Method for optimizing the operating power consumption of an ultrasonic cleaning apparatus

Abstract

The application relates to the technical field of ultrasonic cleaning machines, and discloses an ultrasonic cleaning equipment operation power consumption optimization method, which comprises the following steps: collecting cavitation acoustic emission signals in an ultrasonic cleaning tank, and collecting real-time temperature and real-time turbidity of cleaning liquid in real time; performing frequency domain feature extraction on the cavitation acoustic emission signals; calculating a cavitation efficiency index E that represents the current cavitation effect intensity cav The ultrasonic cleaning equipment operation power consumption optimization method can dynamically select optimal control parameters that consider energy efficiency and cleaning effect by constructing a power consumption optimization model with the dual objectives of minimizing equipment operation power consumption and maximizing the cavitation efficiency index, and by using an improved multi-objective whale optimization algorithm to solve a Pareto optimal solution set in real time, so that energy redundancy caused by traditional constant power control is avoided, and cleaning quality is guaranteed or improved.

Description

Technical Field

[0001] This invention relates to the field of ultrasonic cleaning machine technology, specifically to a method for optimizing the power consumption of ultrasonic cleaning equipment. Background Technology

[0002] Ultrasonic cleaning technology utilizes the impact force, high temperature, and micro-jets generated by cavitation to efficiently remove contaminants from workpiece surfaces. It has been widely used in medical devices, precision machinery, electronic components, and optical components. Currently, the operation and control of ultrasonic cleaning equipment mainly faces the following technical challenges: Insufficient energy efficiency optimization and high power consumption: Existing ultrasonic cleaning equipment typically uses constant power or preset program control, with fixed output frequency and duty cycle of the ultrasonic generator, making dynamic adjustment impossible based on the actual state of the cleaning fluid and the intensity of cavitation effect. To ensure cleaning effectiveness, the equipment often operates in redundant power mode for extended periods, resulting in significant energy dissipation as heat and overall low system energy efficiency. For example, under light loads or with relatively clean cleaning fluids, excessive cavitation effect can lead to energy waste and cavitation damage to the workpiece surface. The cavitation effect lacks quantitative characterization, and the adjustment of control parameters is often arbitrary: the cavitation effect is the core driving force of ultrasonic cleaning, but the existing technology has relatively simple means of detecting cavitation intensity, relying mostly on empirical judgment or indirect power monitoring. Some methods assess cavitation intensity by collecting broadband energy of cavitation noise, but ignore the transient cavitation characteristics reflected by discrete line spectrum energy. This results in insufficient accuracy in identifying whether the cavitation effect is in a state of "under-cavitation", "optimal cavitation" or "over-cavitation". The lack of precise quantitative indicators of cavitation efficiency makes the adjustment of control parameters lack a scientific basis and makes it difficult to achieve a balance between low power consumption and high efficiency. Poor adaptability to dynamic environmental changes in the cleaning fluid: During the cleaning process, the temperature and turbidity of the cleaning fluid change in real time with the cleaning duration and the shedding of contaminants. Temperature changes significantly affect the viscosity characteristics and cavitation threshold of the liquid, while increased turbidity increases sound propagation attenuation and changes the distribution of cavitation nuclei. Traditional optimization algorithms or fixed parameter models are difficult to respond to such time-varying nonlinear disturbances in real time. When the state of the cleaning fluid deviates from the ideal operating conditions, the equipment cannot adaptively adjust and control, resulting in a decrease in cleaning effect or an abnormal increase in power consumption. Therefore, an optimization method for the operating power consumption of ultrasonic cleaning equipment is proposed to solve the above problems. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a method for optimizing the operating power consumption of ultrasonic cleaning equipment. This method significantly reduces the operating power consumption of the equipment, achieving energy-saving cleaning and solving the energy redundancy problem caused by traditional constant power control.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a method for optimizing the power consumption of an ultrasonic cleaning device, comprising the following steps: S1: Real-time acquisition of cavitation acoustic emission signals in the ultrasonic cleaning tank, as well as the real-time temperature and turbidity of the cleaning fluid; S2: Extract frequency domain features from the cavitation acoustic emission signal and calculate the cavitation effectiveness index E, which characterizes the intensity of the current cavitation effect. cav ; S3: Construct a system that minimizes device power consumption and maximizes the cavitation efficiency index E. cav A power consumption optimization model with two objectives; S4: An improved multi-objective whale optimization algorithm is used to solve the power consumption optimization model to obtain the Pareto optimal solution set under the current cleaning state, and this is combined with the real-time temperature T. real and real-time turbidity C real Select the optimal control parameter X from the Pareto optimal solution set. best (t); S5: Based on the optimal control parameter X best (t) The output frequency and drive duty cycle of the ultrasonic generator are dynamically adjusted to optimize power consumption.

[0005] The specific process for calculating the cavitation efficiency index in step S2 includes: The acquired cavitation acoustic emission signal is subjected to multi-layer wavelet packet decomposition to reconstruct the low-frequency and high-frequency signals; Calculate the energy of the low-frequency signal as the discrete line spectrum energy E. line Calculate the energy of the high-frequency signal as the broadband noise energy E. broad ; Based on the discrete line spectrum energy E line and broadband noise energy E broad Calculate the cavitation efficiency index E cav The expression is: ; Among them, E broad For broadband noise energy, E broadmax E is the preset broadband noise energy saturation threshold. line E represents the discrete line spectrum energy. lineopt The discrete line spectrum energy reference value is the value corresponding to the optimal transient cavitation, where α1 and α2 are both weighting coefficients.

