A smart, noiseless ultrasonic cleaning method and device

Abstract

This invention belongs to the field of power system technology and discloses an intelligent noiseless ultrasonic cleaning method and device. The method includes: collecting dust samples from the surface of a target component and performing composition testing; analyzing the composition information of the dust samples; matching the cleaning parameter combinations in a pre-configured frequency matching table to the analysis results; and outputting cleaning control parameters; testing the acoustic energy field distribution on the surface of the target component by exciting acoustic energy at an excitation point; plotting energy field and other energy distribution curves; determining the optimal number, frequency, layout position, and excitation sequence of acoustic wave oscillators that meet the cleaning energy threshold; and outputting a cleaning deployment plan; and driving the acoustic wave oscillators in the cleaning device to excite ultrasonic waves containing longitudinal and transverse waves according to the set cleaning deployment plan, performing ultrasonic cleaning at the interface between the target component surface and dust to achieve dust removal. This invention utilizes the simultaneous vibration of a single or multiple acoustic wave oscillators to generate resonance, thereby improving the cleaning effect.

Description

Technical Field

[0001] This invention relates to the field of sound wave applications, and in particular to an intelligent noiseless ultrasonic cleaning method and apparatus. Background Technology

[0002] Cement and electricity are among the most important industries in my country's national economy. In one year, my country's electricity consumption exceeded 10 trillion kilowatt-hours for the first time globally, with coal-fired power accounting for over 50%. With the development and application of AI technology, my country's annual electricity consumption is expected to continue growing for a considerable period. The cement industry is a crucial foundation of my country's national economy, with an annual output of 1.825 billion tons. In both industries, boilers are key pieces of equipment during production. However, when electric boilers burn fossil fuels or biomass fuels, ash from the fuel deposits on the heated surfaces of the furnace and tail flue (such as water-cooled walls, superheaters, reheaters, economizers, and air preheaters), forming ash and slag. Cement kilns emit large amounts of high-temperature waste gas at the kiln head and tail. The waste heat carried by this gas is usually recovered and utilized through a matching waste heat boiler to drive power generation. A long-standing and significant technical challenge during the operation of waste heat boilers is that the flue gas contains high concentrations of highly viscous, easily caking fine dust from cement raw materials and clinker. When these dust particles flow through the heating surfaces, they are easily adsorbed and accumulate on the outer surface of boiler heat exchange tubes (such as superheaters, evaporators, economizers, etc.), forming a dense ash layer with extremely poor thermal conductivity. The presence of this ash layer severely hinders the transfer of heat from flue gas to the working fluid (water / steam), leading to a significant decrease in boiler thermal efficiency and a reduction in steam production and power generation. Simultaneously, the ash accumulates and blocks flue gas flow channels, increasing system ventilation resistance and forcing the kiln system's main exhaust fan to increase its power to maintain negative pressure, resulting in unnecessary energy consumption. Furthermore, severe localized ash accumulation can also cause localized overheating, corrosion, or even tube rupture on the heating surfaces, threatening the continuous and stable operation of the production line. Therefore, an efficient and reliable soot blowing system is an indispensable key piece of equipment for ensuring the safe, economical, and continuous operation of the boiler.

[0003] Existing soot blowing systems mainly include the following methods: 1) Steam soot blowing, due to its high soot blowing energy, long range, and wide coverage, has long been the mainstream soot cleaning method for boilers in the power industry (especially large pulverized coal boilers). Its basic principle is: using superheated steam generated by the boiler itself or auxiliary steam source, high-pressure steam is sprayed onto the heated surface in the form of a high-speed jet through the nozzle at the end of the soot blower, and the ash is peeled off by the kinetic energy and thermal shock of the jet.

[0004] 2) An automatic sootblowing system based on a retractable steam sootblower typically consists of the sootblower body (including a steam inlet valve, motor, gearbox, forward / reverse mechanism, spray gun pipe, and nozzle), a steam piping system, a drainage system, and a program control system. Under program control, the spray gun pipe automatically extends into the furnace, rotating and spraying steam. After completing the purging of a fan-shaped or strip-shaped area, it automatically retracts to protect the spray gun from high-temperature radiation inside the furnace.

[0005] 3) The basic principle of the rotary hammer rapping system or the side impact rapping system is: a motor (or pneumatic device) drives a long transmission shaft (or drives a single rapping hammer) that runs through multiple compartments of the boiler. Multiple rapping hammers are installed on the shaft at a certain phase. When the long shaft rotates, the rapping hammers are raised and lowered in sequence. By impacting the rapping rod (or anvil) that is mechanically connected to the heated surface tube bank, the impact force is transmitted to the entire tube bank, causing it to vibrate at high frequency, thereby shaking off the ash adhering to the tube wall.

[0006] 4) Acoustic cleaning device: This is an environmentally friendly physical cleaning technology that uses high-intensity sound waves (usually low frequency and high energy) as an energy carrier to remove dust from industrial equipment.

[0007] The existing soot blowing system has the following main shortcomings: 1) Steam soot blowing, and automatic soot blowing systems based on retractable steam soot blowers, suffer from significant issues of heat transfer surface damage and lifespan reduction, creating a sharp contradiction. This is the most criticized drawback of steam soot blowing. High-pressure steam jets (especially when the steam carries water or the pressure is unstable) exert a continuous erosive and abrasive effect on the boiler tube walls. (Ref. 1) 2) Steam soot blowing and automatic soot blowing systems based on retractable steam soot blowers still suffer from high steam consumption and poor operating economy. In the traditional timed soot blowing mode, the energy cost of steam soot blowing accounts for a considerable proportion of its benefits, especially when burning low-ash coal or when the boiler is in good operating condition, resulting in a large amount of "ineffective soot blowing" and energy waste.

[0008] 3) Rotary hammer rapping systems or side-impact rapping systems suffer from dead zones and uneven dust removal: Existing rotary hammer or side-impact rapping devices are ineffective in transmitting vibration force in areas far from the rapping point, with dense structural supports, or complex pipe configurations, easily creating "dust removal dead zones." Dust accumulates and hardens in these dead zones, eventually becoming a bottleneck affecting overall thermal efficiency. Poor equipment reliability and high maintenance workload: The long drive shaft needs to traverse multiple high-temperature, high-dust chambers, placing extremely high demands on the sealing and high-temperature resistance of the bearings. Long-term operation can easily lead to problems such as bearing seizure, lubrication failure, and dust intrusion due to seal damage. The rapping hammer, rapping rod (anvil), and corresponding impact-bearing components are subjected to high-stress impacts over long periods, making them highly susceptible to fatigue cracks, deformation, fracture, or severe wear. This results in frequent equipment failures, requiring frequent shutdowns for maintenance and replacement, leading to high maintenance costs and affecting the continuity of the cement production line.

[0009] 4) Acoustic cleaning devices have limited effectiveness against hardened, compacted grime and are typically used for preventative maintenance to prevent the accumulation of dirt and grime. The noise level is generally too high, usually exceeding 150 dB, which does not meet current environmental protection requirements for low noise levels.

