provides a computer device, comprising a memory and a processor, and the memory stores a computer program, wherein: the computer program is executed by the processor to implement any step of the intelligent control method for a water conservancy gate according to the first aspect of the present application.

[0015] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program, wherein: the computer program is executed by a processor to implement any step of the intelligent control method for a water conservancy gate according to the first aspect of the present application.

[0016] The present application has the following beneficial effects: the quantum inverse convolution noise stripping technology is used to accurately separate fluid signals from environmental noise, solving the signal distortion problem of traditional methods under complex working conditions; and the nonlinear field strength mapping algorithm is used to realize accurate control of magnetic field driving, overcoming the inertia hysteresis defects of mechanical transmission systems. The two core technologies work together to make the gate control system have high-precision signal processing capability and millisecond-level dynamic response characteristics, improve the control stability and regulation efficiency under extreme hydrological conditions, reduce mechanical wear and maintenance costs, and realize intelligent upgrading of water conservancy gate control. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0018] Fig. 1 The flowchart of the intelligent control method for a water conservancy gate.

[0019] Fig. 2 Fig. 1 is a schematic diagram of an intelligent control system for a water conservancy gate.

[0020] Fig. 3 Fig. 4 is a flowchart for gate opening calculation.

[0021] Fig. 4 Fig. 5 is a flowchart for gate drive control. DETAILED DESCRIPTION

[0022] In order to make the above objectives, characteristics and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0023] In the following description, a large number of specific details are set forth in order to facilitate a thorough understanding of the present application, but the present application can also be implemented in other ways different from those described herein, and those skilled in the art can make similar generalizations without departing from the connotation of the present application, therefore the present application is not limited to the specific embodiments disclosed below.

[0024] Secondly, "one embodiment" or "embodiment" referred to herein means that a specific feature, structure or characteristic can be included in at least one implementation of the present application. "In one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an embodiment that is independent of or mutually exclusive of other embodiments.

[0025] Reference Signs List Figs. 1-4 For one embodiment of the present application, the embodiment provides an intelligent control method for a water conservancy gate, comprising the following steps: S1: collecting original signals of water surface height and water flow velocity, capturing quantum spectrum of environmental noise, time aligning with the original signals, and generating a signal group; S1.1: collecting original signals of water surface height and water flow velocity; Further, the original signal of water surface height is obtained by a piezoresistive water level sensor or an ultrasonic water level meter, an analog voltage signal output by the sensor is converted into a digital signal by a 24-bit ADC, and the sampling frequency is not less than 100Hz. The original signal of water flow velocity is measured by an acoustic Doppler flowmeter, a flowmeter probe is installed on an underwater fixed support, a pulse wave with a frequency of 1MHz is transmitted and a reflected signal is received, the original signal of water flow velocity is calculated by Doppler shift, and the sampling interval is synchronized with the original signal of water surface height.

[0026] S1.2: capturing noise frequency and amplitude in environmental noise, and combining to generate quantum spectrum of environmental noise in time sequence; Furthermore, environmental noise is collected using a quantum noise sensor based on the sensitivity of diamond NV color center spin states to environmental vibrations. The noise frequency and amplitude are analyzed using the Rabi oscillation spectrum, with the sampling time window strictly aligned with the original water surface height and water flow velocity signals. The noise frequency and amplitude are arranged with millisecond-level timestamps, and a discrete Fourier transform is used to generate the environmental noise quantum spectrum. The spectral resolution is determined by the sampling time and the Hanning window function.

[0027] S1.3: Time-align the original water surface height signal, the original water flow velocity signal, and the ambient noise quantum spectrum to generate a signal group; Furthermore, the raw water surface height and flow velocity signals, as well as the ambient noise quantum spectrum, are time-stamped using a GPS-synchronized clock, with time alignment accuracy controlled to the microsecond level. The signal groups are stored as structured arrays, with each row containing the raw water surface height, flow velocity, and ambient noise quantum spectrum at the same moment. Missing data is supplemented through linear interpolation.