[0006] Preferably, the fitness function F(X) of the power consumption optimization model in step S3 is expressed as: F(X)=[f1(X), f2(X)]; Where X is a control parameter vector, including output frequency and drive duty cycle; f1(X) is the objective function for device power consumption, f1(X) = U × I × cos(θ) × D, where U is the output voltage, θ is the phase difference between voltage and current, and D is the drive duty cycle; F2(X) is the objective function for cavitation effectiveness, f2(X) = -E cav (X), i.e., the cavitation efficiency index E cav Take negative values ​​to transform the problem into a minimization problem.

[0007] Preferably, a cavitation physical mapping mechanism is introduced in step S4, embedding the cavitation bubble collapse energy decay model into the helical position update of the algorithm. The specific process is as follows: In the shrinking encirclement and spiral update stages of the improved multi-objective whale optimization algorithm, a cavitation bubble collapse energy attenuation factor K is introduced. cav The spiral search radius is dynamically adjusted, and its position update formula is: ; Where X(t+1) is the updated position of the individual, X * (t) represents the current optimal individual position, D' represents the distance between the individual and the optimal individual, b is a constant defining the shape of the logarithmic spiral, and l is a random number between [-1, 1]. Calculate the energy decay factor K of cavitation bubble collapse. cav The expression is: ; Among them, E cav (t) represents the cavitation efficiency index corresponding to the current iteration number t, E cavmax T is the historical maximum cavitation efficiency index, λ is the attenuation regulation index, and T is the cavitation efficiency index. max This represents the maximum number of iterations.

[0008] Preferably, in step S4, a real-time temperature T is introduced. real and real-time turbidity C real Converted to fluid viscous drag coefficient μ res This allows for adaptive adjustment of the encirclement step size of the algorithm, specifically: Based on real-time temperature T real and real-time turbidity C real Calculate the fluid viscous drag coefficient μ res The expression is: ; Where μ0 is the basic viscosity coefficient, B is the temperature-sensitive constant, and K is the K value. c β is the turbidity influence coefficient, and β is the zero constant for prevention and removal; Based on the fluid viscous drag coefficient μ res The convergence factor 'a' of the algorithm is dynamically adjusted during the exploration and development phases, expressed as: ; Where η is the resistance mapping weight, μ min and μ max These are the preset minimum and maximum fluid viscous drag coefficients, respectively. When the fluid viscous drag coefficient μ res As the value increases, the convergence factor a decays faster.

[0009] Preferably, combined with real-time temperature T real and real-time turbidity C real The process of selecting the optimal control parameters from the Pareto optimal solution set includes: Calculate the curvature of each solution in the Pareto optimal solution set and identify the inflection point region of the Pareto front; Constructing the environment preference vector V env =[V1, V2], where V1 and V2 represent the preference weights for power consumption and performance, respectively; When the real-time temperature deviates from the optimal cavitation temperature range or the real-time turbidity C real When the threshold is exceeded, adjust the environment preference vector to point to the low-power region; otherwise, point it to the high-efficiency region. Calculate the angle between each solution within the inflection point region and the environmental preference vector, and select the solution with the smallest angle as the optimal control parameter X. best (t).

[0010] Preferably, the specific method for dynamically adjusting the output frequency and drive duty cycle of the ultrasonic generator in step S5 is as follows: Based on the output frequency in the optimal control parameters, phase tracking fine-tuning is performed at the resonant frequency point of the ultrasonic transducer to make the phase difference between the output voltage and the output current approach zero. Based on the drive duty cycle in the optimal control parameters, the continuous output mode of the ultrasonic generator is switched to the pulse output mode. By adjusting the duty cycle of the pulse width modulation signal, the cavitation shielding effect is eliminated.

[0011] Preferably, based on the fluid viscous drag coefficient μ res After dynamically adjusting the convergence factor 'a', when the algorithm is in the exploration phase, a viscous damping attenuation term is introduced to modify the individual's position update formula. The modified position update formula is as follows: ; Where X(t+1) is the updated position of the individual, X rand Let A be the position of an individual randomly selected in the current population, and let D be the coefficient vector calculated based on the convergence factor a. rand Let ξ be the distance vector of a random individual, and μ be the damping sensitivity coefficient. res μ is the fluid viscous drag coefficient. maxThe preset maximum fluid viscous drag coefficient is given by the fluid viscous drag coefficient μ. res As the step size increases, the global search step size decreases exponentially.

[0012] Preferably, in constructing the environment preference vector V env In the value [V1, V2], the specific dynamic calculation process of preference weights V1 and V2 is as follows: Calculate temperature deviation And the turbidity exceedance rate ΔC = max(0, C threshold ), where T real For real-time temperature, T opt For the optimal cavitation temperature, C threshold This is the turbidity safety threshold. The comprehensive environmental degradation index E is calculated based on the calculated temperature deviation ΔT and turbidity exceedance rate ΔC. env The expression is: ; Among them, △T max For the maximum allowable temperature deviation, C max ω1 and ω2 are the normalized weighting coefficients, representing the maximum turbidity range. Based on the comprehensive environmental degradation index E env The preference weights V1 and V2 are calculated using the following expression: ; Where κ is the environmental sensitivity adjustment coefficient, tanh is the hyperbolic tangent function, and when the comprehensive environmental degradation index E env When V1 increases and V2 decreases, the environment preference vector V... env It non-linearly points to the low-power region.