[0010] Therefore, how to provide an intelligent and noiseless ultrasonic cleaning method and device is an urgent problem to be solved. Summary of the Invention

[0011] This invention provides an intelligent, noiseless ultrasonic cleaning method and apparatus to solve the problems mentioned above in the prior art.

[0012] According to a first aspect of the present invention, an intelligent noiseless ultrasonic cleaning method is provided.

[0013] In one embodiment, the intelligent noiseless ultrasonic cleaning method includes: collecting dust samples from the surface of a target component and performing composition testing, analyzing the composition information of the dust samples, and obtaining analysis results; The system initially matches the dust removal parameter combinations from the pre-configured frequency matching table with the analysis results, and outputs the dust removal control parameters. Based on the dust removal control parameters, it uses the excitation point to excite acoustic energy to test the acoustic energy field distribution on the surface of the target component, and plots the energy field and other energy distribution curves. Based on the energy field and other energy distribution curves, it determines the optimal number, frequency, layout position, and excitation sequence of acoustic oscillators that meet the dust removal energy threshold, and outputs the dust removal deployment plan. According to the set dust removal deployment plan, it drives the set acoustic oscillators in the dust removal device to excite ultrasonic waves containing longitudinal and transverse waves to perform ultrasonic dust removal at the interface between the target component surface and dust, so as to achieve dust removal. The dust removal effect is evaluated by integrated sensors, and the frequency matching table and dust removal deployment plan are optimized based on the evaluation results.

[0014] In one embodiment, collecting dust samples from the surface of the target component and performing compositional testing, analyzing the compositional information of the dust samples, and obtaining analysis results includes: collecting dust samples from the surface of the target component in the boiler pipe; analyzing the particle size, diameter, density, and material of the collected dust samples, and obtaining analysis results.

[0015] In one embodiment, the preliminary matching of the pre-configured frequency matching table with the dust removal parameter combination adapted to the analysis results and the output of dust removal control parameters includes: constructing a frequency matching table by combining engineering physical principles with historical dust removal experimental data; inputting the analysis results into the frequency matching table for mapping processing and outputting preliminary dust removal parameters; and automatically filtering and matching the preliminary dust removal parameters according to the classification rules and mapping rules built into the frequency matching table to generate dust removal control parameters.

[0016] In one embodiment, the step of testing the acoustic energy field distribution on the surface of a target component by exciting acoustic energy through an excitation point based on the dust removal control parameters, and plotting the energy field and other energy distribution curves, includes: selecting a test point and installing a single acoustic oscillator as the excitation point; exciting ultrasonic waves through the excitation point, and using an acoustic wave detector to perform a gridded scan on the surface of the target component to test the acoustic energy field, measuring and recording the acoustic energy value at each measurement point; and plotting the energy field and other energy distribution curves on the surface of the target component based on the acoustic energy value at each measurement point.

[0017] In one embodiment, determining the optimal number, frequency, layout position, and excitation sequence of acoustic oscillators that meet the dust removal energy threshold based on energy distribution curves such as the energy field, and outputting the dust removal deployment scheme includes: determining the required energy threshold for dust removal based on energy distribution curves such as the energy field; calculating the optimal number, frequency, layout position, and excitation sequence of acoustic oscillators required to meet the full-field dust removal energy requirements by analyzing the relationship between energy distribution curves such as the energy field and the energy threshold, thereby forming an executable dust removal deployment scheme.

[0018] In one embodiment, the step of driving a set acoustic wave vibrator in the dust removal device to excite ultrasonic waves containing longitudinal and transverse waves according to a set dust removal deployment plan, and performing ultrasonic cleaning on the interface between the target component surface and dust to achieve dust removal includes: a logic control mechanism sending a control command to the excitation mechanism according to the set dust removal deployment plan; the excitation mechanism controlling the designated acoustic wave vibrator to simultaneously vibrate longitudinal and transverse wave signals, forming high-frequency alternating stress; under the continuous action of high-frequency alternating stress, microcracks begin to form inside the dust layer on the surface of the target component; based on material fatigue... According to strength theory, the fatigue strength of dust is far lower than its static compressive or shear strength. Under stress levels far below the required static compressive or shear strength, after a sufficient number of stress cycles, cracks within the dust layer can initiate, propagate, and interconnect. When the structural fatigue damage within the dust layer of the target component accumulates to a critical point, the adhesion between the dust and the component substrate, either entirely or partially, is completely destroyed. Under the action of gravity or airflow, the dust peels off and falls off, achieving efficient cleaning. The entire dust removal process is carried out in the ultrasonic frequency band, and through structural vibration isolation design, noise-free operation is ensured.

[0019] In one embodiment, the excitation mechanism controls a designated acoustic oscillator to simultaneously generate longitudinal and transverse wave signals to form high-frequency alternating stress. This includes: the acoustic oscillator being composed of a piezoelectric transducer, an amplitude transformer, and an impact connection assembly; the excitation mechanism controls the piezoelectric transducer built into the designated acoustic oscillator to generate high-frequency mechanical vibration under the drive of a high-voltage electrical signal, the generated high-frequency mechanical vibration containing both longitudinal and transverse wave signals; the longitudinal and transverse wave signals are amplified by the amplitude transformer and then transmitted to the surface of the target component through the impact connection assembly, thus forming high-frequency alternating stress.

[0020] In one embodiment, the longitudinal wave signal and the transverse wave signal are amplified by an amplitude transformer and then transmitted to the surface of the target component through a vibration connection assembly, forming a high-frequency alternating stress. This stress includes: according to solid acoustics theory, the propagation speed of the longitudinal wave signal is determined by the elastic modulus and density of the material. Because the longitudinal wave signal is faster, it acts first on the interface between the dust and the target component, applying periodic tensile and compressive stress to the dust; the transverse wave signal arrives subsequently, and its propagation speed depends on the shear modulus and density of the material, applying periodic shear stress to the same interface; the tensile and compressive stresses and the shear stresses are superimposed in time and space, jointly forming a high-frequency alternating stress.

[0021] In one embodiment, the step of evaluating the dust removal effect through integrated sensors and optimizing the frequency matching table and dust removal deployment scheme based on the evaluation results includes: monitoring the dust removal process and the state of the target component after dust removal through integrated sensors; extracting the dust removal effect coefficient from the monitoring data and comparing it with a preset dust removal effect reference range; if the dust removal effect coefficient belongs to the dust removal effect reference range, the dust removal effect is determined to be up to standard, and there is no need to optimize the frequency matching table and dust removal deployment scheme, and the current dust removal deployment scheme continues to be run and monitored; if the dust removal effect coefficient does not belong to the dust removal effect reference range, the dust removal effect deviation value is obtained, and the parameter mapping relationship in the frequency matching table and the dust removal deployment scheme are iteratively optimized based on the dust removal effect deviation value, while issuing a maintenance warning according to the degree of deviation of the dust removal effect deviation value.

[0022] According to a second aspect of the present invention, an intelligent noiseless ultrasonic cleaning device is provided.

