Construction method of digital twin model of sound field of electrical equipment

Abstract

The invention relates to the technical field of digital twinning, in particular to a construction method of a digital twinning model of a sound field of electrical equipment. The method comprises the following steps: acquiring structural design data of the electrical equipment, and constructing a three-dimensional geometric model of the electrical equipment according to the structural design data of the electrical equipment, so as to obtain the three-dimensional geometric model of the electrical equipment; acquiring equipment acoustic characteristic data, and performing multi-material sound wave propagation analysis on the three-dimensional electrical equipment geometric model based on the equipment acoustic characteristic data so as to obtain multi-material sound wave propagation data; carrying out structural design optimization on the three-dimensional electrical equipment geometric model based on the multi-material sound wave propagation data so as to obtain equipment structural design optimization data; and performing acoustic simulation on the three-dimensional electrical equipment geometric model according to the equipment acoustic characteristic data so as to obtain acoustic simulation data. The acoustic performance and the noise suppression rate of the equipment are improved based on the digital twinning technology.

Description

Technical Field

[0001] The present invention relates to the field of digital twin technology, and in particular to a method for constructing a digital twin model of an electrical equipment sound field. Background Art

[0002] Traditional methods usually rely on manual modeling and parameter setting. The whole process is very cumbersome and easily affected by human factors, resulting in errors. Traditional methods usually rely on computational methods such as finite element analysis for vibration and noise analysis. This method has problems of insufficient computational accuracy and high computational cost when dealing with complex, multi-physics field coupled systems, especially for some high-frequency noise problems. The model cannot accurately capture its tiny changes. Traditional models often do not take into account the needs of intelligence and real-time data updates, so it is difficult to dynamically adjust and optimize during the use of the equipment, limiting the overall value and application potential of digital twin technology. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a method for constructing a digital twin model of the sound field of electrical equipment to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above purpose, a method for constructing a digital twin model of the sound field of an electrical device comprises the following steps:

[0005] Step S1: acquiring electrical equipment structural design data, and constructing a three-dimensional electrical equipment geometric model according to the electrical equipment structural design data, thereby obtaining a three-dimensional electrical equipment geometric model;

[0006] Step S2: Acquire equipment acoustic characteristic data, and perform multi-material sound wave propagation analysis on the three-dimensional electrical equipment geometric model based on the equipment acoustic characteristic data, thereby obtaining multi-material sound wave propagation data; perform structural design optimization on the three-dimensional electrical equipment geometric model based on the multi-material sound wave propagation data, thereby obtaining equipment structural design optimization data;

[0007] Step S3: performing acoustic simulation on the three-dimensional electrical equipment geometric model according to the equipment acoustic characteristic data, thereby obtaining acoustic simulation data; performing abnormal noise analysis on the acoustic simulation data, thereby obtaining abnormal noise data; performing equipment noise optimization control according to the abnormal noise data, thereby obtaining equipment noise optimization data;

[0008] Step S4: construct a digital twin model of the sound field of the electrical equipment according to the equipment noise optimization data and the equipment structure design optimization data, so as to obtain a digital twin model of the sound field of the electrical equipment.

[0009] The present invention can accurately reproduce the appearance and structure of the equipment by constructing a three-dimensional electrical equipment geometric model, provide basic data for subsequent analysis, and avoid the errors and cumbersomeness caused by manual modeling in traditional methods. In step S2, by analyzing the propagation of multi-material sound waves, the propagation characteristics of sound waves in different materials of the equipment can be more accurately simulated, and the structural optimization design is carried out in combination with these data, so that the acoustic performance of the equipment is improved, the generation of noise is reduced, and the optimized structural design can effectively reduce the propagation path of the noise, and improve the working efficiency and comfort of the equipment. This process can avoid the problems of high calculation cost and insufficient calculation accuracy in traditional methods, especially when dealing with complex equipment, and can more accurately capture the details of sound wave propagation. Step S3 can more comprehensively predict the noise level of the equipment in actual operation through acoustic simulation, identify the noise source through abnormal noise analysis and adjust it in time, and further reduce the noise impact. In addition, the equipment noise optimization control based on these abnormal noise data can dynamically optimize the equipment in real time, ensure that the equipment can maintain low noise operation under different use conditions, and avoid the defects of lack of real-time data update and adjustment in traditional methods. Finally, by combining the equipment structure design optimization data and noise optimization data, the digital twin model of the electrical equipment sound field can not only accurately reflect the sound field of the equipment, but also be updated and adjusted in real time, thus greatly enhancing the practicality and application potential of digital twin technology. The entire process integrates precise modeling, intelligent optimization and real-time feedback, greatly improving the accuracy, efficiency and adaptability of the design and operation process, overcoming many limitations of traditional methods.

[0010] Preferably, step S1 specifically comprises:

[0011] Step S11: acquiring electrical equipment structural design data, and performing equipment housing design feature extraction and internal component design feature extraction based on the electrical equipment structural design data, thereby obtaining equipment housing design data and internal component design data;

[0012] Step S12: Modeling the geometric shape of the housing based on the device housing design data, thereby obtaining housing geometric shape modeling data;

[0013] Step S13: assigning material properties to the shell geometry modeling data according to the device shell design data, thereby obtaining shell material property modeling data;

[0014] Step S14: setting the shell thickness according to the shell material property modeling data, thereby obtaining the device shell modeling data;

[0015] Step S15: performing motor modeling based on the internal component design data, thereby obtaining motor modeling data;

[0016] Step S16: constructing a three-dimensional geometric model of the electrical equipment according to the equipment housing modeling data and the motor modeling data, thereby obtaining a three-dimensional geometric model of the electrical equipment.

[0017] The present invention can systematically gather all aspects of equipment design information by acquiring equipment structure design data and extracting shell design features and internal component design features, avoiding errors in manual modeling and ensuring the integrity and accuracy of design data. Next, by modeling the shell geometry, it can be ensured that the design of the equipment shell can accurately reflect its appearance and functional requirements, providing a solid foundation for subsequent analysis and optimization. After assigning shell material properties, it can not only ensure that the influence of material properties on equipment performance is taken into account, but also provide real material information for structural analysis, thereby improving the accuracy of analysis. Further, by setting the shell thickness, the structural strength of the shell and its transmission characteristics to vibration and noise can be accurately controlled and optimized. Motor modeling can provide detailed parameters for the power system of the equipment to ensure that its operating characteristics are fully considered. Finally, by combining the shell modeling data and the motor modeling data, a complete three-dimensional electrical equipment geometric model is constructed, which can accurately reproduce the overall structure of the equipment and provide support for subsequent performance analysis, acoustic simulation, vibration analysis, etc. Overall, this series of steps can significantly improve the accuracy and efficiency of equipment design through systematic and automated modeling and data processing, overcome the cumbersome operations and computing cost problems in traditional methods, and avoid interference from human factors, providing a more reliable and intelligent design solution, which helps to enhance the application value of digital twin technology.

[0018] Preferably, step S2 specifically comprises:

[0019] Step S21: Acquire device acoustic characteristic data, and perform sound propagation path feature extraction and sound propagation intensity feature extraction based on the device acoustic characteristic data, thereby obtaining sound propagation path data and sound propagation intensity data;

[0020] Step S22: inputting the sound propagation path data and the sound propagation intensity data into the three-dimensional electrical equipment geometric model, and using simulation software to simulate the sound wave propagation, thereby obtaining the sound wave propagation simulation data;

[0021] Step S23: performing multi-material attenuation analysis according to the acoustic wave propagation simulation data, thereby obtaining multi-material acoustic wave attenuation data;

[0022] Step S24: evaluating the acoustic wave propagation velocity of the multi-material acoustic wave attenuation data, thereby obtaining the multi-material acoustic wave propagation data;

[0023] Step S25: Optimizing the structural design of the three-dimensional electrical equipment geometric model based on the multi-material sound wave propagation data, thereby obtaining equipment structural design optimization data.

[0024] The present invention provides more systematic and accurate basic data for the entire acoustic analysis process by obtaining the acoustic characteristic data of the equipment and extracting the sound propagation path and intensity characteristics, avoiding the interference of human factors and improving the accuracy and reliability of the data. Next, these data are input into the three-dimensional electrical equipment geometric model, and the sound wave propagation simulation is performed through the simulation software, which can accurately capture the sound wave propagation behavior of the equipment under different operating environments, ensuring the scientificity and authenticity of the simulation results. This process effectively optimizes the accuracy of acoustic performance evaluation, especially in complex systems with multi-physical field coupling, and can handle more complex sound wave propagation processes. Further multi-material attenuation analysis can evaluate the sound wave attenuation characteristics of different materials, providing specific material selection and design basis for subsequent optimization. In addition, through the evaluation of the sound wave propagation velocity, it is possible to analyze how materials and structures affect the propagation characteristics of sound waves in the equipment, providing more data support for optimizing the structure. Finally, by optimizing the structural design of the three-dimensional electrical equipment geometric model based on these data, not only the acoustic performance of the equipment is improved, but also noise pollution is reduced, and the effect and function of the overall design are improved. Through the synergistic effect of these steps, not only the problem of insufficient calculation accuracy in traditional methods is solved, but also intelligent dynamic optimization during the use of equipment is realized, so that digital twin technology can better meet the actual application needs.

[0025] Preferably, step S23 is specifically:

[0026] Step S231: performing Fourier transform on the acoustic wave propagation simulation data to obtain acoustic wave propagation frequency domain data;

[0027] Step S232: extracting propagation material characteristics from the acoustic wave propagation simulation data, thereby obtaining propagation material data;

[0028] Step S233: selecting an attenuation model based on the propagation material data, thereby obtaining an attenuation model;

[0029] Step S234: performing frequency domain energy attenuation analysis on the sound wave propagation frequency domain data according to the attenuation model, thereby obtaining sound wave frequency domain energy attenuation data;

[0030] Step S235: performing material interface reflection loss analysis on the sound wave propagation frequency domain data, thereby obtaining sound wave propagation material interface reflection loss data;

[0031] Step S236: performing interface correction on the sound wave frequency domain energy attenuation data according to the sound wave propagation material interface reflection loss data, thereby obtaining multi-material sound wave attenuation data.

