Megasonic wave cleaning adjustment system and method

Abstract

The present invention discloses a megasonic cleaning adjustment system, and in particular, relates to a megasonic cleaning adjustment system and method, which constructs a virtual three-dimensional model, performs preprocessing, performs complexity analysis on the virtual three-dimensional model, performs regional segmentation, outputs regional segmentation results, performs analysis based on the regional segmentation results, obtains analysis results, establishes allocation instructions based on the analysis results, and sends the allocation instructions to a cleaning end for cleaning. The present invention realizes accurate and efficient cleaning through a scanning modeling module, a model analysis module, an energy allocation module and a cleaning guidance module, divides an object into multiple regions based on its geometric features, optimizes cleaning frequency, energy density and time for different regions, corrects energy through wave attenuation and dispersion in complex regions to ensure in-depth cleaning, and an automated cleaning guidance module generates instructions based on the analysis results, dynamically adjusts the cleaning process, ensures uniform and lossless cleaning and improves efficiency, and is particularly suitable for fine cleaning of complex structures.

Description

Technical Field

[0001] The present invention relates to the technical field of megasonic cleaning, and in particular to a megasonic cleaning adjustment system and method. Background Art

[0002] With the rapid development of modern society, electricity has become an indispensable infrastructure to support the operation of the economy and society. However, the volatility and unpredictability of electricity demand have brought huge challenges to the stable operation of the power system. Especially in the face of sudden increases in power load or power grid failures, how to ensure the continuity and stability of power supply has become an urgent problem to be solved. Capacitor energy storage technology, as an important energy storage method, has gradually been widely used in power systems because of its advantages such as fast response speed, high power density and long cycle life.

[0003] At present, a Chinese patent with application number CN201910034157.9 discloses a megasonic cleaning method, device and megasonic cleaning equipment. When the megasonic cleaning equipment cleans the wafer, the controller of the megasonic cleaning equipment obtains the setting parameters of the megasonic nozzle, determines the operating area according to the setting parameters, and determines the operating time of the megasonic nozzle in each partition according to the number of partitions and the time ratio in the setting parameters. The operating trajectory of the megasonic nozzle in each partition is fitted according to the operating time of the megasonic nozzle in each partition. The controller of the megasonic cleaning equipment can control the megasonic nozzle to follow this operating trajectory to perform special cleaning on a specific area from the set operating starting point to the operating end point, thereby improving the cleaning effect of the wafer.

[0004] This invention makes it difficult to optimize the cleaning frequency, energy density and time for different areas, which affects the deep cleaning effect and dynamically adjusts the cleaning process. Summary of the invention

[0005] The technical problem solved by the present invention is that in the related art, it is difficult to optimize the cleaning frequency, energy density and time for different areas, which affects the deep cleaning effect and dynamically adjusts the cleaning process.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] A megasonic cleaning and adjustment system includes a scanning modeling module, a model analysis module, an energy distribution module and a cleaning guidance module:

[0008] The scanning and modeling module is used to scan the object to be cleaned and construct a virtual three-dimensional model, and pre-process the virtual three-dimensional model;

[0009] The model analysis module is used to analyze the preprocessed virtual three-dimensional model, perform region segmentation on the virtual three-dimensional model based on the geometric features of the virtual three-dimensional model, and output the region segmentation result;

[0010] The energy allocation module is used to perform energy allocation analysis and time allocation analysis according to the regional segmentation result to obtain the analysis result;

[0011] The cleaning guidance module is used to establish a distribution instruction according to the analysis result, and send the distribution instruction to the cleaning end for cleaning;

[0012] The scanning modeling module includes a scanning preparation unit, a scanning execution unit and a scanning processing unit;

[0013] The scanning preparation unit is used to set scanning parameters, and the scanning parameters include scanning accuracy and scanning speed;

[0014] The scanning execution unit is used to scan the object to be cleaned at multiple angles to obtain a virtual three-dimensional model;

[0015] The scanning processing unit is used to preprocess the virtual three-dimensional model, and the preprocessing is to remove noise.