[0023] In one embodiment, the intelligent noiseless ultrasonic cleaning device includes: The system comprises a logic control mechanism, an excitation mechanism, several acoustic wave oscillators, an acoustic wave detector, an integrated sensor, and a power module; wherein the acoustic wave oscillator is composed of a piezoelectric transducer, an amplitude transformer, and a vibration connection assembly. Dust samples from the surface of the target component are collected for physical property analysis to obtain particle size, density, and material information. The analysis results are then input into a logic control mechanism. The logic control mechanism calls a frequency matching table to generate cleaning control parameters. The logic control mechanism instructs an excitation mechanism to drive a single test acoustic oscillator and coordinates with an acoustic wave detector to scan the component surface, plotting energy field and other energy distribution curves. Based on the energy field and other energy distribution curves and the cleaning energy threshold, the logic control mechanism calculates the optimal number, layout, and excitation sequence of the required acoustic oscillators, forming a cleaning deployment plan. When the dust removal deployment plan is executed, the excitation mechanism outputs a specific electrical signal to each acoustic wave vibrator in the array according to the dust removal deployment plan. The piezoelectric transducer in each acoustic wave vibrator converts the electrical signal into mechanical vibration. After being amplified by the amplitude transformer, the vibration is coupled to the component through the vibration connection assembly, which excites a high-frequency alternating stress containing both longitudinal and transverse waves. This high-frequency alternating stress propagates in the component, and the longitudinal and transverse waves apply high-frequency tensile and shear alternating stresses to the dust interface, causing the dust layer to peel off due to material fatigue. During and after the dust removal process, integrated sensors monitor the dust removal process and the status of the target components in real time to evaluate the dust removal effect and feed the data back to the logic control mechanism. Based on the deviation between the effect data and the preset target, the logic control mechanism dynamically optimizes the mapping rules of the frequency matching table and the dust removal deployment plan through a self-learning algorithm. The power module supplies power to the logic control mechanism, the excitation mechanism, several acoustic oscillators, the acoustic detector, and the integrated sensors to realize an intelligent dust removal process from intelligent diagnosis, precise planning, adaptive execution to continuous optimization. The entire process operates in the ultrasonic frequency band and is combined with vibration isolation design to maintain noiseless operation.

[0024] According to a third aspect of the present invention, a computer device is provided.

[0025] In some embodiments, the computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method described above.

[0026] According to a fourth aspect of the present invention, a computer-readable storage medium is provided.

[0027] In one embodiment, a computer program is stored on the computer-readable storage medium, which, when executed by a processor, implements the steps of the above method.

[0028] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects: 1) This invention is noiseless, utilizing an ultrasonic frequency range that does not generate noise. The figure shows the sound level of a certain acoustic dust removal method. For example... Figure 13 The image shows the test results for a certain brand of acoustic cleaning system. The voltage drop was generally above 150dB in the 200Hz-10kHz range. Figure 14 The image shows the test results of an oscillator at a certain frequency in this device. The sound intensity at 40kHz is approximately 140dB, while the sound intensity below 20kHz within the sound wave range is generally less than 50dB.

[0029] 2) The acoustic oscillator of this invention uses both longitudinal and transverse waves, similar to the shape of seismic waves, and employs high-frequency vibration to achieve the required number of cycles for fatigue strength.

[0030] 3) This invention can use acoustic wave oscillators of different frequencies to adapt to different working conditions.

[0031] 4) This invention can cause a single acoustic wave vibrator or multiple acoustic wave vibrators to vibrate simultaneously, generating resonance and improving the dust removal effect.

[0032] 5) This invention, through its integrated sensor-based real-time evaluation and automatic optimization mechanism, achieves precise monitoring and optimization of the dust removal process. It can automatically adjust the dust removal plan based on real-time monitoring data, ensuring the dust removal effect is always at its best. By automatically adjusting the frequency matching table and dust removal deployment plan, the system not only improves dust removal efficiency but also reduces maintenance costs and energy consumption. Furthermore, it can promptly issue maintenance warnings based on the degree of deviation in the dust removal effect, ensuring the long-term stable operation of the equipment.

[0033] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0034] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0035] Figure 1 This is a flowchart illustrating an intelligent noiseless ultrasonic cleaning method according to an exemplary embodiment; Figure 2 This is a structural block diagram of an intelligent noiseless ultrasonic cleaning device according to an exemplary embodiment; Figure 3 This is a schematic diagram of the structure of a computer device according to an exemplary embodiment; Figure 4 This is a schematic diagram of resonance according to an exemplary embodiment; Figure 5 This is an energy field distribution diagram of an experimental model illustrated according to an exemplary embodiment; Figure 6 This is a control interface diagram illustrated according to an exemplary embodiment; Figure 7 This is a schematic diagram of longitudinal wave propagation according to an exemplary embodiment; Figure 8 This is a schematic diagram of a longitudinal wave piezoelectric ceramic according to an exemplary embodiment; Figure 9 This is a schematic diagram of transverse wave propagation according to an exemplary embodiment; Figure 10 This is a schematic diagram of a transverse wave piezoelectric ceramic according to an exemplary embodiment; Figure 11 This is a stress distribution diagram acting on a micro-cube according to an exemplary embodiment; Figure 12 This is a concrete SN curve diagram illustrated according to an exemplary embodiment; Figure 13 This is an acoustic intensity diagram of a certain brand of acoustic soot remover, illustrated according to an exemplary embodiment. Figure 14 This is an acoustic intensity diagram of the present invention illustrated according to an exemplary embodiment. Detailed Implementation

[0036] The following description and accompanying drawings fully illustrate specific embodiments described herein to enable those skilled in the art to practice them. Some portions and features of certain embodiments may be included in or replace portions and features of other embodiments. The scope of the embodiments herein includes the entire scope of the claims and all available equivalents thereof. The various embodiments described herein are presented in a progressive manner, with each embodiment focusing on its differences from other embodiments; similar or identical parts between embodiments can be referred to interchangeably.

[0037] The modules in the apparatus or system of this application can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0038] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0039] Figure 1 An embodiment of an intelligent noiseless ultrasonic cleaning method according to the present invention is shown.

[0040] In this optional embodiment, the intelligent noiseless ultrasonic cleaning method includes: Collect dust samples from the surface of the target component and perform compositional testing. Analyze the compositional information of the dust samples to obtain the analysis results. The initial matching of the dust removal parameter combination with the analysis results in the pre-configured frequency matching table is performed, and the dust removal control parameters are output. Based on the dust removal control parameters, the acoustic energy field distribution on the surface of the target component is tested by exciting acoustic energy at the excitation point, and the energy field and other energy distribution curves are plotted. Based on energy distribution curves such as energy fields, the optimal number, frequency, layout position and excitation sequence of acoustic oscillators that meet the dust removal energy threshold are determined, and a dust removal deployment scheme is output. According to the set dust removal deployment plan, the acoustic oscillator set in the dust removal device is driven to excite ultrasonic waves containing longitudinal and transverse waves to perform ultrasonic cleaning on the interface between the target component surface and dust, so as to remove the dust. The dust removal effect is evaluated by integrating sensors, and the frequency matching table and dust removal deployment scheme are optimized based on the evaluation results.