[0028] S2: Construct a quantum correlation mapping function based on the signal group, remove the noise through quantum inverse convolution operation, and generate a pure signal group; S2.1: Based on the noise frequency and amplitude in the quantum spectrum of the ambient noise, the quantum correlation mapping function is generated by integration operation, and the expression is: ; in, is the quantum correlation mapping function, is the noise frequency, is the amplitude, represents the integration time step, is the quantum Fourier transform; Furthermore, the quantum correlation mapping function The noise frequency in the quantum spectrum of the environmental noise is measured by numerical integrator and amplitude Perform time domain integration operation, integration time step The integration result is input into the quantum Fourier transform. deal with, A fast algorithm based on quantum phase estimation is used to convert the time domain integration result into a frequency domain quantum state representation, generating a complex matrix form of the quantum correlation mapping function N, whose matrix dimension matches the noise spectrum resolution.

[0029] S2.2: Perform a fast Fourier transform on the quantum correlation mapping function to generate a frequency domain representation; Further, the quantum correlation mapping function N performs fast Fourier transform through Cooley-Tukey algorithm, and the transform point number is set as the frequency domain sampling point number of the quantum spectrum of the ambient noise. The frequency domain representation is stored in the form of a complex array, and the array elements include amplitude and phase components, the amplitude component corresponds to the noise energy distribution, and the phase component is used for phase correction in subsequent inverse convolution operation.

[0030] It should be noted that in the signal processing procedure, the generation and subsequent processing of the quantum correlation mapping function N need to perform different nature transformations in the time domain and the frequency domain, respectively. The quantum Fourier transform Φ acts on the time domain integral result, and the purpose is to convert the time domain noise characteristics into the quantum state frequency domain representation, which is a key step for constructing the quantum correlation mapping function N. The fast Fourier transform performed on the quantum correlation mapping function N subsequently is to convert the generated quantum correlation mapping function N from the time domain representation to the frequency domain representation, providing the basis for the frequency domain operation in the subsequent quantum inverse convolution operation. The two transformations have essential differences: the quantum Fourier transform Φ realizes the conversion from the classical signal to the quantum state, while the fast Fourier transform is to convert the time domain form of the quantum correlation mapping function N into the frequency domain form, and the two serve the needs of different processing stages. The output of the quantum Fourier transform Φ is the quantum state representation of the quantum correlation mapping function N, and the output of the fast Fourier transform is the classical frequency domain representation of the quantum correlation mapping function N. This step-by-step processing ensures the complete characterization of the noise characteristics in the quantum computing framework and the feasibility of the subsequent frequency domain operation.

[0031] S2.3: Perform quantum inverse convolution operation on the water surface height original signal and the water flow velocity original signal according to the frequency domain representation, and combine to form a pure signal group; The expression for performing the quantum inverse convolution operation is: ; ; wherein, is the purified water surface height original signal, is the purified water flow velocity original signal, is the water surface height original signal, is the water flow velocity original signal, is the quantum convolution operation symbol, is the frequency domain inverse kernel function; Further, the frequency domain inverse kernel function is generated by taking the inverse of the frequency domain representation of the quantum correlation mapping function , retaining the amplitude inverse and reversing the phase. The water surface height original signal and the water flow velocity original signal perform quantum convolution operation with The operation is realized by point-by-point multiplication in the frequency domain, and the result is restored to the time domain signal through inverse Fourier transform. and the original signal of water flow velocity Aligned by timestamp, the signals are combined into a clean signal group in the form of a two-dimensional array.

[0032] S3: Based on the pure signal group, the theoretical gate opening is inversely solved by the improved flow equation, and the target gate opening is generated by combining the inertia compensation coefficient; S3.1: Based on the pure signal group, the instantaneous flow rate is calculated by the improved flow equation, which is expressed as: ; in, is the instantaneous flow rate, is the dynamic flow coefficient, is the acceleration due to gravity, is the water flow inclination compensation item; ; in, is the dynamic flow coefficient, is the base flow coefficient, is the Reynolds number; It should be noted that the improved flow equation was derived by making three key improvements to the traditional flow calculation formula. The first step, based on the standard flow equation, introduced the Reynolds number theory and the energy loss formula for inclined flow channels from fluid mechanics to establish a dynamic flow coefficient that incorporates the flow inclination angle and Reynolds number. The second step was to re-derive the kinetic energy theorem and explicitly include the velocity kinetic energy term in the equation to more completely describe the fluid energy conversion process. The third step was to derive the baseline flow coefficient through statistical analysis of historical laminar flow condition data.