[0016] The model analysis module includes a model identification unit and a region segmentation unit;

[0017] The model identification unit is used to obtain geometric features of the virtual three-dimensional model, the geometric features being the number of sharp corners and the number of facets, and the complexity is determined according to the geometric features:

[0018] If the number of sharp corners is greater than the preset threshold of the number of sharp corners or the number of facial skins is greater than the preset threshold of the number of facial patches, it is determined to be a complex model;

[0019] If the number of sharp corners is less than the preset threshold of the number of sharp corners and the number of face skins is less than the preset threshold of the number of face patches, it is determined to be a simple model;

[0020] Outputting a judgment result, wherein the judgment result is a complex model or a simple model;

[0021] The region segmentation unit is used to segment the virtual three-dimensional model according to the judgment result:

[0022] If the judgment result is a simple model, the output is a non-segmentation instruction;

[0023] If the judgment result is a complex model, a segmentation instruction is output, and the virtual 3D model is segmented according to the segmentation instruction. If there are vertices and adjacent vertices , calculate the triangular mesh The discrete Gaussian curvature of is expressed as:

[0024]

[0025] in, is the discrete Gaussian curvature, for and The angle between For The area of ​​the local region centered on

[0026] The region segmentation unit divides the virtual three-dimensional model into several geometric regions according to discrete Gaussian curvature:

[0027] If the discrete Gaussian curvature ≈ 1, the part with discrete Gaussian curvature ≈ 1 is divided into a flat area;

[0028] If the discrete Gaussian curvature is less than 0, the portion where the discrete Gaussian curvature is less than 0 is divided into a concave area;

[0029] If the discrete Gaussian curvature is greater than 0, the portion with the discrete Gaussian curvature greater than 0 is divided into a convex region;

[0030] If the absolute value of the discrete Gaussian curvature is ≫ 0, the portion where the absolute value of the discrete Gaussian curvature is ≫ 0 is divided into the corner region;

[0031] Outputting a region segmentation result, wherein the region segmentation result includes a flat region, a concave or convex region, and a corner region;

[0032] The energy distribution module includes an energy distribution unit, a time distribution unit and an analysis output unit;

[0033] The energy allocation unit is used to perform energy allocation analysis according to the region segmentation result and the judgment result, and the energy allocation analysis is:

[0034] If the judgment result is a simple model, the frequency is the first frequency value, the moving speed is the first moving speed value, and the energy density is the first energy density value;

[0035] If the energy distribution unit determines that the model is complex:

[0036] Flat area: the frequency is the second frequency value, the moving speed is the second moving speed value, and the energy density is the second energy density value;

[0037] Concave area: The frequency is the third frequency value, the moving speed is the third moving speed value, the concave depth is obtained, and the concave depth is analyzed:

[0038] If the depression depth is less than the depression depth threshold, the energy density is a third energy density value;

[0039] If the depression depth is greater than the depression depth threshold, the wave attenuation correction energy is calculated, and its mathematical expression is:

[0040]

[0041] in, Correcting the energy for wave attenuation, is the third energy density, is the absorption coefficient of sound waves in the concave area, is the depth of the depression;

[0042] Raised area: The frequency is the fourth frequency value, the moving speed is the fourth moving speed value, the surface area of ​​the raised area is obtained, and the discrete Gaussian curvature of the raised area is analyzed:

[0043] If the discrete Gaussian curvature of the raised area is less than the discrete Gaussian curvature threshold of the raised area, the energy density is the fourth energy density value;

[0044] If the discrete Gaussian curvature of the convex area is greater than the discrete Gaussian curvature threshold of the convex area, the dispersion correction energy is calculated, and its mathematical expression is:

[0045]

[0046] in, To disperse the correction energy, is the fourth energy density, is the energy concentration factor, Discretize the Gaussian curvature for the convex region;

[0047] Corner area: The frequency is the fifth frequency value, the moving speed is the fifth moving speed value, and the mathematical expression for energy density calculation is:

[0048]

[0049] in, is the energy density, is the fifth energy density value, is the incident angle between the cleaning head and the corner;

[0050] Output energy distribution analysis results, wherein the energy distribution analysis results include simple model energy distribution results and complex model energy distribution results.

