Laser cleaning method for marine microorganisms on surface of metal equipment

Abstract

The invention discloses a laser cleaning method for marine microorganisms on the surface of metal equipment and belongs to the technical field of laser processing. The problems that an existing laser cleaning method cannot automatically adjust cleaning parameters according to a microbial layer, and the cleaning quality is affected are solved. Comprising the following steps: randomly setting cleaning parameters of a laser, acquiring a sound signal and a spectral signal of laser cleaning in a cleaning process, processing, and calculating to obtain a sound signal characteristic quantity and a spectral signal characteristic quantity; fusing the acoustic signal characteristic quantity and the spectral signal characteristic quantity, and constructing a calculation formula of a cleaning quality comprehensive index Q; calculating an optimal acoustic signal characteristic quantity and an optimal spectral signal characteristic quantity corresponding to the maximum cleaning quality comprehensive index Q value, and further obtaining an optimal cleaning parameter of the laser; and carrying out laser cleaning on the marine microorganisms based on the optimal cleaning parameters. The method is used for high-quality cleaning of marine microorganisms on the surface of metal equipment.

Description

Technical Field

[0001] This invention relates to a laser cleaning method for marine microorganisms on the surface of metal equipment, belonging to the field of laser processing technology. Background Technology

[0002] With the development of marine engineering, shipbuilding, and other fields, metal equipment operates in seawater environments for extended periods, making its surfaces highly susceptible to contamination by marine microorganisms (algae, barnacles, etc.). This biofouling leads to a series of engineering problems, including accelerated corrosion, reduced heat transfer efficiency, and decreased material fatigue strength, severely impacting service life and operational safety. Conventional cleaning methods, such as high-pressure water jetting and chemical treatments, suffer from problems such as substrate corrosion, environmental pollution, or low efficiency. Laser cleaning, with its high precision, offers unique advantages in protecting materials from marine microorganisms and in marine engineering operations and maintenance.

[0003] In practical applications of laser cleaning, the quality and effectiveness of cleaning typically rely on the operator's experience or post-processing inspection methods (such as visual surface inspection, scanning electron microscopy, and energy dispersive spectroscopy). These traditional methods are not only inefficient and highly subjective, but also difficult to monitor and automatically adjust the cleaning process in real time, limiting the further development of laser cleaning technology in high-standard industrial settings. Existing monitoring research on the cleaning process mainly focuses on the cleaning of paint layers and metal oxides. However, when dealing with unevenly distributed marine microorganisms, the signals are more complex and diverse, making existing monitoring methods difficult to apply.

[0004] Existing studies have focused on monitoring cleaning targets such as paint layers and metal oxides. Cleaning monitoring methods typically employ only a single sensor, resulting in low stability and accuracy, and making them unsuitable for various cleaning environments.

[0005] Therefore, there is an urgent need for a method to adaptively adjust cleaning parameters to achieve high-quality cleaning of metal surfaces in response to the uneven growth of microbial layers on the metal surface. Summary of the Invention

[0006] To address the problem that existing laser cleaning methods cannot automatically adjust cleaning parameters based on the microbial layer, thus affecting cleaning quality, this invention provides a laser cleaning method for marine microorganisms on the surface of metal equipment.

[0007] The present invention provides a laser cleaning method for marine microorganisms on the surface of metal equipment, comprising:

[0008] First, the cleaning parameters of the laser are randomly set. During the cleaning process, the acoustic and spectral signals of the laser cleaning are collected and processed to calculate the acoustic signal characteristic quantity and the spectral signal characteristic quantity. The acoustic signal characteristic quantity is calculated based on the time-domain average amplitude of the acoustic signal, the average energy of the acoustic signal, and the standard deviation of the acoustic signal energy. The spectral signal characteristic quantity is calculated based on the plasma spectral line intensity of the substrate characteristic elements and the plasma spectral line intensity of the contamination layer characteristic elements.

[0009] The acoustic signal characteristics and spectral signal characteristics are fused to construct a formula for calculating the comprehensive cleaning quality index Q;

[0010] The optimal acoustic signal characteristic and the optimal spectral signal characteristic corresponding to the maximum cleaning quality comprehensive index Q value are calculated, and then the optimal cleaning parameters of the laser are obtained; the laser cleaning of marine microorganisms is carried out based on the optimal cleaning parameters.