[0033] It should also be noted that the dynamic flow coefficient is a correction model established through fluid mechanics experiments and engineering experience. Its core consists of three components: a reference flow coefficient, a flow angle compensation term, and a Reynolds number compensation term. The reference flow coefficient is derived from the classical theoretical value of thin-wall weir flow and calibrated through extensive laminar flow experiments. The flow angle compensation term is derived based on the energy loss theory of oblique flow. The Reynolds number compensation term is based on turbulent boundary layer theory and is determined by fitting flume test data at different Reynolds numbers.

[0034] S3.2: Inversely solve the theoretical gate opening based on the preset flow rate and instantaneous flow rate. The expression is: ; in, is the theoretical gate opening, The preset flow rate; Furthermore, the theoretical gate opening Through the preset flow and the relationship inverse solution obtained, the calculation process contains kinetic energy correction term to consider the impact of flow rate.

[0035] It should be noted that the determination method of the preset flow is derived from the actual operation demand of the water conservancy dispatching system. First, the dispatching instruction value comes from the real-time database of the basin water resources management system, which is collected by the flow monitoring equipment of the upstream and downstream hydrological stations and transmitted to the control center through the SCADA system.

[0036] S3.3: Through the water flow velocity original signal in the pure signal group, the inertia compensation coefficient is calculated, and the expression is: ; wherein, is the inertia compensation coefficient, is the flow rate change sensitivity (value range: 0.03-0.05), is the water level change sensitivity (value range: 0.01-0.02), is the inclination change sensitivity (value range: 0.005-0.01); Further, the inertia compensation coefficient is calculated by the flow rate change rate , the water level change rate and the inclination change rate , wherein the change rate is calculated by the five-point central difference method. The coefficients , , respectively take the middle value 0.04, 0.015 and 0.0075 in the range, to ensure the stability of the compensation effect. The calculation result is used to correct the dynamic response lag of the theoretical gate opening.

[0037] S3.4: The product of the theoretical gate opening and the inertia compensation coefficient is taken as the target gate opening; Further, the target gate opening is obtained by the product of the theoretical gate opening and the inertia compensation coefficient , and the target gate opening is limited within the mechanical stroke range of the gate. When the value exceeds the safety threshold, the protection mechanism is triggered to suspend the opening adjustment, and after the compensation coefficient returns to normal, the execution is continued. The final output is the target gate opening instruction value.

[0038] ​It should be noted that the determination process of the safety threshold comprises three rigorous steps. The theoretical calculation stage is based on the fatigue strength theory in material mechanics, and the maximum allowable impact force is calculated through the stress-strain curve of the gate transmission shaft, and then the safety threshold is derived according to Newton's second law and the rotational inertia formula.

[0039] S4: According to the target gate opening and the current position of the gate, the adjustment amount is calculated, and the magnetic field strength and the magnetic field direction vector are determined by using the nonlinear field strength mapping algorithm to generate the three-dimensional magnetic field control parameters; S4.1: Collect the current position of the gate, and combine the target gate opening to obtain the adjustment amount; Further, the current position of the gate is collected in real time by a high-precision magnetic grid encoder, and the measurement resolution reaches 0.1 millimeter level, and the sampling frequency is not less than 100 Hz. The adjustment amount is obtained by subtracting the target gate opening from the current position of the gate, and the difference is processed by low-pass filtering to eliminate high-frequency jitter. When the adjustment amount exceeds the mechanical stroke limit value, the overload protection is triggered immediately and the adjustment command is locked. The adjustment amount represents the precise displacement amount that the gate needs to move, and the displacement direction is determined by the sign of the adjustment amount. A positive value indicates that the opening needs to be increased, and a negative value indicates that the opening needs to be decreased.

[0040] It should be noted that the mechanical stroke limit value refers to the maximum safe displacement range allowed by the mechanical structure of the gate, which is determined by the length of the gate guide rail, the stroke of the driving mechanism and the structural strength. In the intelligent control method of the water conservancy gate, the mechanical stroke limit value is used as a preset protection parameter, and when the adjustment amount exceeds the mechanical stroke limit value, the overload protection mechanism is triggered to pause the opening adjustment. The specific value of the mechanical stroke limit value is determined by finite element analysis and fatigue test of the gate steel structure, to ensure that the gate transmission mechanism does not deform plastically or fail under extreme working conditions. The setting of the mechanical stroke limit value needs to consider the physical constraints between the fully open position and the fully closed position of the gate, as well as the mechanical wear allowance caused by long-term operation.