[0051] Preferably, the time allocation unit is used to allocate time according to the region segmentation result and the judgment result, and the time allocation is:

[0052] If the judgment result is a simple model, the time is the first time value;

[0053] If the judgment result is a complex model:

[0054] Flat area: time is the second time value;

[0055] Concave area: time is the third time value;

[0056] Raised area: time is the fourth time value;

[0057] Corner area: time is the fifth time value;

[0058] Outputting time allocation analysis results, wherein the time allocation analysis results include simple model time allocation results and complex model time allocation results;

[0059] The analysis output unit is used to combine the energy allocation analysis result and the time allocation analysis result and output them as an analysis result.

[0060] Preferably, the cleaning guidance module includes an instruction establishment unit and a terminal cleaning unit;

[0061] The instruction establishment unit is used to establish energy allocation instructions according to the energy allocation result, wherein the energy density of the corner area changes with the incident angle gradient of the preset cleaning head and the corner, the cleaning order of the complex model is flat area, convex area, concave area and corner area, and the time allocation instruction is established according to the time allocation analysis result;

[0062] The terminal cleaning unit is used to send energy allocation instructions and time allocation instructions to the cleaning terminal to start cleaning.

[0063] A megasonic cleaning and adjustment method comprises the following steps:

[0064] Step S1, scanning the object to be cleaned and constructing a virtual three-dimensional model, preprocessing the virtual three-dimensional model, analyzing the preprocessed virtual three-dimensional model, performing region segmentation on the virtual three-dimensional model based on geometric features of the virtual three-dimensional model, and outputting the region segmentation result;

[0065] Step S2, performing energy allocation analysis and time allocation analysis according to the regional segmentation result to obtain analysis results;

[0066] Step S3, establishing a distribution instruction according to the analysis result, and sending the distribution instruction to the cleaning end for cleaning.

[0067] Beneficial effects of the present invention: The present invention realizes accurate and efficient cleaning through a scanning modeling module, a model analysis module, an energy distribution module and a cleaning guidance module. The object is divided into multiple areas based on its geometric features, and the cleaning frequency, energy density and time are optimized for different areas. The energy in complex areas is corrected by wave attenuation and dispersion to ensure in-depth cleaning. The automated cleaning guidance module generates instructions based on the analysis results and dynamically adjusts the cleaning process to ensure uniform and lossless cleaning and improve efficiency. It is particularly suitable for fine cleaning of complex structures. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1A basic flow diagram of a megasonic cleaning and conditioning system provided by one embodiment of the present invention;

[0069] Figure 2 A flowchart of the steps of a megasonic cleaning adjustment method provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0070] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0071] Embodiment 1, refer to Figure 1 , provides a megasonic cleaning and adjustment system, including a scanning modeling module, a model analysis module, an energy distribution module and a cleaning guidance module:

[0072] The scanning and modeling module is used to scan the object to be cleaned and construct a virtual three-dimensional model, and to pre-process the virtual three-dimensional model.

[0073] The model analysis module is used to analyze the preprocessed virtual three-dimensional model, perform region segmentation on the virtual three-dimensional model based on the geometric features of the virtual three-dimensional model, and output the region segmentation result.

[0074] The energy allocation module is used to perform energy allocation analysis and time allocation analysis according to the regional segmentation results and obtain the analysis results.

[0075] The cleaning guidance module is used to establish allocation instructions according to the analysis results and send the allocation instructions to the cleaning end for cleaning.

[0076] The scanning modeling module includes a scanning preparation unit, a scanning execution unit and a scanning processing unit:

[0077] The scanning preparation unit is used to set scanning parameters, and the scanning parameters include scanning accuracy and scanning speed.

[0078] The scanning preparation unit is responsible for setting the scanning parameters. By setting these parameters reasonably, the accuracy and balanced efficiency of the scanning process are ensured, laying a good foundation for subsequent steps.

[0079] The scanning execution unit is used to scan the object to be cleaned at multiple angles to obtain a virtual three-dimensional model.