[0011] The laser cleaning method for marine microorganisms on the surface of metal equipment according to the present invention includes a method for obtaining the time-domain average amplitude of the acoustic signal, the mean energy of the acoustic signal, and the standard deviation of the acoustic signal energy, comprising:

[0012] Perform a Fourier transform on the acoustic signal to obtain the time-domain average amplitude, the mean energy of the acoustic signal, and the standard deviation of the acoustic signal energy.

[0013] The laser cleaning method for marine microorganisms on the surface of metal equipment according to the present invention includes a method for obtaining the plasma spectral intensity of characteristic elements of the substrate and the plasma spectral intensity of characteristic elements of the contaminant layer, comprising:

[0014] The baseline correction of the spectral signal is performed using an adaptive iterative weighted penalized least squares method, and the plasma spectral intensity of the substrate characteristic elements and the contamination layer characteristic elements are extracted.

[0015] According to the laser cleaning method for marine microorganisms on the surface of metal equipment of the present invention, the acoustic signal characteristic quantities are composed of the normalized time-domain average amplitude of the acoustic signal and the normalized acoustic energy kurtosis fluctuation index. The fusion is obtained;

[0016] The normalized time-domain average amplitude of the acoustic signal is obtained by normalizing the time-domain average amplitude of the acoustic signal.

[0017] Acoustic energy kurtosis fluctuation index for:

[0018] (1),

[0019] In the formula, K is the global kurtosis factor, and CV E The energy fluctuation coefficient;

[0020] (2),

[0021] In the formula The number of points in the discrete acoustic signal sequence. Let n be the time-domain amplitude of the discrete acoustic signal. The time-domain average amplitude of the acoustic signal;

[0022] (3),

[0023] In the formula The average energy of the sound signal. The standard deviation of the acoustic signal energy;

[0024] Acoustic energy kurtosis fluctuation index Normalization yields the normalized acoustic energy kurtosis fluctuation index. .

[0025] The laser cleaning method for marine microorganisms on the surface of metal equipment according to the present invention is expressed as follows: :

[0026] ,

[0027] In the formula This is the amplitude weighting coefficient for the acoustic signal. The normalized time-domain average amplitude of the acoustic signal. For acoustic wave weighting coefficients, The normalized acoustic energy kurtosis fluctuation index .

[0028] According to the laser cleaning method for marine microorganisms on the surface of metal equipment of the present invention, the spectral signal characteristic quantity is obtained by fusing the normalized plasma spectral line intensity of the substrate characteristic elements and the normalized plasma spectral line intensity of the contaminant layer characteristic elements.

[0029] The laser cleaning method for marine microorganisms on the surface of metal equipment according to the present invention expresses the spectral signal characteristic quantities as follows: :

[0030] ,

[0031] In the formula The substrate spectral line intensity weighting coefficient. The normalized plasma spectral line intensities of the substrate characteristic elements are obtained by normalizing the plasma spectral line intensities of the substrate characteristic elements. The weighting coefficient for the intensity of the contamination layer spectral lines. The normalized plasma spectral line intensities of characteristic elements in the contamination layer are obtained by normalizing the plasma spectral line intensities of characteristic elements in the contamination layer.

[0032] The laser cleaning method for marine microorganisms on the surface of metal equipment according to the present invention, and the acoustic signal characteristic quantities and spectral signal characteristics By integrating the data, a comprehensive cleaning quality index Q is constructed as follows:

[0033] (4),

[0034] In the formula For acoustic signal weighting coefficients, These are the weighting coefficients for the spectral signal.

[0035] According to the laser cleaning method for marine microorganisms on the surface of metal equipment of the present invention, the laser beam emitted by the laser with randomly set cleaning parameters is oscillated at high speed by the scanning galvanometer and reciprocates on the surface of the metal equipment at a set speed; the acoustic signal of the laser cleaning is collected by an acoustic signal sensor, which is a microphone.