[0041] Specifically, this invention employs a method of applying high-frequency ultrasonic waves to easily ash-collecting components (the heat dissipation part of a boiler, typically with steel pipes) through the excitation mechanism of a ash-cleaning device. The frequency of the ultrasonic waves is between 5KHz and 100KHz. The ultrasonic waves contain both longitudinal and transverse waves. According to the propagation characteristics of waves, the longitudinal waves reach the component first, generating tensile and compressive stress on the dust on the surface of the steel pipe. Subsequently, the transverse waves generate shear stress on the dust, causing fatigue damage to the dust, resulting in increased gaps and impairing the adhesion of the dust.

[0042] Tiny particles in a gas, especially fine particles smaller than 1 μm, vibrate under the influence of sound waves. Particles of different sizes, densities, or shapes respond to sound waves at different speeds, resulting in relative motion between particles. Simultaneously, secondary effects such as acoustic flow and radiation pressure caused by sound waves further intensify the particle attraction. Through frequent collisions, particles adhere together via van der Waals forces, electrostatic interactions, or liquid bridging forces, forming larger aggregates. This achieves a dust removal effect.

[0043] In this optional embodiment, the step of collecting dust samples from the surface of the target component and performing compositional testing, analyzing the compositional information of the dust samples, and obtaining the analysis results includes: Dust samples were collected from the surface of target components in the boiler piping. The particle size, diameter, density, and material composition of the collected dust samples were analyzed to obtain the analysis results.

[0044] Specifically, after the boiler piping system is shut down or enters a safe maintenance state, workers use clean sampling tools to collect approximately 20-50 grams of ash sample from the surface of the target component. After loosening the ash with a portable vibrator, a handheld laser particle size analyzer measures key data within 30 seconds, instantly quantifying the ash coarseness and directly guiding the selection of ultrasonic frequency. When fine particles predominate, a high-frequency vibrator above 30kHz should be prioritized. A portable X-ray fluorescence analyzer is used to detect the elemental composition within 60 seconds, quickly determining the chemical properties of the ash. A simple densitometer is used to measure the bulk density, assessing the compactness of the ash layer to reference the set power. Lower density combined with high hardness indicates the need for medium to high power. The above data is input into the system, and a fast matching algorithm outputs optimized parameters within 5 seconds, achieving a minute-level response from detection to strategy adjustment. This allows the system to quickly adapt to changes in fuel or operating conditions, improving the real-time performance and economy of ash removal.

[0045] In this optional embodiment, the initial matching of the pre-configured frequency matching table with the dust removal parameter combination adapted to the analysis results, and the output dust removal control parameters include: A frequency matching table was constructed by combining engineering physics principles with historical dust removal experimental data; The analysis results are input into the frequency matching table for mapping processing, and the preliminary dust removal parameters are output. The initial dust removal parameters are automatically filtered and matched based on the built-in classification and mapping rules of the frequency matching table to generate dust removal control parameters.

[0046] Specifically, the system automatically filters and matches preliminary dust removal parameters based on the classification and mapping rules built into the frequency matching table. The filtering process ensures that the final selected combination of dust removal parameters maximizes the dust removal requirements, taking into account the specific requirements of the application, such as dust removal intensity and vibration mode. For example, when the dust is large and dense, a stronger vibration and a higher frequency combination may be needed, while for lighter or newer dust, a lower frequency and shorter vibration duration can be used. Through this automated matching and filtering, the system generates final dust removal control parameters, which will be used to guide the subsequent dust removal process.

[0047] The frequency matching table is generated by combining engineering physics principles with historical dust removal experimental data, creating a table containing combinations of dust removal parameters. In this table, each dust type, such as light, relatively light, medium, large, and large, corresponds to a recommended oscillator frequency, vibration time, vibration frequency, and other dust removal parameters. After obtaining the dust composition analysis results of the target component, these results are used as input to the frequency matching table for mapping processing. After matching, preliminary dust removal parameters are obtained, including oscillator frequency, vibration time, and vibration frequency, as shown in Tables 1, 2, 3, and 4.

[0048] Table 1 Selection Table for Dust and Oscillator Frequency ; Table 2 Selection Table for Dust and Vibration Time ; Table 3 Selection Table for Dust and Vibration Time ; Table 4 Selection Table for Dust and Vibration Modes ; like Figure 4 As shown, the left side shows the superposition of two sinusoidal signal waveforms with similar frequencies but different phases, while the right side shows the superposition of two sinusoidal signal waveforms with different frequencies. It is evident that superimposing signals of different frequencies achieves a more effective resonance effect.

[0049] In this optional embodiment, the step of testing the acoustic energy field distribution on the surface of the target component by exciting acoustic energy through the excitation point based on the dust removal control parameters, and plotting the energy field and other energy distribution curves, includes: Select a test point and install a single acoustic oscillator as the excitation point; Ultrasonic waves are excited by the excitation point, and the surface of the target component is scanned in a grid pattern using an acoustic wave detector (such as the DD802 model) to test the acoustic energy field, and the acoustic energy value (dB) at each measurement point is measured and recorded. Based on the acoustic energy values ​​at each measurement point, energy distribution curves such as the energy field on the surface of the target component are plotted.

[0050] Specifically, based on the dust removal control parameters, acoustic energy is excited through an excitation point, and the acoustic energy field distribution on the surface of the target component is tested. A suitable test point is selected, and a single acoustic transducer is installed at the selected excitation point as an energy source. When selecting the excitation point, the geometry and material properties of the target component need to be considered, because different locations may have different effects on the propagation and distribution of energy. After installing the transducer, ultrasonic waves are excited by the transducer, causing the acoustic energy to propagate on the surface of the target component, and the surface of the component is scanned in a grid pattern using a DD802 acoustic wave detector.

[0051] At this point, the acoustic wave detector records the acoustic wave energy value (in dB) at multiple measurement points. The acoustic energy value at each measurement point reflects the intensity of the ultrasonic wave energy received at that point. Through these measurement data, a detailed acoustic wave energy field distribution map can be obtained, providing a scientific basis for dust removal work. During measurement, the spacing of the gridded measurement points should be appropriately selected based on factors such as the surface morphology and material of the component to ensure coverage of the entire target component surface. The acoustic energy values ​​of all measurement points will be recorded, and a complete energy field and energy distribution curve will be plotted.

[0052] In this optional embodiment, the step of determining the optimal number, frequency, layout position, and excitation sequence of acoustic oscillators that meet the dust removal energy threshold based on energy field and other energy distribution curves, and outputting a dust removal deployment scheme, includes: Based on the energy distribution curves such as the energy field, determine the energy threshold required for dust removal (e.g., 70dB). By analyzing the relationship between energy distribution curves such as energy fields and energy thresholds, the optimal number, frequency, layout position, and excitation sequence of acoustic oscillators required to meet the energy requirements of full-field dust removal are calculated, thus forming an executable dust removal deployment scheme.