[0080] The scanning execution unit performs multi-angle scanning to ensure that all details on the object surface are captured and generate a complete virtual 3D model. The effect is to ensure that the scanning data is comprehensive and reduce scanning blind areas.

[0081] The scanning processing unit is used to pre-process the virtual three-dimensional model, and the pre-processing is to remove noise.

[0082] The scanning processing unit pre-processes the acquired virtual 3D model to remove noise and redundant data, which has the effect of improving the clarity and accuracy of the model and ensuring that the analysis and cleaning steps are based on reliable model data.

[0083] The scanning and modeling module obtains a virtual 3D model of the object to be cleaned through high-precision, multi-angle scanning and pre-processes the model. The effect is reflected in the construction of an accurate virtual 3D model, the removal of noise and unnecessary data, and the provision of a basis for subsequent cleaning.

[0084] The model analysis module includes a model discrimination unit and a region segmentation unit:

[0085] The model identification unit is used to obtain the geometric features of the virtual 3D model. The geometric features are the number of sharp corners and the number of facets. The complexity is determined based on the geometric features:

[0086] If the number of sharp corners is greater than a preset threshold value for the number of sharp corners or the number of facial skins is greater than a preset threshold value for the number of facial patches, it is determined to be a complex model.

[0087] If the number of sharp corners is less than the preset threshold of the number of sharp corners and the number of face skins is less than the preset threshold of the number of face patches, it is determined to be a simple model.

[0088] The judgment result is output, and the judgment result is a complex model or a simple model.

[0089] The model discrimination unit evaluates the complexity of the virtual 3D model by calculating the number of sharp corners and the number of facets of the virtual 3D model. If the complexity of the virtual 3D model is high, it is identified as a complex model, otherwise it is a simple model. The effect is to quickly classify objects and provide preliminary judgment for subsequent segmentation and cleaning solutions.

[0090] The region segmentation unit is used to segment the virtual three-dimensional model according to the judgment result:

[0091] If the judgment result is a simple model, the output is a non-segmentation instruction.

[0092] If the judgment result is a complex model, a segmentation instruction is output, and the virtual 3D model is segmented according to the segmentation instruction. If there are vertices and adjacent vertices , calculate the triangular mesh The discrete Gaussian curvature of is expressed as:

[0093]

[0094] in, is the discrete Gaussian curvature, for and The angle between For The area of ​​the local region centered.

[0095] The region segmentation unit divides the virtual 3D model into several geometric regions according to discrete Gaussian curvature:

[0096] If the discrete Gaussian curvature ≈ 1, the portion where the discrete Gaussian curvature ≈ 1 is divided into a flat area.

[0097] If the discrete Gaussian curvature is less than 0, the portion where the discrete Gaussian curvature is less than 0 is divided into a concave area.

[0098] If the discrete Gaussian curvature is greater than 0, the portion where the discrete Gaussian curvature is greater than 0 is divided into a convex region.

[0099] If the absolute value of the discrete Gaussian curvature is ≫0, the portion where the absolute value of the discrete Gaussian curvature is ≫0 is divided into a corner region.

[0100] Output the region segmentation result, which includes flat area, concave or convex area and corner area.

[0101] The region segmentation unit divides the complex model into regions based on the discrimination results. By calculating discrete Gaussian curvature, the model is divided into flat regions, concave or convex regions, and corner regions. The effect is to accurately divide the complex regions to ensure that each region receives the most appropriate cleaning treatment.

[0102] The model analysis module automatically determines the complexity of the model by analyzing the geometric features of the virtual 3D model, and refines the complex model into different geometric areas to provide a basis for subsequent cleaning strategies. The overall effect is to provide accurate area division and optimized energy allocation strategy for the cleaning process.

[0103] The energy distribution module includes energy distribution unit, time distribution unit and analysis output unit:

[0104] The energy allocation unit is used to perform energy allocation analysis according to the region segmentation result and the judgment result, and the energy allocation analysis is:

[0105] If the judgment result is a simple model, the frequency is the first frequency value, the moving speed is the first moving speed value, and the energy density is the first energy density value.

[0106] Among them, the first frequency value is to Any value in the range, the first moving speed value is to For any value in to Any value in .