[0032] The present invention uses Fourier transform to perform frequency domain conversion on the acoustic wave propagation simulation data, which can convert the complex signal in the time domain into a frequency domain signal, so as to analyze the propagation characteristics of different frequency components, especially when dealing with high-frequency noise problems, and can accurately capture small changes. Then, through the propagation material feature extraction, the acoustic characteristics of the material can be obtained, and the necessary basic data can be provided for the subsequent attenuation model selection, thereby ensuring the accuracy and applicability of the model. Based on the attenuation model selection, the frequency domain energy attenuation analysis is carried out, which helps to quantify the energy loss of different frequency bands during the propagation process, and then evaluate the influence of materials and structures on the propagation of acoustic waves. The material interface reflection loss analysis further improves the accuracy of the model, can identify and quantify the reflection loss of acoustic waves at different material interfaces, and further improves the simulation results of acoustic wave propagation. Finally, the acoustic wave frequency domain energy attenuation data is interface-corrected based on the material interface reflection loss data, so that the multi-material acoustic wave attenuation data is more accurate and reliable, ensuring that the acoustic model can reflect the real multi-physics field coupling effect. The synergy of these steps makes the analysis results more refined and efficient, thereby achieving more accurate noise control and optimization during the equipment design process, promoting the possibility of intelligent real-time data updates and dynamic adjustments, and enhancing the application value of digital twin technology.

[0033] Preferably, step S25 is specifically as follows:

[0034] Step S251: using finite element analysis software to solve the free vibration of the three-dimensional electrical equipment geometric model, thereby obtaining free vibration data of the geometric model;

[0035] Step S252: performing harmonic response analysis on the three-dimensional electrical equipment geometric model based on the multi-material sound wave propagation data, thereby obtaining sound wave vibration data;

[0036] Step S253: performing vibration resonance effect analysis according to the free vibration data of the geometric model and the acoustic vibration data, thereby obtaining vibration resonance effect data;

[0037] Step S254: performing structural reinforcement optimization on the three-dimensional electrical equipment geometric model according to the vibration resonance effect data, thereby obtaining structural reinforcement optimization data;

[0038] Step S255: superimposing sound-absorbing materials on the three-dimensional electrical equipment geometric model according to the vibration resonance effect data, thereby obtaining sound-absorbing material optimization data;

[0039] Step S256: Perform equipment structure design optimization integration based on the structural reinforcement optimization data and the sound-absorbing material optimization data, so as to obtain equipment structure design optimization data.

[0040] The present invention can accurately simulate the vibration characteristics of the equipment at different frequencies by using finite element analysis to solve free vibration, ensuring that the model can accurately reflect the actual physical behavior. By performing harmonic response analysis on the geometric model of three-dimensional electrical equipment, the propagation of acoustic vibration can be deeply understood, and the design can be further optimized to reduce noise generation. The analysis of vibration resonance effect helps to identify the resonant frequency that causes unstable equipment performance, thereby providing an important basis for subsequent structural reinforcement and optimization. Structural reinforcement optimization improves the rigidity of the equipment, reduces the noise caused by vibration, and improves the service life and stability of the equipment. At the same time, the superposition of sound-absorbing materials can effectively reduce the propagation of noise, especially in high-frequency noise control. Finally, the combination of structural reinforcement optimization and sound-absorbing material optimization provides a more comprehensive equipment structure design optimization solution, so that the equipment can better cope with different noise sources and vibration effects during actual use. These steps work together to not only improve the accuracy and reliability of the analysis, but also lay the foundation for the dynamic adjustment and optimization of the equipment, breaking through the limitations of traditional methods, and greatly improving the application value and feasibility of digital twin technology.

[0041] Preferably, step S253 is specifically as follows:

[0042] Perform vibration frequency statistics based on the free vibration data of the geometric model to obtain free vibration frequency data;

[0043] Performing vibration frequency statistics on the sound wave vibration data, thereby obtaining the sound wave vibration frequency data;

[0044] Resonance frequency matching is performed according to the free vibration frequency data and the acoustic wave vibration frequency data, thereby obtaining resonance frequency data;

[0045] Perform time statistics according to the resonance frequency data to obtain resonance time data;

[0046] Performing vibration amplitude analysis on the free vibration data of the geometric model according to the resonance time data, thereby obtaining free vibration amplitude data;

[0047] Performing vibration amplitude analysis on the acoustic wave vibration data according to the resonance time data, thereby obtaining the acoustic wave vibration amplitude data;

[0048] The vibration amplitude range is counted according to the free vibration amplitude data and the acoustic wave vibration amplitude data, so as to obtain the vibration amplitude range data;

[0049] The vibration resonance effect is integrated according to the resonance frequency data and the vibration amplitude range data, so as to obtain the vibration resonance effect data.

[0050] The present invention effectively improves the understanding of the vibration characteristics of the equipment by performing detailed frequency statistics and amplitude analysis on the free vibration data and the acoustic vibration data, ensuring that the resonant frequency and vibration amplitude range of the equipment can be accurately identified. By performing statistics on the free vibration frequency and the acoustic vibration frequency, the vibration characteristics of the equipment under different working conditions can be more comprehensively grasped, providing data support for the subsequent resonant frequency matching, which helps to avoid the resonance phenomenon during the operation of the equipment and reduce the potential risk of failure. The time statistics of the resonant frequency data provide a more detailed analysis, so that the operating behavior of the equipment in different time periods can be evaluated, and the design and use cycle of the equipment can be further optimized. In addition, the vibration amplitude analysis not only helps to evaluate the impact of vibration on equipment performance, but also provides guidance for optimizing vibration control strategies. By performing statistics on the vibration amplitude range, vibration problems can be foreseen in the design stage, and then appropriate measures can be taken to prevent them. Finally, through the integration of the vibration resonance effect, the equipment can be comprehensively optimized to ensure that it exhibits better performance and stability under various operating conditions. This series of steps effectively compensates for the lack of accuracy and high computational cost of traditional methods in vibration and noise analysis. By introducing intelligent real-time data updates and dynamic adjustment mechanisms, it breaks through the limitations of traditional methods and enhances the application potential and overall value of digital twin technology.

[0051] Preferably, step S254 is specifically as follows:

[0052] According to the vibration resonance effect data, the key geometric structure parts of the three-dimensional electrical equipment geometric model are identified to obtain the key geometric structure parts data;

[0053] Add support points to the key geometric structure data, so as to obtain support point addition data;

[0054] Select the flexible support system for the support point addition data, so as to obtain the flexible support system data;

[0055] Adding a shock absorbing device to the data of key geometric structure parts, thereby obtaining the data of adding the shock absorbing device;

[0056] The structural reinforcement optimization and integration is carried out according to the flexible support system data and the data of the additional shock absorbing device, so as to obtain the structural reinforcement optimization data.

[0057] The present invention can effectively improve the stability of the structure and reduce damage or failures caused by vibration by adding support points to key geometric structure parts. In addition, the selection of a suitable flexible support system can effectively share the vibration energy and reduce the vibration amplitude of the equipment, thereby improving the overall operating performance of the equipment. The addition of a shock absorbing device can further reduce vibration transmission and suppress the resonance effect caused by high-frequency noise, especially having a significant effect on the optimization of high-frequency noise problems. Through the effective combination of the flexible support system and the shock absorbing device, the optimized structural reinforcement measures can significantly improve the vibration resistance of the equipment, reduce structural fatigue in long-term operation, extend the service life of the equipment, and realize dynamic adjustment and real-time optimization during the actual application of the equipment. These optimization schemes solve the problems of insufficient calculation accuracy and high cost of traditional methods, and can flexibly respond to different working conditions, give full play to the advantages of digital twin technology, and achieve continuous optimization of equipment performance.

[0058] Preferably, step S3 specifically comprises:

[0059] Step S31: performing acoustic simulation on the three-dimensional electrical equipment geometric model according to the equipment acoustic characteristic data, thereby obtaining acoustic simulation data;

[0060] Step S32: performing sound pressure distribution analysis on the acoustic simulation data to obtain sound pressure distribution data;

[0061] Step S33: performing noise source identification on the acoustic simulation data to obtain noise source data;

[0062] Step S34: performing abnormal noise analysis on the sound pressure distribution data according to the noise source data, thereby obtaining abnormal noise data;

[0063] Step S35: Perform equipment noise optimization control according to the abnormal noise data, so as to obtain equipment noise optimization data.

[0064] The present invention can effectively predict and optimize the noise problem of the equipment in the design stage by accurately analyzing the acoustic characteristics and noise source data of the equipment. In the acoustic simulation stage, a comprehensive acoustic simulation can be performed based on the geometric model of the three-dimensional electrical equipment to obtain detailed acoustic simulation data to help discover potential noise problems. On the basis of the sound pressure distribution analysis, the noise distribution of each component of the equipment can be intuitively identified, so as to optimize the structural design or adjust the material distribution in a targeted manner. Noise source identification further helps to locate the key areas where noise is generated, providing a basis for subsequent noise source control. By analyzing the abnormal noise data, it is possible to identify and analyze the abnormal noise problems that occur during the operation of the equipment, and make timely adjustments and optimizations, thereby avoiding the omission of high-frequency noise problems caused by inaccurate modeling in traditional methods. On these basis, the equipment noise optimization control scheme can effectively reduce the noise level of the equipment during operation, and has the ability of intelligent and real-time optimization, avoiding the defects of traditional methods that cannot adapt to changes in dynamic environments, thereby giving full play to the advantages of digital twin technology and realizing continuous noise control and optimization of the equipment during use. These innovative optimization measures help the equipment to achieve more efficient and low-noise operation in the design stage, greatly improving the equipment performance and user experience.

[0065] Preferably, step S32 is specifically:

[0066] Step S321: extracting sound wave propagation characteristics from acoustic simulation data to obtain sound wave propagation data;

[0067] Step S322: identifying the sound pressure area according to the sound wave propagation data, thereby obtaining the sound pressure area data;

[0068] Step S323: constructing a two-dimensional sound pressure distribution map according to the sound pressure area data, thereby obtaining a two-dimensional sound pressure distribution map;

[0069] Step S324: calculating the sound pressure level of the two-dimensional sound pressure distribution diagram to obtain sound pressure level data;

[0070] Step S325: mapping the sound pressure distribution of the three-dimensional electrical equipment geometric model according to the sound pressure level data, thereby obtaining sound pressure distribution data.