[0036] According to the laser cleaning method for marine microorganisms on the surface of metal equipment of the present invention, the spectral signal of laser cleaning is acquired by a spectral fiber optic probe and calibrated by a spectrometer.

[0037] The beneficial effects of this invention are as follows: This invention's method achieves high-quality cleaning of marine microorganisms on metal surfaces based on the synergistic feedback of acoustic and optical signals, and realizes quality control of the cleaning process through optimization of laser cleaning parameters. It utilizes acoustic and spectroscopic signals as basic data to optimize laser cleaning parameters, enabling non-destructive laser removal of marine microorganisms. This invention combines laser processing technology with non-destructive testing and online monitoring technology.

[0038] During the implementation of the method of the present invention, the cleaning effect can be comprehensively evaluated by a series of characteristic values ​​of acoustic and spectral signals.

[0039] This invention utilizes combined acoustic and spectral monitoring to monitor both the physical stripping process and chemical state changes, enabling comprehensive process sensing and monitoring with more complete information. Compared to single-signal monitoring, the combined monitoring of two signal characteristics improves process monitoring accuracy and reduces the false judgment rate. This invention is applicable to complex cleaning environments, avoiding interference from smoke, dust, water mist, and other environmental factors. Even when one signal is interfered with, the other signal can still be monitored and identified. Furthermore, this invention targets unevenly growing microbial layers, using weighting coefficients of removal mechanism control indicators to more accurately determine cleaning quality and achieve high-quality cleaning. Attached Figure Description

[0040] Figure 1 This is a schematic diagram of the signal acquisition device for the laser cleaning method of marine microorganisms on the surface of metal equipment described in this invention;

[0041] Figure 2 This is a schematic diagram of the removal thickness of the microbial layer by quantifying the time-domain average amplitude of the acoustic signal in a specific embodiment of the present invention;

[0042] Figure 3 The acoustic energy kurtosis fluctuation index in a specific embodiment Schematic diagram of surface roughness after quantitative cleaning;

[0043] Figure 4 This is a schematic diagram illustrating the qualitative determination of the cleaning status using the AlⅠⅠ spectral lines in a specific embodiment;

[0044] Figure 5 This is a macroscopic morphology image of a metal surface after cleaning, under conditions where hard attachments to marine microorganisms account for a relatively high proportion.

[0045] Figure 6 This is a macroscopic morphology image of a metal surface after cleaning, where the EPS layer accounts for a relatively large proportion of marine microorganisms. Detailed Implementation

[0046] 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.

[0047] Specific Implementation Method 1: Combination Figure 1 As shown, the present invention provides a laser cleaning method for marine microorganisms on the surface of metal equipment, comprising:

[0048] First, the cleaning parameters of the laser are randomly set. During the cleaning process, the acoustic and spectral signals of the laser cleaning are collected and processed to calculate the acoustic signal characteristic quantity and the spectral signal characteristic quantity. The acoustic signal characteristic quantity is calculated based on the time-domain average amplitude of the acoustic signal, the average energy of the acoustic signal, and the standard deviation of the acoustic signal energy. The spectral signal characteristic quantity is calculated based on the plasma spectral line intensity of the substrate characteristic elements and the plasma spectral line intensity of the contamination layer characteristic elements.

[0049] The acoustic signal characteristics and spectral signal characteristics are fused to construct a formula for calculating the comprehensive cleaning quality index Q;

[0050] The optimal acoustic signal characteristic and the optimal spectral signal characteristic corresponding to the maximum cleaning quality comprehensive index Q value are calculated, and then the optimal cleaning parameters of the laser are obtained; the laser cleaning of marine microorganisms is carried out based on the optimal cleaning parameters.

[0051] The metal surface in this embodiment includes an aluminum alloy surface.

[0052] When laser light is applied to microorganisms on a metal surface, a violent thermal effect occurs, accompanied by significant acoustic emission and plasma radiation phenomena. When atoms and ions in the plasma transition from the excited state back to the ground state, they emit atomic and ionic spectral lines. The plasma expansion process and the shock waves released when the laser breaks through the air attenuate into sound waves as they propagate over long distances, carrying a wealth of information about the material removal process. This implementation method achieves real-time identification and intelligent feedback control of the microbial removal status and changes in the cleaning process by acquiring and processing acoustic and spectral signals in real time.