[0013] Preferably, the solution with the smallest included angle is selected as the optimal control parameter X. best After (t), to avoid frequent jumps in the control parameters of the ultrasonic generator, a hysteresis smoothing mechanism for the control parameters is introduced. The specific process includes: Calculate the optimal control parameter X selected at the current time. best (t) The number of control parameters actually executed at the previous time step X exec The parameter variation distance ΔX between (t-1) is where the control parameter X includes the output frequency and the drive duty cycle; Determine whether the parameter change distance ΔX is less than the preset dead zone threshold δ. If yes, the actual control parameter executed at the current moment remains unchanged. If no, that is, the parameter change distance ΔX is greater than or equal to the dead zone threshold δ, then the optimal control parameter X selected at the current moment is... best (t) Perform first-order inertial filtering to obtain the actual control parameters X executed at the current time.exec (t), the expression is: ; Where γ is the filtering smoothing coefficient, and 0 < γ ≤ 1.

[0014] Preferably, the dead zone threshold δ is based on the real-time turbidity C of the cleaning fluid. real The rate of change is adaptively adjusted, and the expression is: ; Where δ(t) is the dead zone threshold at the current time, δ base The basic dead zone threshold is given, ρ is the rate of change sensitivity coefficient, and C is the base dead zone threshold. real (t) represents the real-time turbidity at the current moment, C real (t-△t) represents the real-time turbidity of the previous sampling period, where △t is the sampling period. When the real-time turbidity C real When the rate of change increases, the dead zone threshold δ(t) decreases to improve the control parameter X. exec (t) Sensitivity to the process of dirt removal by the cleaning solution.

[0015] Compared with the prior art, the present invention provides a method for optimizing the power consumption of ultrasonic cleaning equipment, which has the following beneficial effects: 1. The method for optimizing the operating power consumption of ultrasonic cleaning equipment is to construct a power consumption optimization model with the dual objectives of minimizing equipment operating power consumption and maximizing cavitation efficiency index, and to use an improved multi-objective whale optimization algorithm to solve the Pareto optimal solution set in real time. This method can dynamically select the best control parameters that take into account both energy efficiency and cleaning effect, avoid the energy redundancy caused by traditional constant power control, and at the same time ensure or improve the cleaning quality.

[0016] 2. The method for optimizing the power consumption of the ultrasonic cleaning equipment converts real-time temperature and turbidity into fluid viscous resistance coefficients. Based on the dynamic adjustment of the convergence factor and global search step size in the optimization algorithm, when the viscous resistance of the cleaning fluid increases due to a decrease in temperature or an increase in turbidity, the algorithm automatically accelerates the decay rate of the convergence factor, prompting the search strategy to quickly shift from global exploration to local development, thereby improving the optimization efficiency under harsh working conditions. At the same time, the cavitation bubble collapse energy decay factor is embedded in the physical decay model, correcting the spiral update path of the whale algorithm, making the algorithm's response to changes in control parameters due to cavitation effects more consistent with actual physical laws, and improving the engineering credibility of the optimization results. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the structure of an ultrasonic cleaning equipment power consumption optimization method proposed in this invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Please see Figure 1 A method for optimizing the operating power consumption of an ultrasonic cleaning device includes the following steps: S1: Real-time acquisition of cavitation acoustic emission signals in the ultrasonic cleaning tank, as well as the real-time temperature and turbidity of the cleaning fluid; The cavitation acoustic emission signal is collected by a broadband piezoelectric ceramic hydrophone installed at the bottom of the cleaning tank; Temperature and turbidity sensors (based on infrared scattering or transmission principles) are used to measure the temperature and turbidity of the cleaning fluid in real time. Temperature affects liquid viscosity, vapor pressure, and cavitation threshold; turbidity reflects the concentration of suspended contaminants or emulsified oil in the cleaning fluid, directly altering sound propagation attenuation and cavitation nucleus density. The recommended synchronous acquisition cycle for these three parameters is 0.5–2 seconds, balancing real-time performance with system processing load.

[0020] S2: Extract frequency domain features from the cavitation acoustic emission signal and calculate the cavitation effectiveness index E, which characterizes the intensity of the current cavitation effect. cav ; S3: Construct a system that minimizes device power consumption and maximizes the cavitation efficiency index E. cav A power consumption optimization model with two objectives; S4: An improved multi-objective whale optimization algorithm is used to solve the power consumption optimization model to obtain the Pareto optimal solution set under the current cleaning state, and this is combined with the real-time temperature T. real and real-time turbidity C real Select the optimal control parameter X from the Pareto optimal solution set. best (t); S5: Based on the optimal control parameter X best (t) The output frequency and drive duty cycle of the ultrasonic generator are dynamically adjusted to optimize power consumption.

[0021] The specific process for calculating the cavitation efficiency index in step S2 includes: The acquired cavitation acoustic emission signal is subjected to multi-layer wavelet packet decomposition to reconstruct the low-frequency and high-frequency signals; Calculate the energy of the low-frequency signal as the discrete line spectrum energy E. line Calculate the energy of the high-frequency signal as the broadband noise energy E. broad ; Based on the discrete line spectrum energy Eline and broadband noise energy E broad Calculate the cavitation efficiency index E cav The expression is: ; Among them, E broad For broadband noise energy, E broadmax E is the preset broadband noise energy saturation threshold. line E represents the discrete line spectrum energy. lineopt The discrete line spectrum energy reference value is the value corresponding to the optimal transient cavitation, where α1 and α2 are both weighting coefficients.