[0071] The present invention can extract key propagation features from acoustic simulation data through sound wave propagation feature extraction, providing a basis for subsequent sound pressure area identification and noise distribution analysis. The identification of sound pressure area data helps locate the noise source area in the equipment, making noise control more targeted and improving the accuracy of the analysis. The construction of a two-dimensional sound pressure distribution map further realizes the visualization of noise distribution, so that the noise intensity in different areas can be seen at a glance, providing an intuitive basis for optimization design. On the basis of sound pressure level calculation, accurate sound pressure level data can be obtained, providing a reliable quantitative standard for actual noise control. Finally, by mapping the sound pressure level data to a three-dimensional electrical equipment geometric model, the sound pressure distribution can be intuitively displayed, the equipment design can be further optimized, and the noise level can be effectively controlled. Compared with traditional methods, these steps fully consider the dynamic changes of the equipment during the noise analysis process, avoiding the problems of insufficient accuracy and high computational cost of traditional models when dealing with complex multi-physics field coupling systems. Through intelligent analysis and real-time data update, this method can not only improve the analysis accuracy, but also realize continuous noise monitoring and optimization of the equipment during operation, thereby maximizing the value of digital twin technology.

[0072] Preferably, step S35 is specifically as follows:

[0073] Step S351: performing noise equipment influence structure identification according to abnormal noise data, thereby obtaining noise equipment influence structure data;

[0074] Step S352: performing device housing optimization design on the noise device influencing structure data, thereby obtaining device housing optimization data;

[0075] Step S353: replacing the high damping support material of the equipment with respect to the equipment shell optimization data, thereby obtaining the high damping support material data of the equipment;

[0076] Step S354: Designing a sound insulation structure for the noise equipment impact structure data, thereby obtaining sound insulation structure data;

[0077] Step S355: Optimize and integrate equipment noise according to the equipment high-damping support material data and the sound insulation structure data, so as to obtain equipment noise optimization data.

[0078] The present invention can clarify the specific impact of the noise source on the equipment structure by identifying the structure affecting the noise equipment, laying the foundation for subsequent structural optimization and noise control. In the process of optimizing the design of the equipment shell, by accurately adjusting the shell structure, the noise propagation of the equipment can be effectively reduced, providing support for more efficient noise isolation. Replacing the high-damping support material further optimizes the noise absorption effect, thereby reducing the noise problem caused by vibration transmission, especially in the control of high-frequency noise. Through the design of the sound insulation structure, it is fundamentally possible to prevent the noise from propagating from the inside to the outside of the equipment, provide an additional noise isolation layer for the equipment, and further enhance the noise suppression effect. Combined with the high-damping support material and the sound insulation structure data, the equipment noise optimization integration solution can achieve more comprehensive noise control, so that the noise generated by the equipment during operation is effectively reduced, meeting higher noise control requirements. Compared with the traditional method, this process can respond to the noise changes of the equipment in real time through intelligent dynamic adjustment, avoiding the problems caused by the traditional method due to insufficient calculation accuracy and high calculation cost, and improving the accuracy and effect of noise optimization, so that the potential of digital twin technology in noise management can be more fully utilized. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0080] Figure 1 A schematic diagram of the steps of the method for constructing a digital twin model of the sound field of electrical equipment according to the present invention;

[0081] Figure 2 Detailed step flow diagram of step S1 in the present invention;

[0082] Figure 3 Detailed step flow diagram of step S2 in the present invention;

[0083] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0084] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0085] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0086] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0087] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for constructing a digital twin model of an electrical equipment sound field, the method comprising the following steps:

[0088] Step S1: acquiring electrical equipment structural design data, and constructing a three-dimensional electrical equipment geometric model according to the electrical equipment structural design data, thereby obtaining a three-dimensional electrical equipment geometric model;

[0089] In this embodiment, detailed structural design data of electrical equipment is collected. These data include information such as the shell size of the equipment, the position of internal components, and the material selection. The structural design of the electrical equipment is digitally modeled by CAD tools (such as AutoCAD, SolidWorks, etc.), and a three-dimensional geometric model of the equipment is generated based on the design data. In this process, the design data must ensure that the size and shape of each component are accurately described, and the connection relationship of the parts is set in detail according to the design requirements. By defining structural features, such as parameters such as shell thickness and strength, the three-dimensional electrical equipment geometric model can accurately reproduce the device structure in the computer. The key to this stage is to ensure that the proportion and size of each component are not deviated, and attention should be paid to the unification of units and the maintenance of proportional relationships during the model construction process. The construction process of the entire model depends on the input of structural data, and consistency with the design data must be maintained, and verification and feedback are performed through software simulation tools.

[0090] Step S2: Acquire equipment acoustic characteristic data, and perform multi-material sound wave propagation analysis on the three-dimensional electrical equipment geometric model based on the equipment acoustic characteristic data, thereby obtaining multi-material sound wave propagation data; perform structural design optimization on the three-dimensional electrical equipment geometric model based on the multi-material sound wave propagation data, thereby obtaining equipment structural design optimization data;

[0091] In this embodiment, the acoustic characteristic data of the device are obtained, which include but are not limited to the acoustic impedance, density, rigidity and other characteristic data of the device shell material. These acoustic characteristic data are usually obtained by experimental determination or from standard documents. When performing multi-material sound wave propagation analysis, the three-dimensional geometric model of the device is first discretized, divided into multiple small units, and corresponding acoustic material properties are assigned to each unit. Then, the finite element analysis (FEA) software (such as ANSYS, COMSOL, etc.) is used to simulate the sound wave propagation of the three-dimensional geometric model, and the propagation path, propagation speed and attenuation characteristics of the sound wave between different materials are calculated. During the sound wave propagation analysis process, parameters such as the frequency range and the acoustic impedance of the material need to be set. The frequency range is usually selected from 20Hz to 20kHz to cover the sound spectrum generated when the device is working. Based on the simulation data of sound wave propagation, the difference in the sound wave attenuation effect and the sound wave propagation speed between the materials is further analyzed, and the structural design of the three-dimensional geometric model is optimized to ensure that the acoustic characteristics of the device meet the design requirements.

[0092] Step S3: performing acoustic simulation on the three-dimensional electrical equipment geometric model according to the equipment acoustic characteristic data, thereby obtaining acoustic simulation data; performing abnormal noise analysis on the acoustic simulation data, thereby obtaining abnormal noise data; performing equipment noise optimization control according to the abnormal noise data, thereby obtaining equipment noise optimization data;

[0093] In this embodiment, based on the acoustic characteristic data of the device, a simulation model is set in the software (such as COMSOL Multiphysics, ANSYS, etc.). Select an appropriate acoustic module for simulation to calculate the sound pressure distribution of different parts of the device. During the simulation process, the material acoustic properties of the device are combined with the spatial distribution of the geometric model to obtain the acoustic response of the device when it is working. Next, the obtained acoustic simulation data is subjected to abnormal noise analysis, the area where the sound is abnormally increased is identified, and its frequency and intensity are recorded. For the analysis of abnormal noise, it is necessary to perform statistical analysis on each frequency point, and a threshold value (such as 80dB) is usually set. Noise exceeding this threshold value is considered to be abnormal noise. Abnormal noise data is used to optimize the noise control of the device, select appropriate silencing or shock absorbing materials, and redesign the shell and internal structure of the device to reduce noise generation. During the optimization process, the propagation path and intensity of the sound are mainly reduced by changing the structural geometric parameters, material selection, etc., to ensure that the noise level is within a reasonable range.

[0094] Step S4: construct a digital twin model of the sound field of the electrical equipment according to the equipment noise optimization data and the equipment structure design optimization data, so as to obtain a digital twin model of the sound field of the electrical equipment.

[0095] In this embodiment, the digital twin model needs to include the three-dimensional geometric information, acoustic characteristics and noise control scheme of the equipment. First, the three-dimensional geometric model of the equipment is integrated with the acoustic simulation results to ensure that the material and structural characteristics of each part are consistent with the acoustic simulation data. Then, according to the equipment noise optimization control data, the design of the housing and internal components of the equipment is fine-tuned to optimize the structure and acoustic characteristics. Finally, through digital twin technology, virtualization technology is used to build a digital model of the equipment in a computer, which can simulate the performance of the equipment in an actual working environment. The digital twin model should include dynamic analysis of noise sources and improved design of the equipment housing. In this way, the acoustic characteristics of the equipment can be fully evaluated before the actual production of the product, and the structure can be optimized or adjusted as needed.

[0096] Preferably, step S1 specifically comprises:

[0097] Step S11: acquiring electrical equipment structural design data, and performing equipment housing design feature extraction and internal component design feature extraction based on the electrical equipment structural design data, thereby obtaining equipment housing design data and internal component design data;

[0098] In this embodiment, the structural design data of the electrical equipment is obtained. The data includes the shape, size, material of the equipment shell, and the configuration and functional requirements of the internal components. The shell design feature extraction is to analyze the shell structural design data of the electrical equipment, identify the geometric features, opening position, thickness and other parameters of the shell, and convert them into a software format suitable for modeling. These data can be obtained by manually analyzing the structural drawings or by extracting detailed design information from CAD files. At the same time, the internal component design feature extraction focuses on the various components inside the device, mainly extracting the structural features and dimensions of components such as motors, radiators, and batteries, and integrating them through the interface relationship with the shell design data. The shell design data includes thickness, material type, etc., and the internal component design data includes the position, size and working characteristics of each component. Finally, the shell design data and the internal component design data will be obtained separately to ensure that these design data are consistent with the functional requirements of the equipment.

[0099] Step S12: Modeling the geometric shape of the housing based on the device housing design data, thereby obtaining housing geometric shape modeling data;

[0100] In this embodiment, based on the device housing design data, it is first necessary to model the geometric shape of the housing. The key to this step is to build the geometric shape of the housing through modeling software (such as SolidWorks, AutoCAD, etc.) according to the specific size, shape and opening position in the housing design data. In this process, the basic size parameters such as the length, width, and height of the housing are first input, and these dimensions are obtained through design drawings or CAD files. Then, the opening position and shape of the housing are set according to the design requirements, including holes and gaps of components such as vents, interfaces, and buttons. The geometric shape modeling data of the housing should include the size, position, and proportional relationship of all shape features. For example, if the housing design requires a certain radian curve or chamfer design, these parameters need to be accurately set during modeling. Finally, a three-dimensional geometric model of the housing is obtained, which can completely restore the design requirements and ensure consistency with the device function and shape requirements.

[0101] Step S13: assigning material properties to the shell geometry modeling data according to the device shell design data, thereby obtaining shell material property modeling data;

[0102] In this embodiment, appropriate material properties are extracted and assigned from the design to ensure that the housing has suitable physical properties. First, the appropriate material type is selected according to the housing design data, such as aluminum alloy, plastic, steel, etc. The physical properties of each material, such as density, elastic modulus, thermal conductivity, etc., are obtained through a standard material database or searched from known literature. These material properties are combined with the geometric shape of the housing, and corresponding material properties are assigned to each area or component in the modeling software. In particular, different parts of the housing (such as ventilation areas, interface areas, etc.) use different materials, and the assigned material properties should be consistent with the actual design requirements. Finally, all material properties should be accurately assigned to ensure that the performance of different materials at work can be accurately reflected in subsequent analysis.