[0053] When lasers act on different material surfaces, plasma spectroscopy produces different plasma lines. By analyzing the different types and intensities of these lines, changes in the elemental composition of the cleaned surface can be determined, thus analyzing changes in the surface material layer. Different laser energies result in different time-frequency domain acoustic signals when applied to material surfaces. By extracting different characteristic quantities from these acoustic signals, changes in the amount of microbial layer removed and the smoothness of the surface can be determined. Both methods monitor the surface cleaning status from two perspectives: changes in physical properties (qualitative) and the amount of contaminants removed (quantitative), allowing for a comprehensive analysis of the quality of laser-cleaned marine microbial layers.

[0054] This implementation method is based on the collaborative monitoring of acoustic-spectral signals to monitor the quality of laser cleaning of marine microbial layers on metal surfaces. It can adaptively adjust the comprehensive evaluation structure model for unevenly growing microbial layers on metal surfaces and regulate the contribution of different signals.

[0055] Furthermore, methods for obtaining the time-domain average amplitude, mean energy, and standard deviation of the acoustic signal include:

[0056] Perform a Fourier transform on the acoustic signal to obtain the time-domain average amplitude, the mean energy of the acoustic signal, and the standard deviation of the acoustic signal energy.

[0057] Methods for obtaining the plasma spectral intensity of characteristic elements in the substrate and the plasma spectral intensity of characteristic elements in the contamination layer include:

[0058] The spectral signal was baseline corrected using the adaptive iterative weighted penalized least squares method (airPLS) to extract the plasma spectral line intensities of substrate characteristic elements and contamination layer characteristic elements.

[0059] Furthermore, the acoustic signal characteristics are composed of the normalized time-domain average amplitude of the acoustic signal and the normalized acoustic energy kurtosis fluctuation index. The fusion is obtained;

[0060] The normalized time-domain average amplitude of the acoustic signal is obtained by normalizing the time-domain average amplitude of the acoustic signal.

[0061] Acoustic energy kurtosis fluctuation index for:

[0062] (1),

[0063] In the formula, K is the global kurtosis factor, and CV E The energy fluctuation coefficient;

[0064] (2),

[0065] In the formula The number of points in the discrete acoustic signal sequence. Let n be the time-domain amplitude of the discrete acoustic signal. The time-domain average amplitude of the acoustic signal;

[0066] (3),

[0067] In the formula The average energy of the sound signal. The standard deviation of the acoustic signal energy;

[0068] Acoustic energy kurtosis fluctuation index Normalization yields the normalized acoustic energy kurtosis fluctuation index. .

[0069] The characteristic quantities of the acoustic signal are represented as :

[0070] ,

[0071] In the formula This is the amplitude weighting coefficient for the acoustic signal. The normalized time-domain average amplitude of the acoustic signal. For acoustic wave weighting coefficients, The normalized acoustic energy kurtosis fluctuation index .

[0072] In this embodiment, the spectral signal characteristics are obtained by fusing the normalized plasma spectral line intensities of the substrate characteristic elements and the normalized plasma spectral line intensities of the contamination layer characteristic elements.

[0073] The spectral signal characteristics are expressed as :

[0074] ,

[0075] In the formula The substrate spectral line intensity weighting coefficient. The normalized plasma spectral line intensities of the substrate characteristic elements are obtained by normalizing the plasma spectral line intensities of the substrate characteristic elements. The weighting coefficient for the intensity of the contamination layer spectral lines. The normalized plasma spectral line intensities of characteristic elements in the contamination layer are obtained by normalizing the plasma spectral line intensities of characteristic elements in the contamination layer.

[0076] The time-domain average amplitude of the acoustic signal can reflect the average energy of the acoustic signal during the cleaning process, thereby determining the thickness of the microbial layer relative to the thickness removed before cleaning. By establishing a quantitative relationship between the time-domain average amplitude of the acoustic signal and the thickness removed, the removal status of microorganisms can be determined using the average amplitude.