[0022] The collected cavitation acoustic emission signals cannot be directly used as control indicators. They need to be transformed into a numerical value that can quantitatively characterize the cavitation "quality"—the cavitation effectiveness index E—after feature extraction. cav The specific process is as follows: First, the original signal undergoes multi-level wavelet packet decomposition. Compared to ordinary Fourier transform, wavelet packet decomposition can simultaneously preserve time and frequency domain information, making it more suitable for handling non-stationary cavitation noise. Typically, a wavelet basis with good compact support characteristics is chosen, and the number of decomposition levels is set to three to five. After decomposition, the sub-band signals corresponding to the ultrasonic driving frequency and its odd harmonic frequencies are recombine into low-frequency signals, which contain stable discrete line spectrum components; the remaining sub-bands reflecting random transient collapse processes are recombine into high-frequency signals, which mainly consist of broadband noise energy.

[0023] Next, the energy values ​​of the low-frequency and high-frequency signals are calculated separately. In practical engineering, the energy is usually obtained by summing the squares of the wavelet coefficients and then taking the logarithm (dB). Then, the cavitation efficiency index is calculated using two energy values: the broadband noise energy is divided by a preset saturation threshold and multiplied by a positive weighting coefficient to obtain the beneficial cavitation contribution; the discrete line spectrum energy is divided by a baseline line spectrum energy value representing the optimal transient cavitation state and multiplied by a negative weighting coefficient to obtain the harmful cavitation shielding term. The final cavitation efficiency index is the former minus the latter. This index is normalized to between 0 and 1; a higher value indicates a more ideal current cavitation state, meaning that the shock wave energy generated by transient collapse is sufficient and the cleaning effect is not weakened by an overly stable resonant bubble layer. The saturation threshold of the broadband noise energy and the baseline value of the optimal line spectrum energy can both be obtained through preliminary experiments using standard water quality and standard contaminated test pieces.

[0024] The fitness function F(X) of the power consumption optimization model in step S3 is expressed as: F(X)=[f1(X), f2(X)]; Where X is a control parameter vector, including output frequency and drive duty cycle; f1(X) is the objective function for device power consumption, f1(X) = U × I × cos(θ) × D, where U is the output voltage, θ is the phase difference between voltage and current, and D is the drive duty cycle; F2(X) is the objective function for cavitation effectiveness, f2(X) = -E cav (X), i.e., the cavitation efficiency index E cav Take negative values ​​to transform the problem into a minimization problem.

[0025] The control parameter vector of the optimization model includes the output frequency and drive duty cycle of the ultrasonic generator. The output frequency is typically continuously adjustable within a small range (e.g., ±5%) around the transducer resonant frequency; the drive duty cycle is adjusted between 20% and 100% in pulse output mode.

[0026] The two objective functions are interdependent: the first objective is the device's operating power consumption, which is equal to the product of output voltage, output current, power factor (i.e., the cosine of the phase difference between voltage and current), and drive duty cycle. The power factor reflects the generator's active power utilization efficiency; reactive power is minimized when the phase difference between voltage and current is zero. The second objective is cavitation efficiency, but to unify it as a minimization problem, a negative cavitation efficiency exponent is used as the objective function; that is, the smaller the value (the larger the corresponding cavitation efficiency exponent), the better.

[0027] The mathematical essence of this optimization problem is to minimize power consumption and negative cavitation efficiency (i.e., maximize cavitation efficiency) within the feasible region of frequency and duty cycle. Since the two objectives are often contradictory (e.g., increasing the duty cycle can enhance cavitation but also increase power consumption), there is no unique global optimal solution. Instead, there exists a set of Pareto optimal solutions, from which subsequent steps need to select the most suitable control parameters based on the operating conditions.

[0028] In step S4, a cavitation physical mapping mechanism is introduced, embedding the cavitation bubble collapse energy decay model into the helical position update of the algorithm. The specific process is as follows: This step employs an improved multi-objective whale optimization algorithm that integrates cavitation physical mapping and adaptive fluid viscous drag to iteratively solve the dual-objective optimization model. The algorithm parameters are set as follows: population size N=50, maximum number of iterations T. max =30, external archive size is set to 30.

[0029] In the shrinking encirclement and spiral update stages of the improved multi-objective whale optimization algorithm, a cavitation bubble collapse energy attenuation factor K is introduced. cav The spiral search radius is dynamically adjusted, and its position update formula is: ; Where X(t+1) is the updated position of the individual, X * (t) represents the current optimal individual position, D' represents the distance between the individual and the optimal individual, b is a constant defining the shape of the logarithmic spiral, and l is a random number between [-1, 1]. Calculate the energy decay factor K of cavitation bubble collapse. cav The expression is: ; Among them, E cav (t) represents the cavitation efficiency index corresponding to the current iteration number t, E cavmax T is the historical maximum cavitation efficiency index, λ is the attenuation regulation index, and T is the cavitation efficiency index. max This represents the maximum number of iterations.