[0103] Step S14: setting the shell thickness according to the shell material property modeling data, thereby obtaining the device shell modeling data;

[0104] In this embodiment, it is necessary to comprehensively consider the functional requirements of the equipment and various factors of the external environment, such as operating temperature, operating pressure, mechanical load, vibration, etc. The combined effect of these factors will determine the thickness range of the shell. The operating temperature will affect the thermal expansion coefficient of the shell material, the operating pressure will determine whether the shell needs to have a stronger pressure resistance, and the mechanical load and vibration require that the shell has sufficient protection in strength and rigidity. For these external environmental parameters, they are usually calculated by empirical formulas or engineering standards, or selected by referring to the design parameters of similar products. For different application scenarios and working conditions, the setting of the shell thickness needs to be combined with specific material mechanical properties, such as tensile strength, compressive strength, yield strength, etc. These material performance data are usually obtained by material testing or consulting relevant material manuals. For example, for aluminum alloys, the tensile strength is about 250MPa, while the tensile strength of steel reaches 500MPa. For the shell thickness setting of the equipment, it is usually necessary to make a reasonable selection based on the tensile strength and compressive strength of the material to ensure that the shell can withstand the corresponding load during use and does not fatigue or break under long-term use. In addition to material strength, heat conduction requirements are also an important factor affecting thickness setting. Especially for equipment in high temperature environments, the shell thickness needs to meet good thermal conductivity to ensure the heat dissipation requirements of the equipment. Durability and service life are also key factors in determining the thickness of the shell. Especially for equipment exposed to harsh environments for a long time, the shell needs to have higher corrosion resistance and anti-aging performance. Therefore, when setting the shell thickness, in addition to considering the material's resistance to mechanical loads, its comprehensive performance requirements such as heat resistance and corrosion resistance should also be considered. In actual operation, different areas of the shell bear different loads. For example, the support area needs to increase the thickness to enhance its bearing capacity, while other areas that do not bear large loads can reduce the thickness appropriately. For the thickness setting of these areas, structural analysis and finite element analysis (FEA) can be used to verify to ensure that the shell will not be excessively deformed or cracked under load. Finally, the set thickness also needs to be verified and adjusted according to the working environment, service life and fatigue load of the shell to ensure that it meets all safety and reliability requirements. In terms of numerical setting, calculation and simulation tools are usually used for verification. For example, the tensile strength and compressive strength of the material are used to calculate the initial thickness range, and then fine-tuned according to environmental factors and load conditions to ensure that the design of the housing can maintain long-term stability and reliability under various working conditions. Step S15: Modeling the motor based on the internal component design data to obtain motor modeling data;

[0105] In this embodiment, the design data of the motor needs to include information such as the type of motor (such as DC motor, AC motor, etc.), power, operating voltage, size, etc. According to these design data, the three-dimensional modeling of the motor is performed in the motor modeling software. In this process, it is necessary to set the structure of the motor, including the position and size of components such as the rotor, stator, bearing, and cooling system. The material and structural dimensions of each component are closely related to the performance requirements of the equipment and must be adjusted according to the design requirements. For the rotor and stator of the motor, the model needs to include information such as magnetic field distribution and winding density. The motor modeling data will provide complete motor component layout and geometric data for subsequent integration and analysis. Finally, the three-dimensional model of the motor should meet the design requirements and provide accurate data support for subsequent integration.

[0106] Step S16: constructing a three-dimensional geometric model of the electrical equipment according to the equipment housing modeling data and the motor modeling data, thereby obtaining a three-dimensional geometric model of the electrical equipment.

[0107] In this embodiment, the geometric shape of the housing is combined with the material property data, and the three-dimensional modeling data of the motor is merged with the housing. In the modeling software, the positional relationship between the housing and the motor is adjusted according to the layout requirements of the equipment to ensure that the motor is installed in the appropriate position in the housing. When constructing a three-dimensional geometric model, the interface position, size and connection method of the housing and the motor need to be accurately set according to the actual design requirements to avoid inappropriate space or interference. In the merging process, factors such as the heat dissipation, drive circuit and power management of the equipment also need to be considered. The entire three-dimensional electrical equipment geometric model should accurately reflect the structural design of the equipment to ensure that the housing and internal components can be reasonably matched. Finally, through this modeling step, a complete three-dimensional geometric model of the electrical equipment is obtained, which provides basic data for subsequent manufacturing and assembly.

[0108] Preferably, step S2 specifically comprises:

[0109] Step S21: Acquire device acoustic characteristic data, and perform sound propagation path feature extraction and sound propagation intensity feature extraction based on the device acoustic characteristic data, thereby obtaining sound propagation path data and sound propagation intensity data;

[0110] In this embodiment, the acoustic characteristic data of the device is obtained by measuring the acoustic characteristics of the device housing, internal components and working environment. The acoustic characteristics of the device include the propagation speed of sound waves in different materials, absorption coefficient, reflection coefficient and scattering coefficient, etc. In order to extract the characteristics of the sound propagation path, an acoustic sensor is used to arrange measurement points at different positions of the device to record information such as the propagation direction, propagation distance and propagation time of the sound wave, and then determine the propagation path of the sound wave. The sound propagation intensity characteristics are obtained by measuring the vibration response of the surface or each component of the device, and using a sensor (such as an accelerometer or vibration sensor) to record the vibration data caused by the sound, and then calculate the sound wave propagation intensity. All data must be calibrated according to specific standards, and the calibration tools used include precision frequency analyzers, audio signal generators, etc.

[0111] Step S22: inputting the sound propagation path data and the sound propagation intensity data into the three-dimensional electrical equipment geometric model, and using simulation software to simulate the sound wave propagation, thereby obtaining the sound wave propagation simulation data;

[0112] In the present embodiment, the sound propagation path data and propagation intensity data obtained in step S21 are integrated with the three-dimensional electrical equipment geometric model to form a model of multi-physics field coupling. Use simulation software (such as ANSYS, COMSOL Multiphysics, etc.) to simulate the propagation of sound waves, and input the geometry, material properties, acoustic characteristics data, propagation path and intensity data of the equipment. The simulation software will simulate the propagation of sound waves in the equipment according to the geometry of the equipment, the acoustic properties of the material and the external working environment conditions. During the simulation process, it is necessary to set parameters such as the sound wave frequency range, wavelength, and the absorption characteristics of the material. Setting the frequency range is generally based on the operating frequency of the equipment and its acoustic response range, such as 0-20kHz, to meet the needs of different application environments.

[0113] Step S23: performing multi-material attenuation analysis according to the acoustic wave propagation simulation data, thereby obtaining multi-material acoustic wave attenuation data;

[0114] In this embodiment, the multi-material attenuation analysis needs to consider the influence of different materials on the propagation of sound waves, mainly including the absorption coefficient and scattering coefficient of the material. By analyzing the propagation of sound waves at the interfaces of various materials, the attenuation value of each material to the sound waves is calculated. The attenuation coefficient is calculated based on the physical properties of the material (such as density, Young's modulus, speed of sound, etc.) and the frequency and propagation path length of the sound waves. For composite materials, it is also necessary to calculate the attenuation effects of different levels separately. By setting a suitable threshold, such as -3dB attenuation, the effective attenuation effect of the material on the sound wave is determined, and the design is further optimized.

[0115] Step S24: evaluating the acoustic wave propagation velocity of the multi-material acoustic wave attenuation data, thereby obtaining the multi-material acoustic wave propagation data;

[0116] In this embodiment, the propagation speed of sound waves in different materials will be affected by factors such as material properties, temperature, and pressure. The propagation speed of sound waves in different materials is calculated through the time-distance relationship in the sound wave propagation simulation, and the influence of materials on the propagation of sound waves is evaluated. In the process of evaluating the propagation speed, it is necessary to consider the physical properties of the material such as elastic modulus, Poisson's ratio, and density, calculate the sound wave propagation speed at different frequencies according to the acoustic wave speed formula, and verify it through experimental data. Through these evaluation data, the speed changes that occur during the sound wave propagation process are further analyzed, and a basis is provided for subsequent optimization design.

[0117] Step S25: Optimizing the structural design of the three-dimensional electrical equipment geometric model based on the multi-material sound wave propagation data, thereby obtaining equipment structural design optimization data.

[0118] In this embodiment, by analyzing the sound wave propagation characteristics of various parts of the equipment, the areas that cause adverse noise or vibration are identified, and then the geometry, material selection and structural layout of the equipment are optimized. During the optimization process, multiple factors such as the overall strength, rigidity, acoustic properties and vibration characteristics of the equipment need to be considered. For example, for areas with strong sound wave propagation, it is necessary to increase the thickness of the material or replace it with a sound-absorbing material, or optimize the shell structure to reduce reflection and resonance. The goal of optimization is to ensure that the design of the equipment can minimize the impact of noise and vibration on the performance of the equipment while meeting the functional requirements. During the optimization design process, finite element analysis (FEA) software can be used for multi-objective optimization, and multiple rounds of iterative adjustments can be performed in combination with the acoustic simulation results to ensure that the final design meets the acoustic performance standards.

[0119] Preferably, step S23 is specifically:

[0120] Step S231: performing Fourier transform on the acoustic wave propagation simulation data to obtain acoustic wave propagation frequency domain data;

[0121] In this embodiment, it is necessary to perform Fourier transform on the sound wave propagation simulation data obtained in step S22. The purpose of Fourier transform is to convert the time domain signal into a frequency domain signal, so as to analyze the propagation characteristics of sound waves at different frequencies. In the specific operation, the time signal is converted into a frequency component by performing a fast Fourier transform (FFT) on the sound wave simulation data. In this process, the sampling rate of the Fourier transform needs to be set. Generally, the sampling rate should be at least twice the highest frequency of the signal to ensure the integrity of the signal. For example, if the highest frequency of the sound wave is 20kHz, the sampling rate should be set to above 40kHz. The Fourier transform result is frequency domain data, which represents the intensity distribution of the sound wave at different frequencies, and is convenient for the subsequent analysis of the propagation characteristics of each frequency band.