[0077] Global kurtosis factor K, mean acoustic signal energy Harmony signal energy standard deviation Both can reflect the degree of fluctuation and impact of the acoustic signal during the cleaning process. Therefore, an acoustic fusion characteristic quantity—the acoustic energy kurtosis fluctuation index—is proposed. This comprehensively reflects the vibration and impact during the cleaning process, corresponding to the roughness of the cleaned surface. By establishing the relationship between EKFI and surface roughness, EKFI can be used to determine the surface roughness after cleaning.

[0078] The presence and intensity of characteristic peaks in the plasma spectral signatures of substrate elements and contaminant layer elements can reflect the degree of substrate exposure and contaminant removal during the cleaning process, thus qualitatively assessing the cleaning effect. Therefore, acoustic and spectral signals can be used to quantitatively and qualitatively detect the quality of the cleaned microbial layer.

[0079] Furthermore, regarding the characteristic quantities of acoustic signals and spectral signal characteristics By integrating the data, a comprehensive cleaning quality index Q is constructed as follows:

[0080] (4),

[0081] In the formula For acoustic signal weighting coefficients, These are the weighting coefficients for the spectral signal.

[0082] By fusing the characteristic quantities of acoustic and spectral signals, a quantifiable and automatically judged cleaning index model Q is formed. This model not only integrates various characteristic quantities of acoustic and spectral signals for comprehensive judgment, but also adaptively adjusts according to the surface distribution of different microbial layers. Based on the effective response of different microbial layers to different signals, the contribution of acoustic and spectral signals in the model is adaptively changed, thereby achieving the most accurate judgment precision. The Q value is related to the weighting coefficients of each characteristic quantity. In the initial stage, the distribution of different microorganisms on the surface can be judged by spectral pre-scanning, thereby automatically calculating and determining the weighting coefficient when the Q value reaches its maximum. This coefficient is used as the most suitable index model under this condition. Then, the Q value is calculated using different acoustic and spectral signal characteristic quantities according to this model. Within a specified threshold range, the larger the Q value, the better the surface cleaning effect. Experimental calculations show that the accuracy of the quality judgment of the method of this invention is approximately 95%.

[0083] Combination Figure 1 As shown, the laser beam emitted by the laser with randomly set cleaning parameters is oscillated at high speed by the scanning galvanometer and reciprocates on the surface of the metal equipment at a set speed; the acoustic signal of the laser cleaning is collected by an acoustic signal sensor, which is a microphone.

[0084] The spectral signal of laser cleaning is acquired by a spectral fiber optic probe and calibrated by a spectrometer.

[0085] Combination Figure 1 As shown, the signal acquisition device of the method of the present invention may include a nanosecond pulsed laser 1, a scanning galvanometer 2, a microphone 4, a UR22C sound card 5, a fiber optic probe 6, a spectrometer 7, and a computer 8. The nanosecond pulsed laser 1 emits a laser beam that oscillates at high speed through the scanning galvanometer 2, reciprocating at a certain speed on the surface 3 to be processed. Then, the acoustic and spectral signals of the cleaning process are acquired by the pre-fixed UR22C sound card 5 and the fiber optic probe 6. The acoustic signal is transmitted to the UR22C sound card 5 through the microphone 4, and then a time-frequency domain image is obtained in real time on the computer 8. The spectral signal is calibrated by the spectrometer 7 after the fiber optic probe 6 is used to display the intensity of different wavelength spectral lines on the computer 8. Specific Implementation

[0086] 1. Laser cleaning is used to remove the marine microbial layer on the surface of aluminum alloy, and acoustic and spectral sensors are used to collect the corresponding signals.

[0087] 2. Preprocessing of the acquired signals. Environmental noise, mechanical noise from equipment operation, and electronic noise from sensors are removed from the acoustic signals, retaining only the acoustic signals from the laser cleaning process. The processed acoustic signals are then subjected to Fourier transform to obtain a time-frequency image, and the time-domain average amplitude, global kurtosis factor, mean energy, and standard deviation of the acoustic signal energy are calculated. Baseline correction and background subtraction are performed on the spectral signals to obtain the spectral line intensities for specific wavebands. The cleaning state of the sample after cleaning is observed, and the removal thickness and surface roughness are measured.