[0030] In step S4, the real-time temperature T is introduced. real and real-time turbidity C real Converted to fluid viscous drag coefficient μ res This allows for adaptive adjustment of the encirclement step size of the algorithm, specifically: Based on real-time temperature T real and real-time turbidity C real Calculate the fluid viscous drag coefficient μ res The expression is: ; Where μ0 is the basic viscosity coefficient, B is the temperature-sensitive constant, and K is the K value. c β is the turbidity influence coefficient, and β is the zero constant for prevention and removal; Based on the fluid viscous drag coefficient μ res The convergence factor 'a' of the algorithm is dynamically adjusted during the exploration and development phases, expressed as: ; Where η is the resistance mapping weight, μ min and μ max These are the preset minimum and maximum fluid viscous drag coefficients, respectively. When the fluid viscous drag coefficient μ res As the value increases, the convergence factor a decays faster.

[0031] Combined with real-time temperature T real and real-time turbidity C real The process of selecting the optimal control parameters from the Pareto optimal solution set includes: Calculate the curvature of each solution in the Pareto optimal solution set and identify the inflection point region of the Pareto front; Constructing the environment preference vector V env =[V1, V2], where V1 and V2 represent the preference weights for power consumption and performance, respectively; When the real-time temperature deviates from the optimal cavitation temperature range or the real-time turbidity C real When the threshold is exceeded, adjust the environment preference vector to point to the low-power region; otherwise, point it to the high-efficiency region. Calculate the angle between each solution within the inflection point region and the environmental preference vector, and select the solution with the smallest angle as the optimal control parameter X. best (t).

[0032] The standard whale optimization algorithm mimics the bubble-web hunting behavior of humpback whales and possesses good global search and local exploitation capabilities. However, its direct application to cavitation optimization has two drawbacks: first, the position update lacks guidance from physical processes; second, the convergence factor remains constant, failing to adapt to drastic changes in the viscosity of the cleaning fluid due to temperature and turbidity. Therefore, this invention makes substantial improvements in two aspects.

[0033] (I) Cavitation Physical Mapping Mechanism In the spiral position update phase of the algorithm, a cavitation bubble collapse energy decay factor is introduced. This factor is calculated by multiplying the ratio of the cavitation efficiency index at the current iteration number to the historical maximum cavitation efficiency index by a term that decays exponentially with the number of iterations. This exponential decay term simulates the physical law of bubble collapse energy gradually dissipating with the sound wave period during actual cavitation. When the current cavitation efficiency index is high and the iteration is still in its early stages, this factor has a larger value, resulting in a larger spiral search radius, which is beneficial for fine-tuning the search near the optimal solution. Conversely, if the cavitation efficiency is poor or the iteration is nearing its end, the factor automatically decreases to avoid ineffective large-amplitude perturbations. This update method, which embeds a physical mechanism, significantly improves the algorithm's search targeting and convergence efficiency.

[0034] (ii) Adaptive adjustment of fluid viscous resistance Increased temperature or turbidity of the cleaning fluid leads to increased viscosity. Cavitation is more difficult to occur in high-viscosity environments, and sound wave propagation attenuation increases. Therefore, large-scale global exploration should be suppressed, and convergence to a local optimum should be accelerated; otherwise, frequent frequency jumps will cause energy waste or even damage the transducer. To address this, an equivalent fluid viscosity drag coefficient is first calculated based on real-time temperature and turbidity. This coefficient is composed of the base viscosity multiplied by a temperature-related exponential term, plus turbidity multiplied by an influence coefficient, and finally a small positive constant is added to prevent computational overflow. Then, this drag coefficient is used to dynamically adjust the convergence factor in the optimization algorithm. The convergence factor was originally a parameter that linearly decreases from 2 to 0 during iteration, controlling the timing of the algorithm's transition from global exploration to local development. The improved convergence factor, based on the original decreasing law, is multiplied by a correction term related to the drag coefficient: when the drag coefficient increases, the overall convergence factor decreases and its decay rate accelerates, thus allowing the algorithm to enter the local development stage more quickly. In addition, when the algorithm is in the exploration phase (i.e., using random individuals to guide the global search), an additional viscous damping attenuation term is introduced. This term is in exponential form, and as the drag coefficient increases, the step size of the global search decreases exponentially, effectively preventing excessive frequency jumps that violate physical reality under high viscosity.

[0035] With the above two improvements, the algorithm's search behavior is more in line with the actual physical constraints of the cavitation process, and the convergence speed and stability are significantly improved.

[0036] (III) Selection of optimal control parameters based on environmental preferences After iteration, a set of Pareto optimal solutions and their front surfaces are obtained. Not all solutions are suitable for the current cleaning conditions, so an environmental preference vector needs to be introduced. First, the curvature of each point on the Pareto front is calculated, and the region with the largest curvature, i.e. the inflection point region, is identified. The solutions in this region achieve the best trade-off between power consumption and efficiency. Then, a two-dimensional environmental preference vector is constructed based on real-time temperature and turbidity, where the first component represents the preference for low power consumption and the second component represents the preference for high efficiency. The specific calculation method is as follows: first, the absolute deviation of temperature from the optimal cavitation temperature and the excess amount of turbidity exceeding the safety threshold are calculated (if not exceeded, it is taken as zero). Then, these two quantities are divided by their respective maximum allowable values ​​and multiplied by weight coefficients, and the sum is obtained to obtain a comprehensive environmental degradation index. This index is mapped to the range of 0 to 1 through a hyperbolic tangent function, thus obtaining the first preference weight equal to this mapped value, and the second preference weight equal to 1 minus the first preference weight. When the overall environmental degradation index is high (e.g., excessively high temperature or severely excessive turbidity), the weight of the first preference approaches 1, indicating that a low-power solution should be prioritized, as pursuing high-efficiency cavitation is meaningless and may even damage the equipment. Conversely, the weight of the second preference is larger, indicating that a solution with high cavitation efficiency should be prioritized to ensure cleaning quality. Finally, the angle between each solution and the environmental preference vector is calculated within the inflection point region, and the solution with the smallest angle is selected as the optimal control parameter for the current moment.