[0122] Step S232: extracting propagation material characteristics from the acoustic wave propagation simulation data, thereby obtaining propagation material data;

[0123] In this embodiment, based on the sound wave propagation simulation data, the characteristic data of various propagation materials involved are extracted. The characteristics are mainly extracted from the acoustic impedance, density, Young's modulus, elastic modulus, absorption coefficient and other aspects of the material. In order to obtain these characteristic data, calculations are first performed based on the geometric model of the equipment, the material hierarchy and the corresponding acoustic properties. The acoustic impedance of each material is obtained by multiplying its density and the speed of sound, and these parameters are usually determined by the physical properties of the material. In actual operation, for each material, a known acoustic formula is used to calculate its acoustic properties. For example, the propagation characteristics of sound waves in different materials such as steel and plastic will be different. For the interface of each material, the interface contact impedance and the acoustic characteristics of the contact surface are used to judge the propagation behavior of the sound wave in this area, and the characteristic data of each propagation material is further established.

[0124] Step S233: selecting an attenuation model based on the propagation material data, thereby obtaining an attenuation model;

[0125] In this embodiment, a suitable attenuation model is selected by analyzing the acoustic characteristic data of each material. The basis for selecting the attenuation model mainly includes the relationship between the absorption characteristics of the material and the frequency of the sound wave. Common attenuation models include exponential attenuation models, linear attenuation models, etc., and different attenuation modes are selected according to different materials and frequency bands. For example, for high-frequency sound waves, an exponential attenuation model is usually used to describe them because high-frequency sound waves suffer large losses during propagation; while for low-frequency sound waves, a linear attenuation model can be used. During operation, it is necessary to analyze all materials on the sound wave propagation path, calculate their effects on the sound waves one by one, and select the corresponding attenuation model based on the calculated attenuation coefficient. The parameters of the attenuation model are set based on experimental data, material properties, and known standards. The parameters that need to be set include the attenuation constant, wavelength range, absorption coefficient of the material, etc.

[0126] Step S234: performing frequency domain energy attenuation analysis on the sound wave propagation frequency domain data according to the attenuation model, thereby obtaining sound wave frequency domain energy attenuation data;

[0127] In this embodiment, the energy attenuation values ​​at different frequencies are calculated based on the frequency domain data of the sound wave in combination with the selected attenuation model. The attenuation value is determined by factors such as the absorption coefficient of the material, the wave velocity, the propagation path, and the frequency. In the specific implementation process, it is necessary to set the frequency range of the sound wave, such as 0-20kHz, and subdivide it into multiple frequency bands for analysis. For each frequency band, the corresponding energy loss is calculated based on the attenuation coefficient of the material. Attenuation analysis usually uses an integral method to accumulate the energy attenuation of each frequency band to obtain the total energy loss data. The attenuation amount of each frequency band depends on the thickness of the material, the frequency of the sound wave, and the propagation path of the sound wave. The attenuation data will provide a basis for subsequent design optimization.

[0128] Step S235: performing material interface reflection loss analysis on the sound wave propagation frequency domain data, thereby obtaining sound wave propagation material interface reflection loss data;

[0129] In this embodiment, it is necessary to analyze the material interface reflection loss during the sound wave propagation process. The material interface reflection loss is usually caused by the reflection and refraction effects of sound waves at the interface of different materials. This analysis needs to calculate the reflection loss of the sound wave using the reflection coefficient based on the difference in acoustic impedance of the materials. In the specific operation, the reflection coefficient of each interface is first calculated. The reflection coefficient can be calculated by the formula R = (Z2-Z1) / (Z2+Z1), where Z1 and Z2 are the acoustic impedances of the two materials, respectively. By calculating the reflection loss of each material interface, the reflected energy at each interface is obtained. Reflection loss usually affects the propagation efficiency of sound waves, so it must be calculated accurately. The data of interface reflection loss is crucial for optimizing material layout and reducing unnecessary energy loss.

[0130] Step S236: performing interface correction on the sound wave frequency domain energy attenuation data according to the sound wave propagation material interface reflection loss data, thereby obtaining multi-material sound wave attenuation data.

[0131] In this embodiment, since the sound waves will have reflection losses at the interfaces of different materials, the original frequency domain energy attenuation data needs to be adjusted. In the specific implementation, a weighted correction method is adopted to perform weighted averaging on the energy attenuation data according to the reflection coefficient to calculate the corrected attenuation value. This process mainly considers the reflection characteristics of the material interface, and the corrected energy attenuation data will more accurately reflect the energy loss in the real propagation environment. The key to interface correction is to accurately calculate the acoustic impedance differences of each material, and adjust the frequency domain data according to the attenuation model to ensure that the multi-material sound wave attenuation data finally obtained has high accuracy and practical significance.

[0132] Preferably, step S25 is specifically as follows:

[0133] Step S251: using finite element analysis software to solve the free vibration of the three-dimensional electrical equipment geometric model, thereby obtaining free vibration data of the geometric model;

[0134] In this embodiment, the geometric model of the three-dimensional electrical equipment needs to be imported into the finite element analysis software. Common geometric data formats include STEP or IGES formats. In the analysis, the physical properties of the model need to be defined, including the density, Young's modulus, Poisson's ratio, etc. of the material. When setting these parameters, the standard data of the material used in the equipment should be obtained. If it is steel, the density of the standard steel should be used (for example, 7.85g / cm 3 ), Young's modulus (about 200GPa), and other data. When solving free vibration, define boundary conditions and constraints. Usually, the support points or fixed points of the equipment are boundary conditions. Set the solver parameters, including calculation accuracy and step size, and select a suitable solution algorithm (such as direct method or iterative method). Finally, perform frequency response analysis on the equipment to obtain free vibration frequency and mode shape data. The vibration frequency and mode shape of each mode will serve as the basic data for subsequent analysis.

[0135] Step S252: performing harmonic response analysis on the three-dimensional electrical equipment geometric model based on the multi-material sound wave propagation data, thereby obtaining sound wave vibration data;

[0136] In this embodiment, by understanding the natural frequency and vibration mode of the geometric model of the equipment, a frequency band related to the sound wave frequency range (for example, 20 Hz to 10 kHz) is selected for analysis. Next, the harmonic load input, that is, the sound wave propagation simulation data, is applied to the appropriate position of the equipment model. The isotropic or anisotropic properties of the material need to be set in the acoustic vibration model, including the damping ratio, sound velocity, etc. of the material. By setting the analysis range, selecting the calculation accuracy (such as step size and frequency resolution) and running the solver for calculation. The results obtained include the vibration response data of the equipment at a specific frequency, that is, the displacement, velocity and acceleration data of each point, which are used to further analyze the vibration characteristics.

[0137] Step S253: performing vibration resonance effect analysis according to the free vibration data of the geometric model and the acoustic vibration data, thereby obtaining vibration resonance effect data;

[0138] In this embodiment, based on the frequency and vibration shape of each mode in the free vibration data, it is determined whether the sound wave vibration frequency resonates with the natural frequency of the device. Using the frequency matching method, the sound wave vibration frequency is compared with the natural frequency of the device one by one to check whether there is a resonance phenomenon. When the frequency of the sound wave is close to or equal to a certain natural frequency of the device, a resonance phenomenon occurs, causing the vibration amplitude to increase sharply. For each resonance point, its frequency, displacement amplitude and other information are recorded in detail. Furthermore, by comparing the vibration shape and sound wave vibration data of each mode, the affected area and the propagation path of the vibration are analyzed, and finally the vibration resonance effect data is obtained for subsequent optimization design.

[0139] Step S254: performing structural reinforcement optimization on the three-dimensional electrical equipment geometric model according to the vibration resonance effect data, thereby obtaining structural reinforcement optimization data;

[0140] In this embodiment, the parts of the equipment where resonance occurs are identified, which are usually manifested as areas with large vibration amplitudes or strong local vibrations. For these areas, optimization is performed by adding reinforcements, increasing structural stiffness or changing materials. Reinforcement can be achieved by adding support points, thickening materials or using high-strength alloy materials. During implementation, the scope and position of the reinforcement area are set according to the intensity and distribution of the resonance effect. For example, for the position with the largest vibration (such as the motor support part), thicker structural parts or reinforced connectors can be added. The reinforcement plan needs to be verified through multiple finite element analyses to ensure that the vibration amplitude after reinforcement is within a safe range, so as to obtain optimized structural reinforcement data.

[0141] Step S255: superimposing sound-absorbing materials on the three-dimensional electrical equipment geometric model according to the vibration resonance effect data, thereby obtaining sound-absorbing material optimization data;

[0142] In this embodiment, suitable sound-absorbing materials, such as high-density foam, rubber gasket, fiber material, etc., are selected for areas with strong vibration or obvious resonance. These materials should have good sound wave absorption characteristics, and the specific selection is based on the frequency range of sound wave propagation and the sound absorption coefficient of the material. During implementation, it is necessary to measure the sound absorption performance of each sound-absorbing material. The sound absorption coefficient can usually be obtained through experimental data, and the sound absorption coefficient should be close to 1 in the high-frequency range. In the model, the sound-absorbing material is superimposed on the resonance area, and the sound absorption effect of the material is evaluated by finite element analysis. The thickness and layout of the material are adjusted to gradually obtain the optimal sound absorption scheme, and finally form the optimization data of the sound-absorbing material.

[0143] Step S256: Perform equipment structure design optimization integration based on the structural reinforcement optimization data and the sound-absorbing material optimization data, so as to obtain equipment structure design optimization data.

[0144] In this embodiment, the sound-absorbing material is combined with the structural reinforcement scheme, and the sound-absorbing material and the reinforcement structure are reasonably arranged in the equipment design. For example, while reinforcing the structure in the resonance area, a sound-absorbing layer is added to jointly reduce the vibration amplitude. Through multiple simulation calculations, it is ensured that the reinforcement and sound-absorbing measures cooperate with each other to achieve the best optimization effect. The balance between structural strength and weight must be considered during the optimization process to ensure that the optimized design does not cause the equipment to be too bulky or lack structural strength while ensuring the vibration control effect. Finally, the optimized structural design data is integrated to obtain the equipment structure design optimization data for production and manufacturing use.

[0145] Preferably, step S253 is specifically as follows:

[0146] Perform vibration frequency statistics based on the free vibration data of the geometric model to obtain free vibration frequency data;

[0147] In this embodiment, the free vibration frequency data of the geometric model is solved by finite element analysis to obtain the vibration frequency of the device in different modes. Assuming that the device is a robotic arm, multiple modal frequencies can be obtained, for example, the first modal frequency is 50Hz, the second modal frequency is 120Hz, etc. The data of different modal frequencies are processed by statistical methods to calculate the average frequency, standard deviation, maximum frequency and other parameters of all modes. When calculating statistics, the standard deviation calculation formula is used:

[0148]

[0149] Among them, f i is the frequency of the ith mode, f is the average frequency, and n is the number of modes. Finally, the free vibration frequency data is generated, and the frequency value of each mode and its corresponding vibration shape are recorded.