[0107] If the judgment result is a complex model:

[0108] Flat area: the frequency is the second frequency value, the moving speed is the second moving speed value, and the energy density is the second energy density value.

[0109] The second frequency value is to Any value in the range, the second moving speed value is to For any value in to Any value in .

[0110] Concave area: The frequency is the third frequency value, the moving speed is the third moving speed value, the concave depth is obtained, and the concave depth is analyzed:

[0111] If the depression depth is less than the depression depth threshold, the energy density is a third energy density value.

[0112] If the depression depth is greater than the depression depth threshold, the wave attenuation correction energy is calculated, and its mathematical expression is:

[0113]

[0114] in, Correcting the energy for wave attenuation, is the third energy density, is the absorption coefficient of sound waves in the concave area, is the depth of the depression.

[0115] Among them, the third frequency value is to Any value in the range, the third moving speed value is to For any value in to Any value in .

[0116] Raised area: The frequency is the fourth frequency value, the moving speed is the fourth moving speed value, the surface area of ​​the raised area is obtained, and the discrete Gaussian curvature of the raised area is analyzed:

[0117] If the discrete Gaussian curvature of the convex region is less than the discrete Gaussian curvature threshold of the convex region, the energy density is a fourth energy density value.

[0118] If the discrete Gaussian curvature of the convex area is greater than the discrete Gaussian curvature threshold of the convex area, the dispersion correction energy is calculated, and its mathematical expression is:

[0119]

[0120] in, To disperse the correction energy, is the fourth energy density, is the energy concentration factor, Discretize the Gaussian curvature for the convex region.

[0121] Among them, the fourth frequency value is to Any value in the fourth moving speed value is to For any value in to Any value in .

[0122] Corner area: The frequency is the fifth frequency value, the moving speed is the fifth moving speed value, and the mathematical expression for energy density calculation is:

[0123]

[0124] in, is the energy density, is the fifth energy density value, is the incident angle between the cleaning head and the corner.

[0125] Output energy distribution analysis results, wherein the energy distribution analysis results include simple model energy distribution results and complex model energy distribution results.

[0126] Among them, the fifth frequency value is to Any value in the range, the fifth moving speed value is to For any value in to Any value in .

[0127] The energy distribution unit distributes the cleaning frequency, movement speed and energy density according to the regional segmentation results. For simple models, uniform parameters are used, and for complex models, precise adjustments are made according to the geometric characteristics of different regions. The effect is to distribute appropriate energy according to the needs of each region to ensure consistent and safe cleaning results.

[0128] The time allocation unit is used to allocate time according to the region segmentation result and the judgment result, and the time allocation is:

[0129] If the judgment result is a simple model, the time is a first time value, and the first time value is any value from 2s to 10s.

[0130] If the judgment result is a complex model:

[0131] Flat area: the time is the second time value, which is any value between 5s and 10s.

[0132] Concave area: the time is the third time value, and the third time value is any value between 10s and 20s.

[0133] Raised area: the time is the fourth time value, and the fourth time value is any value from 8s to 15s.

[0134] Corner area: The time is the fifth time value, which is any value from 10s to 25s.

[0135] Output the time allocation analysis results, which include simple model time allocation results and complex model time allocation results.

[0136] The time allocation unit allocates appropriate cleaning time according to the complexity of the area and the cleaning requirements. The simple model uses a unified time, while the complex model allocates different cleaning times according to the difficulty of different areas. The effect is to avoid unnecessary waste of cleaning time and improve cleaning efficiency.

[0137] The analysis output unit is used to combine the energy allocation analysis result and the time allocation analysis result and output them as the analysis result.

[0138] The analysis output unit combines the results of energy allocation and time allocation into an overall cleaning plan, which has the effect of providing a complete set of cleaning instructions to ensure that each step can be executed as planned to achieve the best cleaning results.

[0139] The energy allocation module automatically adjusts the energy and time distribution used in the cleaning process according to the complexity of the virtual 3D model and the results of regional segmentation, ensuring that each area can be cleaned at the appropriate frequency, speed and energy density, thereby optimizing cleaning efficiency and effects.