[0041] S4.2: Based on the adjustment amount, the magnetic field strength is calculated by using the nonlinear field strength mapping algorithm, and the magnetic field direction vector is determined by the sign of the adjustment amount; The expression for calculating the magnetic field strength is: ; Wherein, is the magnetic field strength, is the rated maximum field strength, is the nonlinear index, is the adjustment amount; Further, the magnetic field strength is calculated by the nonlinear field strength mapping algorithm, takes the rated maximum field strength value of the electromagnetic coil, is the mechanical stroke of the fully open gate. The nonlinear index According to the impeller torque characteristic curve selection, it is ensured that the field strength changes gently when the small opening is adjusted, and the response is rapid when the large opening is adjusted. The absolute value of the required adjustment quantity and The ratio determines the field strength amplitude, and the sign function determines the magnetic field direction vector, and the positive direction corresponds to the axis direction, and the negative direction corresponds to the axis direction. The calculation result is a signed scalar field strength value, and the direction information is stored separately as a three-dimensional unit vector.

[0042] S4.3: Combine the magnetic field strength and the magnetic field direction vector into a three-dimensional magnetic field control parameter; Further, the three-dimensional magnetic field control parameter is synthesized by the amplitude of the magnetic field strength and the magnetic field direction vector, and the direction vector is selected according to the positive and negative directions of the reference coordinate system . The control parameter is stored in the form of 4-quaternion, and the first three components represent , , the axis field strength components, and the fourth component is reserved for the check bit. The field strength components are calculated by orthogonal decomposition to ensure that the length of the synthesized vector is strictly equal to . The final output three-dimensional magnetic field control parameter is directly used for driving the control unit, and the parameter update rate is synchronized with the gate position sampling rate.

[0043] S5: Convert the three-dimensional magnetic field control parameter into three-phase driving current, generate the impeller tangential force through the Lorentz force formula, and obtain the impeller angular acceleration, and control the gate movement through the impeller angular acceleration; S5.1: Perform Park-Clark inverse transformation on the three-dimensional magnetic field control parameter to generate three-phase driving current; Further, the three-dimensional magnetic field control parameter is first decomposed into direct-axis component and quadrature-axis component, and the magnetic field control parameter in the stationary coordinate system is converted into the current instruction in the rotating coordinate system through the Park-Clark inverse transformation matrix. The rotation angle of the transformation matrix is determined by the rotor electrical angle feedback by the impeller position sensor in real time. The generated current instruction is processed through the space vector pulse width modulation algorithm, and the output is three-phase driving current. The amplitude and phase of the three-phase driving current strictly match the spatial distribution required by the magnetic field control parameter, and the current waveform distortion rate is controlled within a certain range (for example, five percent) to ensure the accuracy of the magnetic field orientation. The three-phase driving current is amplified by the insulated gate bipolar transistor power module and then output to the motor winding.

[0044] S5.2: According to the three-phase driving current, the tangential force acting on the impeller is obtained through the Lorentz force formula to generate the impeller tangential force; ​Further, the three-phase drive current generates a rotating magnetic field in the stator winding of the permanent magnet synchronous motor, and the rotor permanent magnet is subjected to the Lorentz force under the action of the magnetic field. The tangential force calculation considers the spatial angle between the current vector and the permanent magnet magnetic field, and is accurately solved by the vector cross product formula. The amplitude of the impeller tangential force is proportional to the axial component of the three-phase drive current, and the direction is determined by the spatial relationship between the current vector and the permanent magnet pole axis. The calculation result is verified by the finite element analysis software, and the error of the force value is ensured to be within 3%. The generated impeller tangential force is transmitted to the transmission mechanism as a torque input.