[0150] Performing vibration frequency statistics on the sound wave vibration data, thereby obtaining the sound wave vibration frequency data;

[0151] In this embodiment, the sound wave propagation process is simulated to obtain the sound wave vibration data. Assuming that the sound wave propagation simulation results show that the frequency range is 20Hz to 10kHz, the sound wave vibration data is statistically analyzed within this frequency band. In specific implementation, the frequency domain data is Fourier transformed to obtain the vibration amplitude at each frequency. Then, the same frequency statistical method is used to calculate the mean value, standard deviation and minimum and maximum values ​​of the vibration data at each frequency. For example, the statistics of the frequency range from 20Hz to 1kHz can use the frequency domain analysis tool to perform FFT (fast Fourier transform) on the signal to obtain the amplitude information of each frequency component, thereby obtaining the sound wave vibration frequency data.

[0152] Resonance frequency matching is performed according to the free vibration frequency data and the acoustic wave vibration frequency data, thereby obtaining resonance frequency data;

[0153] In this embodiment, the free vibration frequency data and the acoustic wave vibration frequency data are matched. The frequencies of the two are compared through the frequency matching algorithm to find similar or equal frequency points. For example, if the free vibration frequency of the device is 50Hz and the acoustic wave vibration frequency is 49.8Hz, the two frequency points will be considered to be close, resulting in a resonance effect. The threshold for resonant frequency matching can be set to ±1Hz, which means that only when the difference between the two frequencies is less than 1Hz, the two are considered to resonate. By comparing all the frequency points one by one, the resonant frequency data is finally obtained, and each pair of resonant frequencies and their corresponding modes and vibration intensities are recorded.

[0154] Perform time statistics according to the resonance frequency data to obtain resonance time data;

[0155] In this embodiment, the running time of the device and the vibration frequency data are combined to record whether the device resonates in different time periods. Time statistics can be achieved by a vibration signal acquisition device, setting a sampling frequency (for example, 1kHz), and analyzing whether there is a signal matching the resonance frequency in each time period. For example, if the device vibrates for 10 seconds at a resonance frequency of 50Hz during operation, it is recorded as the resonance time. The resonance time data is obtained by performing a statistical analysis of the resonance duration of all resonance frequencies. Statistical indicators include the number of times the resonance occurs, the duration of each resonance, and the frequency and location of the resonance.

[0156] Performing vibration amplitude analysis on the free vibration data of the geometric model according to the resonance time data, thereby obtaining free vibration amplitude data;

[0157] In this embodiment, the vibration amplitude of each resonant frequency can be determined by monitoring the signal amplitude of the vibration sensor. For example, when the resonant frequency of the device is 50Hz, the vibration displacement at this frequency is recorded, and the measurement range of the vibration amplitude is set to 0-10mm. Using the resonance time data, the maximum vibration amplitude during the resonance period is obtained through time domain analysis, and the analysis conditions are adjusted according to the working state of the device. During the vibration amplitude analysis process, the amplitude data needs to be compared with the standard range, and the amplitude value of each frequency point is output to obtain the free vibration amplitude data.

[0158] Performing vibration amplitude analysis on the acoustic wave vibration data according to the resonance time data, thereby obtaining the acoustic wave vibration amplitude data;

[0159] In this embodiment, the resonance time data is used to analyze the amplitude of the sound wave vibration, especially the vibration amplitude at the resonant frequency. Assuming that the device is at a resonant frequency of 50Hz, the amplitude of the sound wave vibration is 1.2mm, the vibration amplitude of the sound wave can be monitored in real time by setting a vibration sensor in the sound wave propagation area. According to the record of the resonance time data, the maximum vibration amplitude value in the time period is obtained during each time period when resonance occurs. For example, if the vibration amplitude at a frequency of 50Hz is 1.5mm and lasts for 5 seconds, the data is recorded and subsequently analyzed. Finally, the sound wave vibration amplitude data is output, including the vibration amplitude at each frequency and its duration.

[0160] The vibration amplitude range is counted according to the free vibration amplitude data and the acoustic wave vibration amplitude data, so as to obtain the vibration amplitude range data;

[0161] In this embodiment, two sets of amplitude data are summarized to calculate the maximum value, minimum value, average value, standard deviation and other parameters. Assuming that the maximum value of the free vibration amplitude is 5mm and the minimum value is 0.5mm, and the maximum value of the acoustic vibration amplitude is 2.5mm and the minimum value is 0.2mm, the amplitude range is calculated through these two data sets. In specific implementation, the amplitude range can be set to the difference between the maximum amplitude and the minimum amplitude to obtain the vibration amplitude range data.

[0162] The vibration resonance effect is integrated according to the resonance frequency data and the vibration amplitude range data, so as to obtain the vibration resonance effect data.

[0163] In this embodiment, based on the resonant frequency data, determine which frequencies will cause the device to resonate, and correspond the corresponding vibration amplitude range data to them. For example, if the resonant frequency is 50Hz and the vibration amplitude range is 1mm-5mm, it is necessary to evaluate the response of the device under this frequency range. All resonant frequencies and their corresponding vibration amplitude range data are integrated into a set of data sets to form vibration resonance effect data. These data are used to guide the optimal design of the equipment and reduce unnecessary vibration effects.

[0164] Preferably, step S254 is specifically as follows:

[0165] According to the vibration resonance effect data, the key geometric structure parts of the three-dimensional electrical equipment geometric model are identified to obtain the key geometric structure parts data;

[0166] In this embodiment, the vibration resonance effect data of the device is used to analyze its frequency response characteristics, especially the vibration amplitude of the device at each resonant frequency. By identifying the maximum vibration amplitude area at these vibration frequencies, the key geometric parts related to the operation of the device are determined. Taking the structure of the device as an example, assuming that at a resonance frequency of 50Hz, the maximum vibration amplitude of the device occurs in the support point and connection point area, these parts are identified as key geometric structure parts. On this basis, the geometric model of the device is analyzed through 3D modeling software, such as SolidWorks or AutoCAD, these key parts are calibrated, and their specific geometric parameters, such as length, width, thickness and angle, are extracted as key geometric structure part data.

[0167] Add support points to the key geometric structure data, so as to obtain support point addition data;

[0168] In this embodiment, the support requirements of key geometric structure parts are analyzed based on the data of these parts, especially those parts that are subject to large vibrations and stresses. In specific implementation, in the geometric model of the equipment, by calculating the vibration amplitude and stress distribution, appropriate parts are selected to add support points. For example, when it is found that there is a large vibration at the support point near the base of the equipment, the optimal position for adding support points is determined by calculating the load and stress conditions at that position. The number, position and size of the additional support points are optimized based on the vibration resonance effect data. The parameters of the support points can be set according to the structural load requirements. For example, the position of the support points should be set in an area with a large vibration amplitude, the support point spacing can be set to 100mm, and the load-bearing capacity of each support point is calculated by stress analysis to obtain the support point addition data.

[0169] Select the flexible support system for the support point addition data, so as to obtain the flexible support system data;

[0170] In this embodiment, the flexible support system should be designed according to parameters such as the working environment, vibration frequency and vibration amplitude of the equipment. In the selection process, the vibration frequency range of the equipment and the motion characteristics of the support points are first determined, and the vibration response after the support points are added is analyzed through dynamic analysis and finite element analysis (such as ANSYS). For example, assuming that the addition of support points can effectively reduce the vibration amplitude at a vibration frequency of 50Hz, a flexible support material that meets this frequency response requirement is selected. The selection of flexible support systems includes rubber pads, springs or other vibration absorbing materials, and their stiffness, damping coefficient and elastic modulus should meet the vibration control requirements of the equipment. The key parameters in the selection process include elastic modulus, damping ratio and maximum load-bearing capacity. These parameters can be obtained through material experiments and matched with the working frequency range of the equipment to finally form flexible support system data.

[0171] Adding a shock absorbing device to the data of key geometric structure parts, thereby obtaining the data of adding the shock absorbing device;

[0172] In this embodiment, the transmission path of the vibration source is calculated and simulated to determine the optimal installation position of the shock absorber. For example, when the vibration frequency is 50Hz and 100Hz, the vibration amplitude of certain parts of the equipment, such as the connection bearings, is large, and a shock absorber needs to be added. The type of shock absorber can be selected according to actual needs. Common shock absorbers include spring shock absorbers, hydraulic shock absorbers, etc. When selecting, it is necessary to consider their compatibility with the equipment and the applicable vibration frequency range. The technical parameters of the shock absorber, such as the damping coefficient and load bearing capacity, should be set according to the vibration analysis data, and the effectiveness of the device should be verified by stress analysis. After setting the installation position of the shock absorber, the required size and model of the device are further calculated to finally obtain the data for adding the shock absorber.

[0173] The structural reinforcement optimization and integration is carried out according to the flexible support system data and the data of the additional shock absorbing device, so as to obtain the structural reinforcement optimization data.

[0174] In this embodiment, the structural reinforcement optimization of the equipment is performed in combination with the data of the flexible support system and the data of the additional shock absorbing device. The final structural reinforcement plan is determined by comprehensively analyzing the configuration of the flexible support system and the shock absorbing device. For example, if the vibration amplitude in certain key parts is large and cannot be effectively reduced by adding support points and shock absorbing devices, it is possible to choose to further reinforce the structure of this part. During the reinforcement process, suitable materials (such as high-strength steel or aluminum alloy) are used for structural reinforcement, especially in stress concentration areas. Through stress analysis and fatigue life prediction, it is ensured that the reinforced structure can effectively improve the strength and stability of the equipment. The parameter settings for structural reinforcement include the thickness, material and reinforcement method of the reinforced parts, such as adding support beams or increasing the number of welding points. These reinforcement measures are optimized and integrated based on the vibration resonance effect data and the actual operation of the equipment, and finally form complete structural reinforcement optimization data.