[0088] 3. For example Figure 2 As shown, the time-domain average amplitude of the acoustic signal exhibits a trend of first increasing slowly, then increasing sharply, and finally increasing slowly again with increasing laser energy density. The time-domain average amplitude of the acoustic signal represents the average level of acoustic wave energy. Experiments show that when the laser first acts on the microbial layer, the acoustic signal intensity is relatively low, gradually increasing with increasing laser energy. At this time, the thickness of the removed microbial layer also increases slowly. Until complete removal is achieved, exposing the substrate surface, the average amplitude of the acoustic signal suddenly increases sharply. When the laser's action position changes from the microbial layer to the aluminum alloy substrate, the metal surface undergoes ablation and peeling, generating a significantly enhanced transient high-energy acoustic signal with a marked increase in average amplitude. At this point, the removal thickness is approximately 40 μm, indicating complete removal. Further increasing the laser energy maintains a high energy level for the acoustic signal, but the increase is slow. The time-domain average amplitude of the acoustic signal can be used to quantitatively predict the thickness of the removed microbial layer.

[0089] 4. To overcome the shortcomings of a single feature quantity in responding insufficiently to changes in surface roughness and to improve the sensitivity and robustness of cleaning status discrimination, this invention proposes a novel acoustic feature quantity—the acoustic energy kurtosis fluctuation index—based on the global kurtosis factor K and short-term energy fluctuations. EKFI is used to quantitatively describe the non-stationarity and impact characteristics of acoustic signals during laser cleaning, thereby determining the surface roughness state after cleaning. A higher EKFI corresponds to a greater surface roughness after cleaning, while a lower EKFI indicates a smoother and more uniform surface.

[0090] Global kurtosis factor measures the impulsiveness and spike-like nature of an acoustic signal; energy fluctuation coefficient (CV) E The Energy Kujicic Fluctuation Index (EKFI) measures the degree of acoustic signal fluctuation over time, reflecting signal fluctuations caused by surface uniformity and roughness. Combining these two factors yields the EKFI, and experimental results show that the EKFI matches well with the surface roughness after cleaning. Figure 3As shown, before reaching adequate cleaning, EKFI and roughness decrease in the same direction, indicating that the acoustic signal fluctuates violently and the peaks are severe, reflecting that the surface roughness is still high and not yet clean. When adequate cleaning is reached, both decrease to their lowest values. Then, as the laser energy continues to increase, both rise in the same direction, indicating that the substrate surface is further ablated and the roughness increases. The trends are well consistent. The acoustic energy kurtosis fluctuation index can quantitatively predict the change in surface roughness after cleaning.

[0091] 5. Based on the experimental results, the variation law of the substrate spectral intensity under different wavelength bands was analyzed, and it was found that the intensity at 578.198 nm under different cleaning processes was... The spectral peak changes show a clear trend, which can effectively reflect the cleaning effect, such as... Figure 4 As shown. Under low-energy cleaning, there is no peak value, the surface microbial layer remains, and the substrate is not exposed; before reaching adequate cleaning, the substrate gradually becomes exposed. The intensity of the spectral lines gradually increases until the entire substrate is exposed; as the laser energy continues to increase, the intensity of the spectral lines stabilizes and the increase is slow. The appearance of spectral peaks indicates that the substrate is beginning to be exposed. Stable spectral line intensity indicates that the substrate is fully exposed, achieving adequate cleaning. Changes in spectral lines can qualitatively predict whether the substrate is exposed and whether the marine microbial layer has been completely removed. Similarly, different elemental peaks can be selected as characteristic quantities for the surface microbial layer. In this experiment, after soaking, the sample mainly had two types of microbial layers: a hard attachment layer and an extracellular polymeric substance (EPS) layer. The experiment selected... -396.847nm, -658.9006 nm is used as the characteristic spectral line of the corresponding microbial layer.