[0053] Specifically, based on these acoustic energy values, an energy field diagram of the component surface can be drawn, and the energy value at each measurement point can be determined. Based on the energy field diagram, the 70dB energy line can be found, representing the minimum energy value required to achieve dust removal. By analyzing this energy distribution, the number and arrangement of oscillators can be rationally selected to ensure that all parts of each component reach or exceed 70dB, thereby guaranteeing comprehensive dust removal effectiveness. Figure 5 The figure shown is an approximate distribution of energy transfer in a simulated component based on a test result of an oscillator of a certain frequency.

[0054] In this optional embodiment, the step of driving the acoustic oscillator in the dust removal device to excite ultrasonic waves containing longitudinal and transverse waves according to the set dust removal deployment scheme, and performing ultrasonic cleaning on the interface between the target component surface and dust to achieve dust removal includes: According to the dust removal deployment plan, the logic control mechanism sends control commands to the excitation mechanism. The excitation mechanism controls the designated acoustic oscillator to simultaneously vibrate longitudinal wave signals and transverse wave signals, forming high-frequency alternating stress. Under the continuous action of high-frequency alternating stress, microcracks begin to form inside the dust layer on the surface of the target component. According to the theory of material fatigue strength, the fatigue strength of dust is much lower than its static compressive or shear strength. At stress levels far below those required for the static compressive or shear strength of dust, after a sufficient number of stress cycles (rapidly reaching the fatigue life in the SN curve), cracks within the dust layer can initiate, propagate, and interconnect. When the structural fatigue damage inside the dust layer of the target component accumulates to a critical point, the adhesion between the dust and the component substrate is completely destroyed, and the dust peels off and falls off under the action of gravity or airflow, achieving efficient cleaning. The entire dust removal process is carried out in the ultrasonic frequency band, and through structural vibration isolation design, it is ensured to be a noiseless operation.

[0055] Specifically, the dust removal device needs to achieve the following functions: 1) Excite acoustic signals. The acoustic signals selected include both longitudinal waves that generate tensile and compressive stress, and transverse waves that generate shear stress. In fact, through actual measurement, surface waves with a certain amount of energy are also included. Because surface waves have greater energy, their dust removal effect is more obvious. 2) Adjust the excitation power. Adjusting the excitation power can control the excitation energy. The controller's control range is 0-100%. 3) Control the excitation sequence and order of the excitation oscillators. For example, in a dust removal system with four oscillators F1, F2, F3, and F4, the excitation time of each of the four oscillators can be controlled, as can the excitation order of the four oscillators. It can also be selected whether to excite one oscillator or N oscillators simultaneously.

[0056] For example, it can be controlled to activate for 15 seconds with F1, then 15 seconds with F2, then 15 seconds with F3, then 15 seconds with F4, and finally 15 seconds with F4. It can also be controlled to activate F1 and F2 for 15 seconds each, then activate F3 and F4 simultaneously for 15 seconds each, and other control methods exist. The dust removal device, as shown in the figure, consists of the following parts: the excitation mechanism mainly excites the acoustic signal and also adjusts the excitation power; the logic control mechanism mainly controls the order of excitation of the oscillators; the acoustic oscillator consists of a piezoelectric transducer, an amplitude transformer, and an impact connection assembly.

[0057] Stress distribution diagram, such as Figure 9 , Figure 10 and Figure 11 As shown, the force acting on the surface of a micro-element (i.e., a point mass) in a solid is usually neither perpendicular nor parallel to this surface, but rather has two components: perpendicular and parallel. That is, it includes both normal stress and shear stress. If we study the force acting on a micro-element in a perpendicular coordinate system, we can construct three planes perpendicular to the three coordinate systems through a point P, such as... Figure 5 As shown.

[0058] Nine stress components can be used The forces acting on the three faces are represented by these nine stress components. The first letter in the lower right corner of each stress component indicates the direction of the force, and the second letter indicates the direction perpendicular to the normal to the face. These nine stress components can also fully represent the forces acting on this cube, as can be obtained from the force equilibrium condition: .

[0059] Therefore, only 6 out of the 9 stress components are independent, which is called... Normal stress is called This is the shear stress.

[0060] As mentioned above, the stress and deformation at any point in a solid can be represented by six stress components and six strain components. Therefore, within the linear elastic range, each of the six stress components at any point in a solid is a linear function of the six strain components, i.e.: ; In the formula, the footnotes X, Y, and Z represent the X-axis, Y-axis, and Z-axis of the cube; the footnotes 1, 2, 3, 4, 5, and 6 represent the direction of the force and the direction of the perpendicular normal to the face, respectively. Indicates stress; Represents the elastic constant; This represents the area (m²) of an elastic tetrahedron under compression. 2 ).

[0061] Tensile strength, compressive strength, shear strength, and fatigue strength are all important physical properties of dust. Dust can be considered a special type of brittle material. This brittle material is characterized by numerous cracks. Generally, brittle materials are considered to have the following physical properties: tensile strength is 0.1-0.15 times that of compressive strength; shear strength is 0.3-0.4 times that of compressive strength.

[0062] Fatigue strength is the ability of a material to resist fatigue failure under alternating stress. Generally speaking, the higher the number of cycles, the lower the fatigue strength. It is commonly used in engineering. The number of cycles is used as the measurement benchmark. As long as the actual number of cycles exceeds... If it doesn't break down, it's assumed to have an unlimited lifespan. However, it's generally believed that ultrasonic waves, such as those at 20kHz, can withstand vibrations of a certain frequency. Each test takes 350 seconds. Fatigue strength has the following characteristics compared to static strength: 1. Fatigue strength / static strength ratio: extremely low, 0.2-0.4, or even lower; 2. Crack initiation: extremely easy, the primary defect is the crack; 3. Crack propagation threshold: very low, not significant. like Figure 12 As shown, this is the fatigue strength curve of concrete. The formula for the SN curve of concrete is usually expressed as: ; In the formula, Indicates the maximum stress; Indicates static compressive strength; Indicates the stress ratio; Indicates the material constant; This represents the cycle coefficient. See the SN curve.

[0063] In this optional embodiment, the excitation mechanism controls a designated acoustic oscillator to simultaneously vibrate and generate longitudinal and transverse wave signals, forming a high-frequency alternating stress including: The acoustic oscillator is composed of a piezoelectric transducer, an amplitude transformer, and a vibration connection assembly. The excitation mechanism controls the piezoelectric transducer built into the designated acoustic oscillator to generate high-frequency mechanical vibration under the drive of a high-voltage electrical signal. The generated high-frequency mechanical vibration contains both longitudinal wave and transverse wave signals. After the longitudinal and transverse wave signals are amplified by the amplitude transformer, they are transmitted to the surface of the target component through the vibration connection assembly, forming high-frequency alternating stress.

[0064] Specifically, the acoustic oscillator consists of a piezoelectric transducer, an amplitude transformer, and an impact connection assembly. There are typically multiple oscillators, primarily used to generate acoustic signals. The oscillator of this invention simultaneously generates both longitudinal and transverse wave signals. The frequency is between 5kHz and 100kHz. It is a piezoelectric ceramic Langevin-type sandwich transducer.