[0175] Preferably, step S3 specifically comprises:

[0176] Step S31: performing acoustic simulation on the three-dimensional electrical equipment geometric model according to the equipment acoustic characteristic data, thereby obtaining acoustic simulation data;

[0177] In this embodiment, it is necessary to obtain the acoustic characteristic data of the device, including the position of the sound source, the acoustic impedance of the material, the geometric dimensions of the device, etc. Use an acoustic simulation tool (such as COMSOL Multiphysics or ANSYS Sound) to import the three-dimensional geometric model of the device into the simulation software, and set appropriate boundary conditions and excitation sources. During specific operations, the simulation frequency range should be determined, and the operating frequency range of the device is usually selected, such as 20Hz to 20kHz. During simulation, it is necessary to consider the vibration and sound wave propagation characteristics of each component, and use the finite element method (FEM) to simulate the sound field to obtain the acoustic response of the device at different frequencies. During the simulation process, the parameters set include the power of the sound source, the transfer function, the sound absorption coefficient of the equipment material, etc. The simulation result is the acoustic simulation data of the device in the working state, which provides the sound pressure distribution and sound field conditions at different positions of the device.

[0178] Step S32: performing sound pressure distribution analysis on the acoustic simulation data to obtain sound pressure distribution data;

[0179] In this embodiment, the sound pressure field data in the simulation results is selected, and a suitable analysis area is set, such as the device housing, key operating parts, etc. According to the simulation data, the sound pressure distribution diagram is drawn to determine the high and low distribution of the sound pressure. By analyzing the distribution of the sound pressure on the surface or inside the device, the sound pressure value of each point can be obtained. The sound pressure level is calibrated using the equal sound pressure line technology, and the sound pressure threshold is usually set to 85dB to determine the area with greater noise impact. In this process, the parameters involved include the sound pressure value, sound pressure amplitude and frequency response at each position, and finally the sound pressure distribution data is obtained through data visualization technology.

[0180] Step S33: performing noise source identification on the acoustic simulation data to obtain noise source data;

[0181] In this embodiment, acoustic simulation data is simulated by analyzing the acoustic characteristics of different parts of the equipment to identify the noise source. The spectrum analysis method is used to perform frequency domain analysis on the simulation data to identify the location of the noise source of each component in the equipment. During specific operation, the sound pressure fluctuations of different parts of the equipment are analyzed in the time domain to find areas with higher sound pressure and frequency components and identify them as potential noise sources. The determination of the noise source should take into account factors such as vibration, airflow or friction, especially high-amplitude, low-frequency parts (such as motors, gear systems, etc.) are usually noise sources. In the identification process, by calculating the sound source intensity at different frequencies in the equipment, the areas with the most concentrated sound sources are screened out, and the geometric positions of these areas and the characteristic parameters of the sound sources, such as frequency, power, etc., are recorded to finally form the noise source data.

[0182] Step S34: performing abnormal noise analysis on the sound pressure distribution data according to the noise source data, thereby obtaining abnormal noise data;

[0183] In this embodiment, by comparing the noise source data with the sound pressure distribution data, the abnormal noise area that is different from the conventional noise source is identified. For example, if the sound pressure value of a certain component is much higher than the surrounding area or its spectrum is abnormal (such as too high frequency components), then the area is identified as an abnormal noise source. The difference analysis method is used to calculate the difference between normal noise and abnormal noise. Usually a threshold is set (such as the sound pressure difference exceeds 10dB or the frequency component exceeds 50Hz), and the abnormal noise area is judged according to the threshold. This method can be used to identify noise sources that do not meet the design standards and obtain abnormal noise data, including the location, frequency characteristics, and sound pressure values ​​of the abnormal noise source.

[0184] Step S35: Perform equipment noise optimization control according to the abnormal noise data, so as to obtain equipment noise optimization data.

[0185] In this embodiment, the control scheme is determined by analyzing the location and frequency characteristics of the abnormal noise source. If the abnormal noise source is high-frequency noise, sound-absorbing materials or soundproof covers can be added to this part; if it is a low-frequency noise source, a muffler can be used or the frequency of the vibration source can be adjusted. During specific operations, appropriate control thresholds should be set according to the characteristics of the noise source, such as reducing the noise to below 70dB. By comparing with the standard noise level, the noise optimization target is set, for example, the overall noise level of the equipment is controlled below 85dB. During the optimization process, it is also necessary to adjust the implementation of measures according to the specific location and type of noise sources, such as adding a soundproof cover to the motor, adjusting the operating frequency or changing the component layout, and finally obtaining the equipment noise optimization data, including the implemented control measures, noise reduction effects and adjusted noise levels.

[0186] Preferably, step S32 is specifically:

[0187] Step S321: extracting sound wave propagation characteristics from acoustic simulation data to obtain sound wave propagation data;

[0188] In this embodiment, the sound wave propagation information in the acoustic simulation results is extracted, mainly including the propagation path of the sound wave, the propagation speed and the influence of different materials on the sound wave. For the simulation results, the operating frequency range of the device is first determined, and a frequency band (for example, 20Hz to 20kHz) is usually selected, and the corresponding sound wave propagation characteristics are extracted according to the frequency band. In the simulation model, the acoustic parameters of different materials are set, such as density, elastic modulus and sound speed. The characteristics of the propagation of sound waves in the geometric model of the device are calculated using wave equations (such as the Helmholtz equation), and the key parameters of the sound wave propagation path are extracted, such as propagation direction, attenuation coefficient, reflection and refraction effects, etc. Based on these data, a sound wave propagation path diagram is drawn and converted into sound wave propagation data, including the sound speed, propagation angle and attenuation rate of each path.

[0189] Step S322: identifying the sound pressure area according to the sound wave propagation data, thereby obtaining the sound pressure area data;

[0190] In this embodiment, the area with the strongest sound wave transmission, that is, the area with higher sound pressure, is identified. By analyzing the density, propagation distance and fluctuation amplitude of the propagation path, the area where the sound pressure is concentrated is marked. For example, if the sound pressure peak of the propagation path in a certain area exceeds the set threshold (for example, the sound pressure value exceeds 85dB), the area is identified as a high sound pressure area. In specific operations, a point-by-point analysis method can be used to divide the area based on the set sound pressure threshold (for example, 85dB or 90dB) using the sound pressure value of the extracted propagation path. For each identified area, the geometric coordinates, sound pressure peak value and its relationship with other parts of the equipment are recorded to obtain sound pressure area data. This data is used to determine which parts of the equipment produce stronger noise.

[0191] Step S323: constructing a two-dimensional sound pressure distribution map according to the sound pressure area data, thereby obtaining a two-dimensional sound pressure distribution map;

[0192] In the present embodiment, the two-dimensional view (such as a cross section) of the device is used as a basis to mark all identified sound pressure areas. The specific position and size of each sound pressure area are determined by its geometric coordinates and sound pressure value. In order to more clearly represent the sound pressure distribution, a color gradient method is adopted, in which high sound pressure areas use red, low sound pressure areas use green, and the entire device surface is colored according to the sound pressure data to form a two-dimensional sound pressure distribution map. In this process, appropriate graphics tools or visualization software (such as MATLAB or AutoCAD) are used to draw graphics to determine the visualization method of the sound pressure data. During specific operations, a sound pressure range (for example, 70dB to 120dB) is set, and color coding is performed based on this range to intuitively display the sound pressure differences in each area, and finally a two-dimensional sound pressure distribution map is obtained.

[0193] Step S324: calculating the sound pressure level of the two-dimensional sound pressure distribution diagram to obtain sound pressure level data;

[0194] In this embodiment, sound pressure data is extracted from the two-dimensional graph, and the sound pressure level formula is used to calculate the sound pressure level of each point. The sound pressure level calculation formula is: L = 20*log10(P / P0), where P is the sound pressure value of the point, and P0 is the reference sound pressure (usually 20μPa). Through this formula, each sound pressure value in the figure is converted to a sound pressure level to obtain the sound pressure level of each area. For each sound pressure area, its average sound pressure level, minimum sound pressure level, and maximum sound pressure level are calculated, and the sound pressure level characteristics of each area are judged based on these values. During the processing, a threshold value of the sound pressure level (such as 80dB or 85dB) can be set as a standard to further analyze which areas have exceeded the sound pressure standard. In this way, the sound pressure level data of each area of ​​the equipment is obtained for further analysis and optimization.

[0195] Step S325: mapping the sound pressure distribution of the three-dimensional electrical equipment geometric model according to the sound pressure level data, thereby obtaining sound pressure distribution data.

[0196] In this embodiment, the detailed geometric data of the three-dimensional equipment model is first obtained, and the model is discretized to correspond the sound pressure level data to each grid element or surface point on the surface or inside of the model. Next, each grid or surface point is calibrated according to the sound pressure level value, and the sound pressure data is mapped to the three-dimensional model using color annotation or contour lines to form a three-dimensional sound pressure distribution map. When mapping, a suitable display technology, such as 3D visualization technology (such as OpenGL or Unity3D), is used to combine the sound pressure level information with the surface texture of the model so that the sound pressure distribution map can be truly presented in three-dimensional space. Through this mapping process, complete sound pressure distribution data can be obtained to help locate noise sources and further optimize the design.

[0197] Preferably, step S35 is specifically as follows:

[0198] Step S351: performing noise equipment influence structure identification according to abnormal noise data, thereby obtaining noise equipment influence structure data;

[0199] In this embodiment, the identification process is based on the noise source in the abnormal noise data and the impact area it produces, and the noise peak area is selected as the analysis object. By analyzing the geometric structure of these areas, it is determined which components are most affected by the noise. For example, if the sound pressure value exceeds the set threshold (such as 85dB) and is located in certain equipment parts (such as motor housing, radiator, etc.), these parts can be judged as key areas affected by noise. During the operation, the equipment is subjected to vibration-acoustic coupling analysis using simulation software (such as ANSYS, COMSOL) to further determine the impact of the noise source on the equipment structure. Through this process, the specific structural data of the noise equipment impact is obtained, including information such as the impact part, the degree of impact, and the vibration resonance frequency.

[0200] Step S352: performing device housing optimization design on the noise device influencing structure data, thereby obtaining device housing optimization data;

[0201] In this embodiment, the design goals and optimization requirements of the shell are determined. According to the noise source and its impact area, the parameters such as the acoustic impedance, rigidity and vibration characteristics of the shell material are analyzed to identify which parts need to be optimized. For example, the shell thickness, material selection and sealing affect the noise propagation of the equipment. In the specific design process, the finite element analysis method (such as ABAQUS) is used to simulate the shell to optimize its geometry and material to reduce noise transmission. For the optimization of the shell, the focus is on adjusting the thickness and density of the material, selecting suitable sound insulation materials (such as high-density plastics or composite materials), and optimizing the design of the shell joints to reduce the propagation path and reflection of sound waves. The optimization results should include the new geometry, material properties and thickness of the shell.