[0037] The specific method for dynamically adjusting the output frequency and drive duty cycle of the ultrasonic generator in step S5 is as follows: Based on the output frequency in the optimal control parameters, phase tracking fine-tuning is performed at the resonant frequency point of the ultrasonic transducer to make the phase difference between the output voltage and the output current approach zero. Based on the drive duty cycle in the optimal control parameters, the continuous output mode of the ultrasonic generator is switched to the pulse output mode. By adjusting the duty cycle of the pulse width modulation signal, the cavitation shielding effect is eliminated.

[0038] Once the optimal control parameters are reached, they cannot be directly applied to the ultrasonic generator. Otherwise, frequent parameter fluctuations may occur due to signal noise or random algorithm fluctuations, affecting equipment stability and transducer lifespan. To address this, this invention introduces a smooth execution mechanism with dead time and inertial filtering.

[0039] First, the distance between the optimal control parameters selected at the current moment and the actual control parameters executed at the previous moment is calculated (the Euclidean distance combining the frequency and duty cycle dimensions). This distance is compared with a variable dead zone threshold: if the distance is less than the dead zone threshold, the original actual execution parameters are maintained unchanged without any adjustment. This dead zone threshold is not fixed but adaptively adjusted according to the real-time rate of change of the cleaning fluid turbidity: when the turbidity rises rapidly (e.g., large pieces of dirt suddenly fall off), the dead zone threshold automatically decreases, making the system more sensitive to parameter changes; when the turbidity changes gradually, the dead zone threshold returns to a baseline value to maintain stable control. This adaptive dead zone design effectively balances disturbance resistance and rapid tracking of sudden changes in dirt levels.

[0040] When the change distance reaches or exceeds the dead zone threshold, a first-order inertial filter is applied to the current optimal control parameters: the filtered actual execution parameters are equal to the smoothing coefficient multiplied by the current optimal parameters, plus or minus the coefficient multiplied by the actual execution parameters at the previous moment. The smoothing coefficient is typically between 0.3 and 0.7; the smaller the coefficient, the stronger the filtering effect and the smoother the parameter changes.

[0041] Finally, the filtered execution parameters are sent to the ultrasonic generator. For frequency adjustment, the generator uses phase-locked loop (PLL) technology to monitor the phase difference between the output voltage and current in real time, fine-tuning it near the frequency given by the optimal control parameters to bring the phase difference as close to zero as possible, thereby improving the power factor to close to 1 and reducing reactive power loss. For duty cycle adjustment, the generator switches from continuous output mode to pulse output mode. By adjusting the duty cycle of the pulse width modulation signal, it ensures effective cavitation intensity while providing necessary intervals for bubble collapse and re-nucleation, thus eliminating the bubble shielding effect that is easily generated by continuous cavitation. Numerous experiments show that for moderately contaminated objects, a duty cycle of 60% to 80% can achieve more than 95% of the cleaning effect of continuous mode, while reducing actual active power consumption by more than 30%.

[0042] Based on the fluid viscous drag coefficient μ res After dynamically adjusting the convergence factor 'a', when the algorithm is in the exploration phase, a viscous damping attenuation term is introduced to modify the individual's position update formula. The modified position update formula is as follows: ; Where X(t+1) is the updated position of the individual, X rand Let A be the position of an individual randomly selected in the current population, and let D be the coefficient vector calculated based on the convergence factor a. rand Let ξ be the distance vector of a random individual, and μ be the damping sensitivity coefficient. res μ is the fluid viscous drag coefficient. maxThe preset maximum fluid viscous drag coefficient is given by the fluid viscous drag coefficient μ. res As the step size increases, the global search step size decreases exponentially.

[0043] In constructing the environmental preference vector V env In the value [V1, V2], the specific dynamic calculation process of preference weights V1 and V2 is as follows: Calculate temperature deviation And the turbidity exceedance rate ΔC = max(0, C threshold ), where T real For real-time temperature, T opt For the optimal cavitation temperature, C threshold This is the turbidity safety threshold. The comprehensive environmental degradation index E is calculated based on the calculated temperature deviation ΔT and turbidity exceedance rate ΔC. env The expression is: ; Among them, △T max For the maximum allowable temperature deviation, C max ω1 and ω2 are the normalized weighting coefficients, representing the maximum turbidity range. Based on the comprehensive environmental degradation index E env The preference weights V1 and V2 are calculated using the following expression: ; Where κ is the environmental sensitivity adjustment coefficient, tanh is the hyperbolic tangent function, and when the comprehensive environmental degradation index E env When V1 increases and V2 decreases, the environment preference vector V... env It non-linearly points to the low-power region.