[0140] The cleaning guidance module includes an instruction establishment unit and a terminal cleaning unit:

[0141] The instruction establishment unit is used to establish energy allocation instructions based on the energy allocation results, where the energy density in the corner area changes with the incident angle gradient between the preset cleaning head and the corner, and the cleaning order of the complex model is flat area, raised area, recessed area and corner area. The time allocation instructions are established based on the time allocation analysis results.

[0142] The instruction establishment unit generates detailed cleaning instructions based on the analyzed energy and time distribution results. Especially for corner areas, the energy density of the cleaning head will be dynamically adjusted according to the change of the incident angle. The cleaning order of complex models is flat areas, raised areas, recessed areas and corner areas to optimize the cleaning effect. The effect is to ensure that each area is cleaned according to precise parameters, thereby improving the cleaning accuracy and efficiency.

[0143] The terminal cleaning unit is used to send energy allocation instructions and time allocation instructions to the cleaning terminal to start cleaning.

[0144] The terminal cleaning unit is responsible for transmitting the generated energy and time allocation instructions to the cleaning equipment and starting the cleaning process. The effect is to ensure that the cleaning equipment strictly performs the cleaning task according to the set cleaning sequence and parameters, achieving efficient and uniform cleaning results.

[0145] The cleaning guidance module generates cleaning instructions based on the energy allocation and time allocation analysis results, and sends the instructions to the cleaning equipment to ensure that the entire cleaning process is carried out according to the optimal plan and achieves precise cleaning based on the characteristics of different areas.

[0146] The megasonic cleaning and adjustment system realizes an efficient and accurate cleaning process through scanning modeling module, model analysis module, energy distribution module and cleaning guidance module. Its effects are reflected in the following aspects:

[0147] The virtual three-dimensional model of the object is obtained through the scanning modeling module, and is analyzed based on geometric features. The object is divided into flat areas, concave areas, convex areas and corner areas using indicators such as Gaussian curvature. This ensures that the cleaning strategy can be optimized according to the geometric complexity and surface morphology of each area, so that each area can receive appropriate cleaning treatment.

[0148] Based on the results of regional division, it is possible to allocate appropriate energy density, frequency and cleaning time to different areas. The precise allocation of energy and time avoids energy waste or insufficiency during the cleaning process, while ensuring that each complex geometric area can obtain a suitable cleaning effect.

[0149] The cleaning order is automatically arranged according to the complexity of the object. The easy-to-handle flat areas are cleaned first, followed by the raised areas, recessed areas and corner areas. This cleaning order can reduce the effects of sound wave interference, reflection and energy attenuation, and improve cleaning efficiency.

[0150] For complex models, the depth of depressions and curvature of convexities can be dynamically analyzed during the cleaning process, and the energy density and cleaning time can be adjusted based on these parameters. At the same time, the energy can be corrected by wave attenuation and dispersion to optimize the cleaning depth and accuracy, avoiding local damage or incomplete cleaning.

[0151] All analysis results are converted into automatic instructions and sent to the terminal cleaning equipment. The cleaning process is fully automated, reducing errors caused by human intervention, ensuring the accuracy and consistency of the entire process. It can also adjust energy distribution and cleaning time according to real-time conditions, further improving adaptability and flexibility.

[0152] The intelligent division of the virtual 3D model of the object and the targeted allocation of cleaning strategies can reduce energy consumption and cleaning time while ensuring the cleaning effect. For areas with complex geometries or difficult to clean, the wave correction and dispersed energy strategies are used to achieve in-depth cleaning of various structures, effectively improving the cleaning efficiency and accuracy, and ultimately achieving efficient, uniform and non-destructive cleaning results.

[0153] Example 2, refer to Figure 2 , provides a megasonic cleaning adjustment method, comprising the following steps:

[0154] Step S1, scanning the object to be cleaned and constructing a virtual three-dimensional model, preprocessing the virtual three-dimensional model, analyzing the preprocessed virtual three-dimensional model, segmenting the virtual three-dimensional model based on the geometric features of the virtual three-dimensional model, and outputting the segmentation result.