[0092] 6. By fusing acoustic signal characteristics (quantitative features) and spectral signal characteristics (qualitative features), a single, quantifiable, and automatically assessable cleaning quality index is formed. This index is used for comprehensive, accurate, and real-time evaluation of the effectiveness of laser cleaning of marine microbial layers. Acoustic signal characteristics include the normalized time-domain average amplitude of the acoustic signal and the normalized acoustic energy kurtosis fluctuation index. Spectral signal characteristics include The -578.198nm spectral line and the contaminant spectral lines; different elemental spectral lines are selected for different microbial layers, such as... -396.847nm, Spectral lines of elements such as -658.9006 nm were observed. A comprehensive cleaning quality index, Q, was constructed. Table 1 shows the influence mechanism of each parameter on the Q value.

[0093] Table 1. Mechanism of influence of each parameter on the comprehensive index Q value

[0094] The above characteristic quantities of the acoustic and spectral signals under optimal cleaning conditions are recorded and statistically analyzed. The average value is then used as a reference index. The better the cleaning condition, the closer the obtained characteristic quantity is to the reference index, and the larger the corresponding value, the larger the Q value, representing the optimal cleaning condition. When the weighting coefficients are the same, the process corresponding to the largest Q value is the optimal process under this cleaning condition.

[0095] Different laser cleaning mechanisms exist for removing different marine microbial layers on aluminum alloy surfaces, such as hard deposits and extracellular polymeric substances (EPS) layers. The removal mechanism for hard deposits is a reverse bremsstrahlung mechanism, where the reverse bremsstrahlung absorption of the laser plasma induces a shock wave. Under the pressure of the shock wave, the hard deposits are peeled off the surface. Compared to spectral signal monitoring, the vibrational shock wave is more easily transmitted and reflected by acoustic signals, thus acoustic signal-dominated monitoring methods better reflect changes in surface elements. The removal mechanism for EPS layers is an ablation mechanism. The EPS layer is removed from the substrate surface through a series of processes including decomposition, combustion, and vaporization. Compared to acoustic signal monitoring, during the ablation and decomposition process, the plasma radiates a large number of atomic and ionic spectral lines. Spectral signal-dominated cleaning monitoring methods better reflect changes in surface elements, thus providing a more accurate reflection of the cleaning status.

[0096] In reality, the growth of different microbial layers is uneven, and their distribution ratio on the metal surface is uncertain. Before the experiment, the surface is scanned by spectrum to determine the growth of the microbial layer. The weighting coefficients are automatically calculated and selected to obtain the comprehensive index model structure with the largest Q value. Based on this model, the collected acoustic and spectral signal features are used to calculate the Q value. When the Q value is the largest, it is the optimal cleaning state.

[0097] like Figure 5 and Figure 6 These are macroscopic images of the surface after cleaning under different microbial growth conditions. Figure 5 With corresponding weighting coefficients a and b of 0.74 and 0.26 respectively, the Q value reaches its maximum at this point. Figure 6 With corresponding weighting coefficients a and b of 0.32 and 0.68 respectively, the Q value reaches its maximum at this point, indicating that the cleaning effect obtained in both cases is quite good, with no microbial residue on the surface and no damage to the substrate.

[0098] Although the present invention is described herein with reference to specific embodiments, it should be understood that these embodiments are merely illustrative of the principles and applications of the invention. It should be understood that many modifications may be made to the illustrative embodiments, and that other arrangements may be devised, without departing from the spirit and scope of the invention as defined by the appended claims. It should be understood that the various dependent claims and features described herein may be combined in ways other than those described in the original claims. It should also be understood that features described in conjunction with individual embodiments may be employed in conjunction with other described embodiments.

Claims

1. A laser cleaning method for marine microorganisms on the surface of metal equipment, characterized in that, include: First, the cleaning parameters of the laser are randomly set. During the cleaning process, the acoustic and spectral signals of the laser cleaning are collected, processed, and the acoustic and spectral signal characteristics are calculated. The acoustic signal characteristics are calculated based on the time-domain average amplitude, the mean energy, and the standard deviation of the energy; the spectral signal characteristics are calculated based on the plasma spectral line intensity of the substrate characteristic elements and the plasma spectral line intensity of the contamination layer characteristic elements. The acoustic signal characteristics and spectral signal characteristics are fused to construct a formula for calculating the comprehensive cleaning quality index Q; The optimal acoustic signal characteristic and the optimal spectral signal characteristic corresponding to the maximum cleaning quality comprehensive index Q value are calculated, and then the optimal cleaning parameters of the laser are obtained; the laser cleaning of marine microorganisms is carried out based on the optimal cleaning parameters.