[0044] The solution with the smallest included angle is selected as the optimal control parameter X. best After (t), to avoid frequent jumps in the control parameters of the ultrasonic generator, a hysteresis smoothing mechanism for the control parameters is introduced. The specific process includes: Calculate the optimal control parameter X selected at the current time. best (t) The number of control parameters actually executed at the previous time step X exec The parameter variation distance ΔX between (t-1) is where the control parameter X includes the output frequency and the drive duty cycle; Determine whether the parameter change distance ΔX is less than the preset dead zone threshold δ. If yes, the actual control parameter executed at the current moment remains unchanged. If no, that is, the parameter change distance ΔX is greater than or equal to the dead zone threshold δ, then the optimal control parameter X selected at the current moment is... best (t) Perform first-order inertial filtering to obtain the actual control parameters X executed at the current time. exec(t), the expression is: ; Where γ is the filtering smoothing coefficient, and 0 < γ ≤ 1.

[0045] Based on the dead zone threshold δ and the real-time turbidity C of the cleaning fluid real The rate of change is adaptively adjusted, and the expression is: ; Where δ(t) is the dead zone threshold at the current time, δ base The basic dead zone threshold is given, ρ is the rate of change sensitivity coefficient, and C is the base dead zone threshold. real (t) represents the real-time turbidity at the current moment, C real (t-△t) represents the real-time turbidity of the previous sampling period, where △t is the sampling period. When the real-time turbidity C real When the rate of change increases, the dead zone threshold δ(t) decreases to improve the control parameter X. exec (t) Sensitivity to the process of dirt removal by the cleaning solution.

[0046] It should be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0047] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing the operating power consumption of an ultrasonic cleaning device, characterized in that, Includes the following steps: S1: Real-time acquisition of cavitation acoustic emission signals within the ultrasonic cleaning tank, and the real-time temperature T of the cleaning fluid. real and real-time turbidity C real ; S2: Extract frequency domain features from the cavitation acoustic emission signal and calculate the cavitation effectiveness index E, which characterizes the intensity of the current cavitation effect. cav ; The specific process for calculating the cavitation efficiency index in step S2 includes: The acquired cavitation acoustic emission signal is subjected to multi-layer wavelet packet decomposition to reconstruct the low-frequency and high-frequency signals; Calculate the energy of the low-frequency signal as the discrete line spectrum energy E. line Calculate the energy of the high-frequency signal as the broadband noise energy E. broad ; Based on the discrete line spectrum energy E line and broadband noise energy E broad Calculate the cavitation efficiency index E cav The expression is: ； Among them, E broad For broadband noise energy, E broadmax E is the preset broadband noise energy saturation threshold. line E represents the discrete line spectrum energy. lineopt α1 and α2 are the discrete line spectrum energy reference values ​​corresponding to the optimal transient cavitation, where α1 and α2 are both weighting coefficients. S3: Construct a system that minimizes device power consumption and maximizes the cavitation efficiency index E. cav A power consumption optimization model with two objectives; S4: An improved multi-objective whale optimization algorithm is used to solve the power consumption optimization model to obtain the Pareto optimal solution set under the current cleaning state, and this is combined with the real-time temperature T. real and real-time turbidity C real Select the optimal control parameter X from the Pareto optimal solution set. best (t); S5: Based on the optimal control parameter X best (t) The output frequency and drive duty cycle of the ultrasonic generator are dynamically adjusted to optimize power consumption.

2. The method for optimizing the operating power consumption of an ultrasonic cleaning device according to claim 1, characterized in that, The fitness function F(X) of the power consumption optimization model in step S3 is expressed as: F(X)=[f1(X), f2(X)]; Where X is a control parameter vector, including output frequency and drive duty cycle; f1(X) is the objective function for device power consumption, f1(X) = U × I × cos(θ) × D, where U is the output voltage, θ is the phase difference between voltage and current, and D is the drive duty cycle; F2(X) is the objective function for cavitation effectiveness, f2(X) = -E cav (X), i.e., the cavitation efficiency index E cav Take negative values ​​to transform the problem into a minimization problem.

3. The method for optimizing the operating power consumption of an ultrasonic cleaning device according to claim 1, characterized in that, In step S4, a cavitation physical mapping mechanism is introduced, embedding the cavitation bubble collapse energy decay model into the helical position update of the algorithm. The specific process is as follows: In the shrinking encirclement and spiral update stages of the improved multi-objective whale optimization algorithm, a cavitation bubble collapse energy attenuation factor K is introduced. cav The spiral search radius is dynamically adjusted, and its position update formula is: ； Where X(t+1) is the updated position of the individual, X * (t) represents the current optimal individual position, D' represents the distance between the individual and the optimal individual, b is a constant defining the logarithmic spiral shape, and l is a random number between [-1, 1]. Calculate the energy decay factor K of cavitation bubble collapse. cav The expression is: ； Among them, E cav (t) represents the cavitation efficiency index corresponding to the current iteration number t, E cavmax T is the historical maximum cavitation efficiency index, λ is the attenuation regulation index, and T is the cavitation efficiency index. max This represents the maximum number of iterations.

4. The method for optimizing the operating power consumption of an ultrasonic cleaning device according to claim 1, characterized in that: In step S4, the real-time temperature T is introduced. real and real-time turbidity C real Converted to fluid viscous drag coefficient μ res This allows for adaptive adjustment of the encirclement step size of the algorithm, specifically: Based on real-time temperature T real and real-time turbidity C real Calculate the fluid viscous drag coefficient μ res The expression is: ； Where μ0 is the basic viscosity coefficient, B is the temperature-sensitive constant, and K is the K value. c β is the turbidity influence coefficient, and β is the zero constant for prevention and removal; Based on the fluid viscous drag coefficient μ res The convergence factor 'a' of the algorithm is dynamically adjusted during the exploration and development phases, expressed as: ； Where η is the resistance mapping weight, μ min and μ max These are the preset minimum and maximum fluid viscous drag coefficients, respectively. When the fluid viscous drag coefficient μ res As the value increases, the convergence factor a decays faster.