[0155] By scanning the object to be cleaned, an accurate virtual 3D model is generated, and pre-processing such as noise removal is performed to ensure the clarity of the model. Subsequently, the model is segmented based on geometric features to separate flat, concave, convex, and corner areas to ensure that the cleaning requirements of each area are accurately identified.

[0156] Step S2, performing energy allocation analysis and time allocation analysis according to the region segmentation result to obtain analysis results.

[0157] Based on the results of regional segmentation, targeted energy and time allocation analysis is carried out to ensure that each area is allocated appropriate cleaning energy and time according to its complexity and characteristics. This analysis process ensures maximum cleaning efficiency, especially precise cleaning control for complex areas.

[0158] Step S3, establishing a distribution instruction according to the analysis result, and sending the distribution instruction to the cleaning end for cleaning.

[0159] The energy and time allocation instructions generated based on the analysis results are sent to the cleaning end to ensure that the actual cleaning process is executed according to the optimal plan. By precisely controlling the cleaning parameters, efficient and uniform cleaning of each area can be achieved, ensuring the cleaning quality and energy-saving effect.

[0160] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. Among them, the storage medium may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red Only Memory, referred to as PROM), read-only memory (Read Only Memory, referred to as ROM), magnetic memory, flash memory, magnetic disk or optical disk. These computer program instructions may also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which is implemented in the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0161] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A megasonic cleaning and conditioning system, characterized in that: It includes scanning modeling module, model analysis module, energy distribution module and cleaning guidance module; The scanning and modeling module is used to scan the object to be cleaned and construct a virtual three-dimensional model, and pre-process the virtual three-dimensional model; The model analysis module is used to perform complexity analysis on the preprocessed virtual three-dimensional model, perform region segmentation on the virtual three-dimensional model based on geometric features of the virtual three-dimensional model, and output the region segmentation result; The energy allocation module is used to perform energy allocation analysis and time allocation analysis according to the regional segmentation result to obtain the analysis result; The cleaning guidance module is used to establish a distribution instruction according to the analysis result, and send the distribution instruction to the cleaning end for cleaning; The scanning modeling module includes a scanning preparation unit, a scanning execution unit and a scanning processing unit; The scanning preparation unit is used to set scanning parameters, and the scanning parameters include scanning accuracy and scanning speed; The scanning execution unit is used to scan the object to be cleaned at multiple angles to obtain a virtual three-dimensional model; The scanning processing unit is used to pre-process the virtual three-dimensional model, and the pre-processing is to remove noise; The model analysis module includes a model identification unit and a region segmentation unit; The model identification unit is used to obtain geometric features of the virtual three-dimensional model, the geometric features being the number of sharp corners and the number of facets, and the complexity is determined according to the geometric features: If the number of sharp corners is greater than the preset threshold of the number of sharp corners or the number of facial skins is greater than the preset threshold of the number of facial patches, it is determined to be a complex model; If the number of sharp corners is less than the preset threshold of the number of sharp corners and the number of face skins is less than the preset threshold of the number of face patches, it is determined to be a simple model; Outputting a judgment result, wherein the judgment result is a complex model or a simple model; The region segmentation unit is used to segment the virtual three-dimensional model according to the judgment result; If the judgment result is a simple model, the output is a non-segmentation instruction; If the judgment result is a complex model, a segmentation instruction is output, and the virtual 3D model is segmented according to the segmentation instruction. If there are vertices and adjacent vertices , calculate the triangular mesh The discrete Gaussian curvature of is expressed as: in, is the discrete Gaussian curvature, for and The angle between For The area of ​​the local region centered on The region segmentation unit divides the virtual three-dimensional model into several geometric regions according to discrete Gaussian curvature: If the discrete Gaussian curvature ≈ 1, the part with discrete Gaussian curvature ≈ 1 is divided into a flat area; If the discrete Gaussian curvature is less than 0, the part where the discrete Gaussian curvature is less than 0 is divided into a concave area; If the discrete Gaussian curvature is greater than 0, the portion with the discrete Gaussian curvature greater than 0 is divided into a convex region; If the absolute value of the discrete Gaussian curvature is ≫ 0, the portion where the absolute value of the discrete Gaussian curvature is ≫ 0 is divided into the corner region; Outputting a region segmentation result, wherein the region segmentation result includes a flat region, a concave or convex region, and a corner region; The energy distribution module includes an energy distribution unit, a time distribution unit and an analysis output unit; The energy allocation unit is used to perform energy allocation analysis according to the region segmentation result and the judgment result, and the energy allocation analysis is: If the judgment result is a simple model, the frequency is the first frequency value, the moving speed is the first moving speed value, and the energy density is the first energy density value; If the energy distribution unit determines that the model is complex: Flat area: the frequency is the second frequency value, the moving speed is the second moving speed value, and the energy density is the second energy density value; Concave area: The frequency is the third frequency value, the moving speed is the third moving speed value, the concave depth is obtained, and the concave depth is analyzed: If the depression depth is less than the depression depth threshold, the energy density is a third energy density value; If the depression depth is greater than the depression depth threshold, the wave attenuation correction energy is calculated, and its mathematical expression is: in, Correcting the energy for wave attenuation, is the third energy density, is the absorption coefficient of sound waves in the concave area, is the depth of the depression; Raised area: The frequency is the fourth frequency value, the moving speed is the fourth moving speed value, the surface area of ​​the raised area is obtained, and the discrete Gaussian curvature of the raised area is analyzed: If the discrete Gaussian curvature of the raised area is less than the discrete Gaussian curvature threshold of the raised area, the energy density is the fourth energy density value; If the discrete Gaussian curvature of the convex area is greater than the discrete Gaussian curvature threshold of the convex area, the dispersion correction energy is calculated, and its mathematical expression is: in, To disperse the correction energy, is the fourth energy density, is the energy concentration factor, Discretize the Gaussian curvature for the convex region; Corner area: The frequency is the fifth frequency value, the moving speed is the fifth moving speed value, and the mathematical expression for energy density calculation is: in, is the energy density, is the fifth energy density value, is the incident angle between the cleaning head and the corner; Output energy distribution analysis results, wherein the energy distribution analysis results include simple model energy distribution results and complex model energy distribution results.