[0045] S5.3: Obtain the impeller angular acceleration according to the impeller tangential force, and control the gate movement through the impeller angular acceleration; Further, the impeller angular acceleration is obtained by calculating the impeller tangential force and the impeller moment of inertia, and the moment of inertia includes the equivalent inertia of the impeller body and the transmission component. The angular acceleration value is used as a control command after being filtered by a second-order low-pass filter to eliminate high-frequency vibration components. The gate displacement rate and the impeller angular acceleration establish a linear relationship through the gear transmission ratio, and the control algorithm dynamically adjusts the angular acceleration command according to the deviation between the target displacement and the actual displacement. The displacement and speed feedback are monitored in real time during the gate movement process, and the proportional-integral-derivative adjustment is used to ensure that the motion trajectory is smooth and without overshoot. Finally, the precise closed-loop control of the gate opening is realized.

[0046] The embodiment also provides an intelligent control system for a water conservancy gate, which comprises: a signal acquisition module, configured to acquire original signals of water surface height and water flow velocity, capture environmental noise quantum spectrum, time-align with the original signals, and generate a signal group; a signal purification module, configured to construct a quantum correlation mapping function according to the signal group, strip noise through quantum inverse convolution operation, and generate a pure signal group; an inverse opening degree module, configured to inversely solve the gate opening degree based on the pure signal group through an improved flow equation inverse solution theory, and generate a target gate opening degree in combination with an inertia compensation coefficient; a magnetic field control module, configured to calculate an adjustment amount according to the target gate opening degree and a current position of the gate, determine a magnetic field strength and a magnetic field direction vector by using a nonlinear field strength mapping algorithm, and generate three-dimensional magnetic field control parameters; and a gate control module, configured to convert the three-dimensional magnetic field control parameters into three-phase drive current, generate an impeller tangential force through a Lorentz force formula, and obtain an impeller angular acceleration, so as to control the gate movement through the impeller angular acceleration.

[0047] The embodiment also provides a computer device suitable for the case of the intelligent control method for a water conservancy gate, which comprises: a memory and a processor; the memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions to realize the intelligent control method for a water conservancy gate proposed in the above embodiment.

[0048] ​The computer device can be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected by a system bus. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for running the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is configured to perform wired or wireless communication with an external terminal. The wireless communication can be achieved by WIFI, an operator network, NFC (Near Field Communication) or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball or touchpad arranged on the shell of the computer device, or an external keyboard, touchpad or mouse, etc.

[0049] The embodiment also provides a storage medium having a computer program stored thereon, the program being executed by a processor to implement the intelligent control method for a water conservancy project gate as described above. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic storage, a flash memory, a magnetic disk or an optical disk.

[0050] To sum up, the application achieves the following effects: the quantum deconvolution noise stripping technology is used to accurately separate the fluid signal and the environmental noise, and the signal distortion problem of the traditional method under complex working conditions is solved; the nonlinear field strength mapping algorithm is used to achieve accurate control of the magnetic field driving, and the inertia and hysteresis defects of the mechanical transmission system are overcome. The two core technologies work together to make the gate control system have high-precision signal processing capability and millisecond-level dynamic response characteristics, improve the control stability and regulation efficiency under extreme hydrological conditions, reduce the mechanical loss and maintenance cost, and realize the intelligent upgrading of the water conservancy project gate control.

[0051] It should be noted that the above examples are only used to illustrate the technical solutions of the present application but not limit the present application. Although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced, without departing from the spirit and scope of the technical solutions of the present application, which should be covered in the scope of the claims of the present application.

Claims

1. An intelligent control method for a water conservancy project gate, characterized by: include, Collect the original signals of water surface height and water flow velocity, capture the quantum spectrum of environmental noise, align them with the original signals in time, and generate a signal group; Construct a quantum correlation mapping function based on the signal group, remove the noise through quantum inverse convolution operation, and generate a pure signal group; Based on the pure signal group, the theoretical gate opening is inversely solved by the improved flow equation, and the target gate opening is generated by combining the inertia compensation coefficient; According to the target gate opening and the current gate position, the required adjustment amount is calculated, and the nonlinear field intensity mapping algorithm is used to determine the magnetic field strength and magnetic field direction vector to generate the three-dimensional magnetic field control parameters; The three-dimensional magnetic field control parameters are converted into three-phase drive current, the impeller tangential force is generated by the Lorentz force formula, and the impeller angular acceleration is obtained, and the gate movement is controlled by the impeller angular acceleration.