[0065] like Figure 7 and Figure 8 As shown, the piezoelectric ceramic sheet generated by longitudinal waves uses a thickness vibration mode. The piezoelectric ceramic sheet generated by transverse waves uses a different mode. Generally, piezoelectric transducers operate in a vibrating state, without sufficient time for heat exchange. The static piezoelectric equation for the longitudinal vibration of a piezoelectric crystal under adiabatic conditions is calculated as follows: ; In the formula, Indicate strain ; Represents the constant of elastic force under a fixed electric field. ; Indicates stress ; The piezoelectric constant representing the applied electric field ; Indicates electric field strength ; Indicates electric displacement ; This represents the dielectric constant (N / m) under constant stress.

[0066] The function of the amplitude transformer is to increase the amplitude of vibration. The amplitude transformer can increase the amplitude by 2-10 times.

[0067] The function of the vibration connection component is to ensure that the energy generated by the acoustic oscillator is transmitted to the component being cleaned to the maximum extent.

[0068] like Figure 6 As shown, the interface of the dust removal device is PC104, which is the main operating interface of the dust removal device.

[0069] In this optional embodiment, the longitudinal wave signal and the transverse wave signal are amplified by the amplitude transformer, and then the amplified longitudinal wave signal and the transverse wave signal are transmitted to the surface of the target component through the vibration connection assembly, forming a high-frequency alternating stress including: According to solid acoustics theory, the propagation speed of longitudinal wave signals is determined by the elastic modulus and density of the material. Because the longitudinal wave signal is faster, it first acts on the interface between the dust and the target component, applying periodic tensile and compressive stress to the dust. The transverse wave signal then arrives, and the propagation speed of the transverse wave depends on the shear modulus and density of the material, applying periodic shear stress to the same interface; Tensile and compressive stresses and shear stresses superimpose each other in time and space, together forming high-frequency alternating stress.

[0070] Specifically, the tensile and compressive effects generated by the longitudinal waves and the shearing effects generated by the transverse waves in the ultrasonic cleaning process are repeatedly applied to the high-frequency probe, resulting in fatigue failure. The formulas for the longitudinal and transverse wave velocities are provided.

[0071] like Figure 7 The diagram shows the longitudinal wave velocity and propagation schematic. The formula for calculating the longitudinal wave velocity is: ; In the formula, Longitudinal wave velocity (m / s) for engineering testing express; Represents the elastic modulus (MPa) a ); Poisson's ratio (dimensionless, and) represents the ratio of a given number of units of mass to ... ); Density (Kg / m³) 3 The propagation of longitudinal waves is related to the elastic modulus.

[0072] like Figure 9 The diagram shows the sound velocity of a transverse wave and a schematic diagram of transverse wave propagation. The formula for calculating the transverse wave velocity is: ; In the formula, Shear wave velocity (m / s) for engineering testing express; Shear modulus (MP) a ); Represents the elastic modulus (MPa) a ); Poisson's ratio (dimensionless, and) represents the ratio of a given number of units of mass to ... ); Density (Kg / m³) 3 The propagation of transverse waves is related to the shear modulus.

[0073] In this optional embodiment, the step of evaluating the dust removal effect through integrated sensors and optimizing the frequency matching table and dust removal deployment scheme based on the evaluation results includes: The cleaning process and the state of the target component after cleaning are monitored by integrating sensors (such as differential pressure sensors, thermal imagers, and acoustic emission sensors). The cleaning effect coefficient is extracted from the monitoring data and compared with the preset cleaning effect reference range. If the dust removal effect coefficient is within the dust removal effect reference range, the dust removal effect is determined to meet the standard. There is no need to optimize the frequency matching table and dust removal deployment plan. Continue to run and monitor according to the current dust removal deployment plan. If the dust removal effect coefficient does not fall within the dust removal effect reference range, the dust removal effect deviation value is obtained. Based on the dust removal effect deviation value, the parameter mapping relationship in the frequency matching table and the dust removal deployment scheme are iteratively optimized. At the same time, a maintenance warning is issued according to the degree of deviation of the dust removal effect deviation value.

[0074] Specifically, integrated sensors are installed on one side of the dust removal device to monitor the dust removal process and the state of the target component after dust removal. These integrated sensors include differential pressure sensors, thermal imagers, and acoustic emission sensors. The sensors collect data in real time to evaluate the working status and effectiveness of different stages of the dust removal process. The differential pressure sensor monitors airflow changes during dust removal, the thermal imager detects surface temperature changes of the target component before and after dust removal, and the acoustic emission sensor detects vibrations and sound wave propagation on the component surface. Through the combination of these sensors, the dust removal process can be monitored comprehensively, and key dust removal effect data can be obtained.

[0075] By integrating data collected by sensors, a dust removal efficiency coefficient is extracted, reflecting the actual dust removal effect. This coefficient is calculated based on comprehensive indicators such as dust removal rate, surface energy change, and airflow change on the target component surface. The coefficient needs to be compared with a preset dust removal efficiency reference range to determine if the current effect meets requirements. If the coefficient falls within the reference range, the system determines that the dust removal effect is satisfactory. In this case, there is no need to optimize the frequency matching table or dust removal deployment plan; the system will continue to operate according to the current deployment plan and continue monitoring.

[0076] If the dust removal efficiency coefficient fails to reach the preset reference range, the system will determine that the dust removal efficiency is substandard and enter the optimization process. The system will obtain the deviation value of the dust removal efficiency, which represents the difference between the current dust removal efficiency and the expected efficiency. Based on this deviation value, the system will iteratively optimize the parameter mapping relationship in the frequency matching table. The optimization process may involve adjusting matching rules such as frequency, power, and timing. The system may adjust the sound wave frequency to adapt to more difficult-to-remove dust, increase or decrease the power output, or even adjust the dust removal timing and oscillator excitation sequence to achieve a better dust removal efficiency.

[0077] In addition to optimizing the frequency matching table, the dust removal deployment scheme (such as the number of vibrators, their layout, and the excitation sequence) may also be adjusted based on the deviation of the dust removal effect. If the deviation is large, it may be necessary to increase the number of vibrators or change their layout to make the dust removal coverage more uniform; if the deviation is small, the dust removal efficiency can be optimized by adjusting the excitation sequence or frequency. The optimized frequency matching table and dust removal deployment scheme will be reused in the dust removal process to ensure that the dust removal effect continues to meet the standards. At the same time, the system will also issue maintenance warnings based on the degree of deviation of the dust removal effect to prompt operators to perform system maintenance or adjustments in a timely manner.

[0078] Assume a boiler system's ash removal device is equipped with a differential pressure sensor, a thermal imager, and an acoustic emission sensor. The differential pressure sensor is installed at the boiler pipe inlet and outlet to monitor airflow changes. The thermal imager is installed outside the pipes to monitor the temperature difference on the component surfaces before and after ash removal. The acoustic emission sensor is installed on the pipe surface to monitor sound wave propagation and component vibration. The entire monitoring system acquires data every 5 minutes and transmits data to the central control system in real time.