5. The method for optimizing the operating power consumption of an ultrasonic cleaning device according to claim 4, characterized in that, Combined with real-time temperature T real and real-time turbidity C real The process of selecting the optimal control parameters from the Pareto optimal solution set includes: Calculate the curvature of each solution in the Pareto optimal solution set and identify the inflection point region of the Pareto front; Constructing the environment preference vector V env =[V1, V2], where V1 and V2 represent the preference weights for power consumption and performance, respectively; When the real-time temperature deviates from the optimal cavitation temperature range or the real-time turbidity C real When the threshold is exceeded, adjust the environment preference vector to point to the low-power region; otherwise, point it to the high-efficiency region. Calculate the angle between each solution and the environmental preference vector within the inflection point region, and select the solution with the smallest angle as the optimal control parameter X. best (t).

6. The method for optimizing the operating power consumption of an ultrasonic cleaning device according to claim 1, characterized in that, The specific method for dynamically adjusting the output frequency and drive duty cycle of the ultrasonic generator in step S5 is as follows: Based on the output frequency in the optimal control parameters, phase tracking fine-tuning is performed at the resonant frequency point of the ultrasonic transducer to make the phase difference between the output voltage and the output current approach zero. Based on the drive duty cycle in the optimal control parameters, the continuous output mode of the ultrasonic generator is switched to the pulse output mode. By adjusting the duty cycle of the pulse width modulation signal, the cavitation shielding effect is eliminated.

7. The method for optimizing the operating power consumption of an ultrasonic cleaning device according to claim 4, characterized in that: Based on the fluid viscous drag coefficient μ res After dynamically adjusting the convergence factor 'a', when the algorithm is in the exploration phase, a viscous damping attenuation term is introduced to modify the individual's position update formula. The modified position update formula is as follows: ； Where X(t+1) is the updated position of the individual, X rand Let A be the position of an individual randomly selected in the current population, and let D be the coefficient vector calculated based on the convergence factor a. rand Let ξ be the distance vector of a random individual, and μ be the damping sensitivity coefficient. res μ is the fluid viscous drag coefficient. max The preset maximum fluid viscous drag coefficient is given by the fluid viscous drag coefficient μ. res As the step size increases, the global search step size decreases exponentially.

8. The method for optimizing the operating power consumption of an ultrasonic cleaning device according to claim 5, characterized in that, In constructing the environmental preference vector V env In the value [V1, V2], the specific dynamic calculation process of preference weights V1 and V2 is as follows: Calculate temperature deviation And the turbidity exceedance rate ΔC = max(0, C threshold ), where T real For real-time temperature, T opt For the optimal cavitation temperature, C threshold This is the turbidity safety threshold. The comprehensive environmental degradation index E is calculated based on the calculated temperature deviation ΔT and turbidity exceedance rate ΔC. env The expression is: ； Among them, △T max For the maximum allowable temperature deviation, C max ω1 and ω2 are the normalized weighting coefficients, representing the maximum turbidity range. Based on the comprehensive environmental degradation index E env The preference weights V1 and V2 are calculated using the following expression: ； Where κ is the environmental sensitivity adjustment coefficient, tanh is the hyperbolic tangent function, and when the comprehensive environmental degradation index E env When V1 increases and V2 decreases, the environment preference vector V... env It non-linearly points to the low-power region.

9. The method for optimizing the operating power consumption of an ultrasonic cleaning device according to claim 8, characterized in that, The solution with the smallest included angle is selected as the optimal control parameter X. best After (t), to avoid frequent jumps in the control parameters of the ultrasonic generator, a hysteresis smoothing mechanism for the control parameters is introduced. The specific process includes: Calculate the optimal control parameter X selected at the current time. best (t) The number of control parameters actually executed at the previous time step X exec The parameter variation distance ΔX between (t-1) is where the control parameter X includes the output frequency and the drive duty cycle; Determine whether the parameter change distance ΔX is less than the preset dead zone threshold δ. If yes, the actual control parameter executed at the current moment remains unchanged. If no, that is, the parameter change distance ΔX is greater than or equal to the dead zone threshold δ, then the optimal control parameter X selected at the current moment is... best (t) Perform first-order inertial filtering to obtain the actual control parameters X executed at the current time. exec (t), the expression is: ； Where γ is the filtering smoothing coefficient, and 0 < γ ≤ 1.

10. The method for optimizing the operating power consumption of an ultrasonic cleaning device according to claim 9, characterized in that, Based on the dead zone threshold δ and the real-time turbidity C of the cleaning fluid real The rate of change is adaptively adjusted, and the expression is: ; Where δ(t) is the dead zone threshold at the current time, δ base The basic dead zone threshold is given, ρ is the rate of change sensitivity coefficient, and C is the base dead zone threshold. real (t) represents the real-time turbidity at the current moment, C real (t-△t) represents the real-time turbidity of the previous sampling period, where △t is the sampling period. When the real-time turbidity C real When the rate of change increases, the dead zone threshold δ(t) decreases to improve the control parameter X. exec (t) Sensitivity to the process of dirt removal by the cleaning solution.

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