2. The intelligent control method for a water conservancy project gate according to claim 1, characterized in that: The original signals of water surface height and water flow velocity are collected, and the quantum spectrum of environmental noise is captured, and time-aligned with the original signals to generate a signal group. The specific steps are as follows: Collect original signals of water surface height and water flow velocity; Capture the noise frequency and amplitude in the environmental noise and generate the environmental noise quantum spectrum by combining them in time series; The original signal of water surface height, the original signal of water flow velocity and the quantum spectrum of environmental noise are time-aligned to generate a signal group.

3. The intelligent control method for a water conservancy project gate according to claim 2, characterized in that: The quantum correlation mapping function is constructed based on the signal group, and the noise is removed by quantum inverse convolution operation to generate a pure signal group. The specific steps are as follows: Based on the noise frequency and amplitude in the quantum spectrum of the environmental noise, a quantum correlation mapping function is generated through integration operation; Performing a fast Fourier transform on the quantum correlation mapping function to generate a frequency domain representation; The quantum deconvolution operation is performed on the original signal of water surface height and the original signal of water flow velocity according to the frequency domain representation, and the signals are combined into a pure signal group.

4. The intelligent control method for a water conservancy project gate according to claim 3, characterized in that: Based on the pure signal group, the theoretical gate opening is inversely solved by the improved flow equation. The specific steps are as follows: Based on the pure signal group, the instantaneous flow rate is calculated by the improved flow equation; The theoretical gate opening is inversely solved based on the preset flow and instantaneous flow.

5. The intelligent control method for a water conservancy project gate according to claim 4, characterized in that: The target gate opening is generated by combining the inertia compensation coefficient. The specific steps are as follows: Calculate the inertia compensation coefficient through the original water flow velocity signal in the pure signal group; The product of the theoretical gate opening and the inertia compensation coefficient is taken as the target gate opening.

6. The intelligent control method for a water conservancy project gate according to claim 5, characterized in that: The required adjustment amount is calculated based on the target gate opening and the current gate position, and the nonlinear field intensity mapping algorithm is used to determine the magnetic field intensity and magnetic field direction vector to generate the three-dimensional magnetic field control parameters. The specific steps are as follows: Collect the current gate position and combine it with the target gate opening to obtain the required adjustment amount; Based on the quantity to be adjusted, the nonlinear field intensity mapping algorithm is used to calculate the magnetic field strength, and the magnetic field direction vector is determined by the sign of the quantity to be adjusted; The magnetic field intensity and magnetic field direction vector are combined into a three-dimensional magnetic field control parameter.

7. The intelligent control method for a water conservancy project gate according to claim 6, characterized in that: The three-dimensional magnetic field control parameters are converted into three-phase drive current, the impeller tangential force is generated by the Lorentz force formula, and the impeller angular acceleration is obtained. The gate movement is controlled by the impeller angular acceleration. The specific steps are as follows: Perform Park-Clark inverse transformation on the three-dimensional magnetic field control parameters to generate three-phase drive current; The tangential force acting on the impeller is obtained by using the Lorentz force formula according to the three-phase driving current to generate the impeller tangential force; The impeller angular acceleration is obtained according to the impeller tangential force, and the gate movement is controlled by the impeller angular acceleration.

8. An intelligent control system for a water conservancy project gate, based on the intelligent control method for a water conservancy project gate according to any one of claims 1 to 7, characterized in that: include, The signal acquisition module is used to collect the original signals of water surface height and water flow velocity, and capture the quantum spectrum of environmental noise, align it with the original signal in time, and generate a signal group; The signal purification module is used to construct a quantum correlation mapping function based on the signal group, remove noise through quantum deconvolution operation, and generate a pure signal group; The inverse gate opening module is used to inversely solve the theoretical gate opening through the improved flow equation based on the pure signal group, and generate the target gate opening by combining the inertia compensation coefficient; The magnetic field control module is used to calculate the required adjustment amount based on the target gate opening and the current gate position, and use a nonlinear field strength mapping algorithm to determine the magnetic field strength and magnetic field direction vector to generate three-dimensional magnetic field control parameters; The gate control module is used to convert the three-dimensional magnetic field control parameters into three-phase drive current, generate the impeller tangential force through the Lorentz force formula, and obtain the impeller angular acceleration, and control the gate movement through the impeller angular acceleration.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the intelligent control method for the water conservancy project gate according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the intelligent control method for a water conservancy project gate according to any one of claims 1 to 7 are implemented.

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