[0079] At the start of the cleaning process, the dust layer thickness on the target component (such as the inner wall of a boiler pipe) is 0.5 mm, the surface temperature is 300°C, and the airflow velocity is 5 m / s. During the cleaning process, the system monitors the process in real time using sensors. The formula for calculating the cleaning effectiveness coefficient is: ; Assuming that the amount of dust removed after cleaning is 80g / m², and the amount of dust before cleaning is 100g / m², then the dust removal efficiency coefficient is: ; The system compares this dust removal effectiveness coefficient with a preset reference range, such as a coefficient between 75% and 85%. If the effectiveness coefficient is 80%, the dust removal effect is deemed satisfactory and no optimization is required. The system will continue to operate according to the current dust removal deployment plan, such as a 24kHz frequency, four oscillators, and a 30s excitation time per cycle, and will continue to monitor the process.

[0080] If the dust removal efficiency coefficient fails to reach the reference range, say below 75%, the system will calculate an efficiency deviation value, for example, 10%. The system will then optimize the frequency matching table and dust removal deployment plan based on this deviation value. For instance, if the system decides to improve coverage by increasing the number of vibrators from 4 to 6 and adjusting the excitation sequence to alternating excitation, the optimized dust removal parameters would include: Number of oscillators: 6; Oscillator frequency: 24kHz; Excitation time for each oscillator: 35s; Excitation sequence: Oscillators 1, 2, and 3 are excited first, then oscillators 4, 5, and 6 are excited in turn; Number of excitations per day: 12.

[0081] After the optimized solution was implemented, the dust removal efficiency coefficient increased to 90%, achieving the expected results. The system also issues maintenance warnings, prompting operators to check the status of the dust removal device, and continues to monitor it via sensors after each dust removal operation.

[0082] Figure 2 An embodiment of an intelligent noiseless ultrasonic cleaning device of the present invention is shown.

[0083] In this optional embodiment, the intelligent noiseless ultrasonic cleaning device includes: The system comprises a logic control mechanism 1, an excitation mechanism 2, several acoustic wave vibrators 3, an acoustic wave detector 4, an integrated sensor 5, and a power module 6; wherein the acoustic wave vibrator 3 is composed of a piezoelectric transducer 7, an amplitude transformer 8, and a vibration connection assembly 9. Dust samples from the surface of the target component are collected for physical property analysis to obtain particle size, density, and material information. The analysis results are then input into the logic control mechanism 1. The logic control mechanism 1 calls the frequency matching table to map and generate dust removal control parameters. The logic control mechanism 1 instructs the excitation mechanism 2 to drive a single test acoustic oscillator 3 to work, and coordinates with the acoustic wave detector 4 to scan the surface of the component and plot energy field and other energy distribution curves. Based on the energy field and other energy distribution curves and the dust removal energy threshold, the logic control mechanism 1 calculates the optimal number, layout position, and excitation sequence of the required acoustic oscillators 3 to form a dust removal deployment plan. When the dust removal deployment plan is executed, the excitation mechanism 2 outputs a specific electrical signal to each acoustic wave vibrator 3 in the array according to the dust removal deployment plan. The piezoelectric transducer 7 in each acoustic wave vibrator 3 converts the electrical signal into mechanical vibration. After being amplified by the amplitude transformer 8, it is coupled to the component through the vibration connection assembly 9, which excites a high-frequency alternating stress containing both longitudinal and transverse waves. This high-frequency alternating stress propagates in the component. The longitudinal and transverse waves apply high-frequency tensile and shear alternating stresses to the dust interface, causing the dust layer to peel off due to material fatigue. During and after the dust removal process, the integrated sensor 5 monitors the dust removal process and the status of the target component after dust removal in real time to evaluate the dust removal effect and feeds the data back to the logic control mechanism 1. Based on the deviation between the effect data and the preset target, the logic control mechanism 1 dynamically optimizes the mapping rules of the frequency matching table and the dust removal deployment plan through a self-learning algorithm. The power module 6 supplies power to the logic control mechanism 1, the excitation mechanism 2, several acoustic wave oscillators 3, the acoustic wave detector 4, and the integrated sensor 5 to realize an intelligent dust removal process from intelligent diagnosis, precise planning, adaptive execution to continuous optimization. The entire process operates in the ultrasonic frequency band and is combined with vibration isolation design to maintain noiseless operation.

[0084] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 3 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores static and dynamic information data. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements the steps in the above method embodiments.

[0085] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the computer device to which the present invention is applied. A specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0086] In addition, the present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0087] In addition, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0088] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0089] This invention is not limited to the structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this invention is limited only by the appended claims.

Claims

1. A smart, noiseless ultrasonic cleaning method, characterized in that, include: Collect dust samples from the surface of the target component and perform compositional testing. Analyze the compositional information of the dust samples to obtain the analysis results. The initial matching of the dust removal parameter combinations with the analysis results in the pre-configured frequency matching table is performed, and the dust removal control parameters are output. Based on the dust removal control parameters, the acoustic energy field distribution on the surface of the target component is tested by exciting acoustic energy at the excitation point, and the energy field and other energy distribution curves are plotted. Based on energy distribution curves such as energy fields, the optimal number, frequency, layout position and excitation sequence of acoustic oscillators that meet the dust removal energy threshold are determined, and a dust removal deployment scheme is output. According to the set dust removal deployment plan, the acoustic oscillator set in the dust removal device is driven to excite ultrasonic waves containing longitudinal and transverse waves to perform ultrasonic cleaning on the interface between the target component surface and dust, so as to remove the dust. The dust removal effect is evaluated by integrating sensors, and the frequency matching table and dust removal deployment scheme are optimized based on the evaluation results.

2. The intelligent noiseless ultrasonic cleaning method according to claim 1, characterized in that, The process involves collecting dust samples from the surface of the target component and performing compositional testing. The analysis results, including the analysis of the dust sample's compositional information, include: Dust samples were collected from the surface of target components in the boiler piping. The particle size, diameter, density, and material composition of the collected dust samples were analyzed to obtain the analysis results.

3. The intelligent noiseless ultrasonic cleaning method according to claim 1, characterized in that, The initial matching of the pre-configured frequency matching table with the dust removal parameter combinations based on the adaptation analysis results outputs dust removal control parameters including: A frequency matching table was constructed by combining engineering physics principles with historical dust removal experimental data; The analysis results are input into the frequency matching table for mapping processing, and the preliminary dust removal parameters are output. The initial dust removal parameters are automatically filtered and matched based on the built-in classification and mapping rules of the frequency matching table to generate dust removal control parameters.

4. The intelligent noiseless ultrasonic cleaning method according to claim 1, characterized in that, The method of testing the acoustic energy field distribution on the surface of the target component by exciting acoustic energy through vibration points based on dust removal control parameters, and plotting energy field and other energy distribution curves, includes: Select a test point and install a single acoustic oscillator as the excitation point; Ultrasonic waves are excited by the excitation point, and the surface of the target component is scanned in a grid pattern using an acoustic wave detector to test the acoustic energy field, and the acoustic energy value at each measurement point is measured and recorded. Based on the acoustic energy values ​​at each measurement point, energy distribution curves such as the energy field on the surface of the target component are plotted.