2. A megasonic cleaning and conditioning system as claimed in claim 1, characterized in that: The time allocation unit is used to allocate time according to the region segmentation result and the judgment result, and the time allocation is: If the judgment result is a simple model, the time is the first time value; If the judgment result is a complex model: Flat area: time is the second time value; Concave area: time is the third time value; Raised area: time is the fourth time value; Corner area: time is the fifth time value; Outputting time allocation analysis results, wherein the time allocation analysis results include simple model time allocation results and complex model time allocation results; The analysis output unit is used to combine the energy allocation analysis result and the time allocation analysis result and output them as an analysis result.

3. A megasonic cleaning and conditioning system as claimed in claim 2, characterized in that: The cleaning guidance module includes an instruction establishment unit and a terminal cleaning unit; The instruction establishment unit is used to establish energy allocation instructions according to the energy allocation result, wherein the energy density of the corner area changes with the incident angle gradient of the preset cleaning head and the corner, the cleaning order of the complex model is flat area, convex area, concave area and corner area, and the time allocation instruction is established according to the time allocation analysis result; The terminal cleaning unit is used to send energy allocation instructions and time allocation instructions to the cleaning terminal to start cleaning.

4. A megasonic cleaning and regulating method, which is applied to a megasonic cleaning and regulating system as claimed in any one of claims 1 to 3, characterized in that: The following steps are involved: Step S1, scanning the object to be cleaned and constructing a virtual three-dimensional model, preprocessing the virtual three-dimensional model, analyzing the preprocessed virtual three-dimensional model, performing region segmentation on the virtual three-dimensional model based on geometric features of the virtual three-dimensional model, and outputting the region segmentation result; Step S2, performing energy allocation analysis and time allocation analysis according to the regional segmentation result to obtain analysis results; Step S3, establishing a distribution instruction according to the analysis result, and sending the distribution instruction to the cleaning end for cleaning.

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