2. The laser cleaning method for marine microorganisms on the surface of metal equipment according to claim 1, characterized in that, Methods for obtaining the time-domain average amplitude, mean energy, and standard deviation of acoustic signal energy include: Perform a Fourier transform on the acoustic signal to obtain the time-domain average amplitude, the mean energy of the acoustic signal, and the standard deviation of the acoustic signal energy.

3. The laser cleaning method for marine microorganisms on the surface of metal equipment according to claim 1, characterized in that, Methods for obtaining the plasma spectral intensity of characteristic elements in the substrate and the plasma spectral intensity of characteristic elements in the contamination layer include: The baseline correction of the spectral signal is performed using an adaptive iterative weighted penalized least squares method, and the plasma spectral intensity of the substrate characteristic elements and the contamination layer characteristic elements are extracted.

4. The laser cleaning method for marine microorganisms on the surface of metal equipment according to claim 1, characterized in that, The acoustic signal characteristics consist of the normalized time-domain average amplitude of the acoustic signal and the normalized acoustic energy kurtosis fluctuation index. The fusion is obtained; The normalized time-domain average amplitude of the acoustic signal is obtained by normalizing the time-domain average amplitude of the acoustic signal. Acoustic energy kurtosis fluctuation index for: （1）， In the formula, K is the global kurtosis factor, and CV E The energy fluctuation coefficient; （2）， In the formula The number of points in the discrete acoustic signal sequence. Let n be the time-domain amplitude of the discrete acoustic signal. The time-domain average amplitude of the acoustic signal; （3）， In the formula The average energy of the sound signal. The standard deviation of the acoustic signal energy; Acoustic energy kurtosis fluctuation index Normalization yields the normalized acoustic energy kurtosis fluctuation index. .

5. The laser cleaning method for marine microorganisms on the surface of metal equipment according to claim 4, characterized in that, The characteristic quantities of the acoustic signal are represented as : ， In the formula This is the amplitude weighting coefficient for the acoustic signal. The normalized time-domain average amplitude of the acoustic signal. For acoustic wave weighting coefficients, The normalized acoustic energy kurtosis fluctuation index .

6. The laser cleaning method for marine microorganisms on the surface of metal equipment according to claim 5, characterized in that, The spectral signal characteristics are obtained by fusing the normalized plasma spectral line intensities of the substrate characteristic elements and the normalized plasma spectral line intensities of the contamination layer characteristic elements.

7. The laser cleaning method for marine microorganisms on the surface of metal equipment according to claim 6, characterized in that, The spectral signal characteristics are expressed as : ， In the formula The substrate spectral line intensity weighting coefficient. The normalized plasma spectral line intensities of the substrate characteristic elements are obtained by normalizing the plasma spectral line intensities of the substrate characteristic elements. The weighting coefficient for the intensity of the contamination layer spectral lines. The normalized plasma spectral line intensities of characteristic elements in the contamination layer are obtained by normalizing the plasma spectral line intensities of characteristic elements in the contamination layer.

8. The laser cleaning method for marine microorganisms on the surface of metal equipment according to claim 7, characterized in that, acoustic signal characteristic quantities and spectral signal characteristics By integrating the data, a comprehensive cleaning quality index Q is constructed as follows: （4）， In the formula For acoustic signal weighting coefficients, These are the weighting coefficients for the spectral signal.

9. The laser cleaning method for marine microorganisms on the surface of metal equipment according to claim 1, characterized in that, A laser beam emitted by a laser with randomly set cleaning parameters is reciprocated at a set speed on the surface of a metal device by a high-speed oscillating scanning galvanometer; the acoustic signal of the laser cleaning is collected by an acoustic signal sensor, which is a microphone.

10. The laser cleaning method for marine microorganisms on the surface of metal equipment according to claim 1, characterized in that, The spectral signal of laser cleaning is acquired by a spectral fiber optic probe and calibrated by a spectrometer.

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