5. The intelligent noiseless ultrasonic cleaning method according to claim 1, characterized in that, Based on energy distribution curves such as energy fields, the optimal number, frequency, layout position, and excitation sequence of acoustic oscillators that meet the dust removal energy threshold are determined, and the dust removal deployment scheme is output, including: Based on the energy distribution curves such as the energy field, the energy threshold required for dust removal is determined; By analyzing the relationship between energy distribution curves such as energy fields and energy thresholds, the optimal number, frequency, layout position, and excitation sequence of acoustic oscillators required to meet the energy requirements of full-field dust removal are calculated, thus forming an executable dust removal deployment scheme.

6. The intelligent noiseless ultrasonic cleaning method according to claim 1, characterized in that, The step of driving the acoustic oscillator in the dust removal device to excite ultrasonic waves containing longitudinal and transverse waves according to the set dust removal deployment plan, and performing ultrasonic cleaning on the interface between the target component surface and dust to achieve dust removal includes: According to the dust removal deployment plan, the logic control mechanism sends control commands to the excitation mechanism. The excitation mechanism controls the designated acoustic oscillator to simultaneously vibrate longitudinal wave signals and transverse wave signals, forming high-frequency alternating stress. Under the continuous action of high-frequency alternating stress, microcracks begin to form inside the dust layer on the surface of the target component. According to the theory of material fatigue strength, the fatigue strength of dust is much lower than its static compressive or shear strength. At stress levels far below those required for the static compressive or shear strength of dust, after a sufficient number of stress cycles, cracks within the dust layer can initiate, propagate, and interconnect. When the structural fatigue damage inside the dust layer of the target component accumulates to a critical point, the adhesion between the dust and the component substrate is completely destroyed, and the dust peels off and falls off under the action of gravity or airflow, achieving efficient cleaning. The entire dust removal process is carried out in the ultrasonic frequency band, and through structural vibration isolation design, it is ensured to be a noiseless operation.

7. The intelligent noiseless ultrasonic cleaning method according to claim 6, characterized in that, The excitation mechanism controls a designated acoustic oscillator to simultaneously vibrate and generate longitudinal and transverse wave signals, forming a high-frequency alternating stress including: The acoustic oscillator is composed of a piezoelectric transducer, an amplitude transformer, and a vibration connection assembly. The excitation mechanism controls the piezoelectric transducer built into the designated acoustic oscillator to generate high-frequency mechanical vibration under the drive of a high-voltage electrical signal. The generated high-frequency mechanical vibration contains both longitudinal wave and transverse wave signals. After the longitudinal and transverse wave signals are amplified by the amplitude transformer, they are transmitted to the surface of the target component through the vibration connection assembly, forming high-frequency alternating stress.

8. The intelligent noiseless ultrasonic cleaning method according to claim 7, characterized in that, The longitudinal and transverse wave signals are amplified by the amplitude transformer and then transmitted to the surface of the target component through the vibration connection assembly, forming a high-frequency alternating stress including: According to solid acoustics theory, the propagation speed of longitudinal wave signals is determined by the elastic modulus and density of the material. Because the longitudinal wave signal is faster, it first acts on the interface between the dust and the target component, applying periodic tensile and compressive stress to the dust. The transverse wave signal then arrives, and the propagation speed of the transverse wave depends on the shear modulus and density of the material, applying periodic shear stress to the same interface; Tensile and compressive stresses and shear stresses superimpose each other in time and space, together forming high-frequency alternating stress.

9. The intelligent noiseless ultrasonic cleaning method according to claim 1, characterized in that, The process of evaluating the dust removal effect through integrated sensors and optimizing the frequency matching table and dust removal deployment scheme based on the evaluation results includes: By integrating sensors to monitor the cleaning process and the state of the target component after cleaning, the cleaning effect coefficient is extracted from the monitoring data and compared with the preset cleaning effect reference range. If the dust removal effect coefficient is within the dust removal effect reference range, the dust removal effect is determined to meet the standard. There is no need to optimize the frequency matching table and dust removal deployment plan. Continue to run and monitor according to the current dust removal deployment plan. If the dust removal effect coefficient does not fall within the dust removal effect reference range, the dust removal effect deviation value is obtained. Based on the dust removal effect deviation value, the parameter mapping relationship in the frequency matching table and the dust removal deployment scheme are iteratively optimized. At the same time, a maintenance warning is issued according to the degree of deviation of the dust removal effect deviation value.

10. A smart, noiseless ultrasonic cleaning device, characterized in that, include: The system comprises a logic control mechanism (1), an excitation mechanism (2), several acoustic wave oscillators (3), an acoustic wave detector (4), an integrated sensor (5), and a power supply module (6); wherein the acoustic wave oscillator (3) is composed of a piezoelectric transducer (7), an amplitude transformer (8), and a vibration connection assembly (9); Dust samples from the surface of the target component are collected for physical property analysis. The particle size, density and material information of the dust samples are obtained, and the analysis results are input to the logic control mechanism (1). The logic control mechanism (1) calls the frequency matching table and maps and generates the dust removal control parameters. The logic control mechanism (1) instructs the excitation mechanism (2) to drive a single test acoustic wave vibrator (3) to work, and coordinates with the acoustic wave detector (4) to scan the surface of the component and draw energy field and other energy distribution curves. Based on the energy field and other energy distribution curves and the dust removal energy threshold, the logic control mechanism (1) calculates the optimal number, layout position and excitation sequence of the required acoustic wave vibrators (3) to form a dust removal deployment scheme. When the dust removal deployment plan is executed, the excitation mechanism (2) outputs a specific electrical signal to each acoustic wave vibrator (3) in the array according to the dust removal deployment plan. The piezoelectric transducer (7) in each acoustic wave vibrator (3) converts the electrical signal into mechanical vibration. After being amplified by the amplitude transformer (8), it is coupled to the component through the shock connection assembly (9) to excite a high-frequency alternating stress containing both longitudinal and transverse waves. The high-frequency alternating stress propagates in the component. The longitudinal and transverse waves apply high-frequency tensile and shear alternating stresses to the dust bonding interface, causing the dust layer to peel off due to material fatigue. During and after the cleaning process, the integrated sensor (5) monitors the cleaning process and the status of the target component after cleaning in real time to evaluate the cleaning effect and feeds the data back to the logic control mechanism (1). Based on the deviation between the effect data and the preset target, the logic control mechanism (1) dynamically optimizes the mapping rules of the frequency matching table and the cleaning deployment scheme through a self-learning algorithm. The power module (6) supplies power to the logic control mechanism (1), the excitation mechanism (2), several acoustic wave oscillators (3), the acoustic wave detector (4), and the integrated sensor (5) to realize an intelligent cleaning process from intelligent diagnosis, precise planning, adaptive execution to continuous optimization. The entire process operates in the ultrasonic frequency band and is combined with vibration isolation design to maintain noiseless operation.

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