[0202] Step S353: replacing the high damping support material of the equipment with respect to the equipment shell optimization data, thereby obtaining the high damping support material data of the equipment;

[0203] In this embodiment, the selection requirements of the support material are determined based on the noise source analysis and the equipment housing optimization data. High damping materials should have good energy absorption capacity and be able to effectively reduce the transmission of structural vibration. When making specific selections, parameters such as the damping coefficient, hardness and durability of the support material need to be considered. For example, polyurethane, rubber or specific synthetic composite materials are used, and the damping coefficients of these materials are generally between 0.1 and 0.3. In actual operation, a vibration test bench is used to test the damping effects of different materials to select materials that meet the requirements. When replacing the support material, measure and record key data such as the thickness, density, and damping coefficient of the material to ensure that the performance of the support system meets the requirements of the optimized design.

[0204] Step S354: Designing a sound insulation structure for the noise equipment impact structure data, thereby obtaining sound insulation structure data;

[0205] In this embodiment, the design parameters of the sound insulation structure are determined based on the location of the noise source and the affected area. The structure mainly includes sound insulation walls, the laying of sound insulation materials and the design of sound insulation cavities. When designing, consider using materials with high density and excellent sound absorption performance, such as mineral wool, glass wool or multi-layer composite materials. In the actual operation process, the sound pressure level and noise source distribution data are combined to calculate the noise attenuation requirements of different areas, and the finite element simulation tools (such as COMSOL) are used to analyze the acoustic effects of sound insulation materials, and the design parameters are adjusted according to the calculation results. When designing the sound insulation structure, it is necessary to ensure sealing to avoid the existence of gaps and holes. After completing the design, record the material type, thickness and installation position of all sound insulation layers to form complete sound insulation structure data.

[0206] Step S355: Optimize and integrate equipment noise according to the equipment high-damping support material data and the sound insulation structure data, so as to obtain equipment noise optimization data.

[0207] In this embodiment, the damping effect of the high-damping support material and the sound insulation effect of the sound insulation structure are comprehensively analyzed to determine the best combination scheme. For example, by calculating the noise attenuation of each component, the noise reduction effect of the support material and the sound insulation structure at different frequencies is obtained. In specific implementation, the simulation software is used to simulate the noise propagation of the entire system to analyze the overall effect under the interaction of the noise source, the shell, the support material and the sound insulation structure. During the optimization process, the layout and thickness of the support material and the sound insulation structure are adjusted to ensure maximum noise suppression. The optimization scheme is converted into actual construction drawings, and all optimization parameters, including material type, position, thickness, etc., are recorded to generate the final equipment noise optimization data.

[0208] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0209] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for constructing a digital twin model of an electrical equipment sound field, characterized in that: The following steps are involved: Step S1: acquiring electrical equipment structural design data, and constructing a three-dimensional electrical equipment geometric model according to the electrical equipment structural design data, thereby obtaining a three-dimensional electrical equipment geometric model; Step S2: Acquire equipment acoustic characteristic data, and perform multi-material sound wave propagation analysis on a three-dimensional electrical equipment geometric model based on the equipment acoustic characteristic data, thereby obtaining multi-material sound wave propagation data; Based on multi-material sound wave propagation data, the structural design of the three-dimensional electrical equipment geometric model is optimized to obtain equipment structural design optimization data; Step S3: performing acoustic simulation on the three-dimensional electrical equipment geometric model according to the equipment acoustic characteristic data, thereby obtaining acoustic simulation data; performing abnormal noise analysis on the acoustic simulation data, thereby obtaining abnormal noise data; Perform equipment noise optimization control based on abnormal noise data to obtain equipment noise optimization data; Step S4: construct a digital twin model of the sound field of the electrical equipment according to the equipment noise optimization data and the equipment structure design optimization data, so as to obtain a digital twin model of the sound field of the electrical equipment.

2. The method for constructing a digital twin model of an electrical equipment sound field according to claim 1, characterized in that: Step S1 is specifically as follows: Step S11: acquiring electrical equipment structural design data, and performing equipment housing design feature extraction and internal component design feature extraction based on the electrical equipment structural design data, thereby obtaining equipment housing design data and internal component design data; Step S12: Modeling the geometric shape of the housing based on the device housing design data, thereby obtaining housing geometric shape modeling data; Step S13: assigning material properties to the shell geometry modeling data according to the device shell design data, thereby obtaining shell material property modeling data; Step S14: setting the shell thickness according to the shell material property modeling data, thereby obtaining the device shell modeling data; Step S15: performing motor modeling based on the internal component design data, thereby obtaining motor modeling data; Step S16: constructing a three-dimensional geometric model of the electrical equipment according to the equipment housing modeling data and the motor modeling data, thereby obtaining a three-dimensional geometric model of the electrical equipment.

3. The method for constructing a digital twin model of an electrical equipment sound field according to claim 1, characterized in that: Step S2 is specifically as follows: Step S21: Acquire device acoustic characteristic data, and perform sound propagation path feature extraction and sound propagation intensity feature extraction based on the device acoustic characteristic data, thereby obtaining sound propagation path data and sound propagation intensity data; Step S22: inputting the sound propagation path data and the sound propagation intensity data into the three-dimensional electrical equipment geometric model, and using simulation software to simulate the sound wave propagation, thereby obtaining the sound wave propagation simulation data; Step S23: performing multi-material attenuation analysis according to the acoustic wave propagation simulation data, thereby obtaining multi-material acoustic wave attenuation data; Step S24: evaluating the acoustic wave propagation velocity of the multi-material acoustic wave attenuation data, thereby obtaining the multi-material acoustic wave propagation data; Step S25: Optimizing the structural design of the three-dimensional electrical equipment geometric model based on the multi-material sound wave propagation data, thereby obtaining equipment structural design optimization data.

4. The method for constructing a digital twin model of an electrical equipment sound field according to claim 3, characterized in that: Step S23 is specifically as follows: Step S231: performing Fourier transform on the acoustic wave propagation simulation data to obtain acoustic wave propagation frequency domain data; Step S232: extracting propagation material characteristics from the acoustic wave propagation simulation data, thereby obtaining propagation material data; Step S233: selecting an attenuation model based on the propagation material data, thereby obtaining an attenuation model; Step S234: performing frequency domain energy attenuation analysis on the sound wave propagation frequency domain data according to the attenuation model, thereby obtaining sound wave frequency domain energy attenuation data; Step S235: performing material interface reflection loss analysis on the sound wave propagation frequency domain data, thereby obtaining sound wave propagation material interface reflection loss data; Step S236: performing interface correction on the sound wave frequency domain energy attenuation data according to the sound wave propagation material interface reflection loss data, thereby obtaining multi-material sound wave attenuation data.

5. The method for constructing a digital twin model of an electrical equipment sound field according to claim 3, characterized in that: Step S25 is specifically as follows: Step S251: using finite element analysis software to solve the free vibration of the three-dimensional electrical equipment geometric model, thereby obtaining free vibration data of the geometric model; Step S252: performing harmonic response analysis on the three-dimensional electrical equipment geometric model based on the multi-material sound wave propagation data, thereby obtaining sound wave vibration data; Step S253: performing vibration resonance effect analysis according to the free vibration data of the geometric model and the acoustic vibration data, thereby obtaining vibration resonance effect data; Step S254: performing structural reinforcement optimization on the three-dimensional electrical equipment geometric model according to the vibration resonance effect data, thereby obtaining structural reinforcement optimization data; Step S255: superimposing sound-absorbing materials on the three-dimensional electrical equipment geometric model according to the vibration resonance effect data, thereby obtaining sound-absorbing material optimization data; Step S256: Perform equipment structure design optimization integration based on the structural reinforcement optimization data and the sound-absorbing material optimization data, so as to obtain equipment structure design optimization data.

6. The method for constructing a digital twin model of an electrical equipment sound field according to claim 5, characterized in that: Step S253 is specifically as follows: Perform vibration frequency statistics based on the free vibration data of the geometric model to obtain free vibration frequency data; Performing vibration frequency statistics on the sound wave vibration data, thereby obtaining the sound wave vibration frequency data; Resonance frequency matching is performed according to the free vibration frequency data and the acoustic wave vibration frequency data, thereby obtaining resonance frequency data; Perform time statistics according to the resonance frequency data to obtain resonance time data; Performing vibration amplitude analysis on the free vibration data of the geometric model according to the resonance time data, thereby obtaining free vibration amplitude data; Performing vibration amplitude analysis on the acoustic wave vibration data according to the resonance time data, thereby obtaining the acoustic wave vibration amplitude data; The vibration amplitude range is counted according to the free vibration amplitude data and the acoustic wave vibration amplitude data, so as to obtain the vibration amplitude range data; The vibration resonance effect is integrated according to the resonance frequency data and the vibration amplitude range data, so as to obtain the vibration resonance effect data.

7. The method for constructing a digital twin model of an electrical equipment sound field according to claim 5, characterized in that: Step S254 is specifically as follows: According to the vibration resonance effect data, the key geometric structure parts of the three-dimensional electrical equipment geometric model are identified to obtain the key geometric structure parts data; Add support points to the key geometric structure data, so as to obtain support point addition data; Select the flexible support system for the support point addition data, so as to obtain the flexible support system data; Adding a shock absorbing device to the data of key geometric structure parts, thereby obtaining the data of adding the shock absorbing device; The structural reinforcement optimization and integration is carried out according to the flexible support system data and the data of the additional shock absorbing device, so as to obtain the structural reinforcement optimization data.

8. The method for constructing a digital twin model of an electrical equipment sound field according to claim 1, characterized in that: Step S3 is specifically as follows: Step S31: performing acoustic simulation on the three-dimensional electrical equipment geometric model according to the equipment acoustic characteristic data, thereby obtaining acoustic simulation data; Step S32: performing sound pressure distribution analysis on the acoustic simulation data to obtain sound pressure distribution data; Step S33: performing noise source identification on the acoustic simulation data to obtain noise source data; Step S34: performing abnormal noise analysis on the sound pressure distribution data according to the noise source data, thereby obtaining abnormal noise data; Step S35: Perform equipment noise optimization control according to the abnormal noise data, so as to obtain equipment noise optimization data.

9. The method for constructing a digital twin model of an electrical equipment sound field according to claim 8, characterized in that: Step S32 is specifically as follows: Step S321: extracting sound wave propagation characteristics from acoustic simulation data to obtain sound wave propagation data; Step S322: identifying the sound pressure area according to the sound wave propagation data, thereby obtaining the sound pressure area data; Step S323: constructing a two-dimensional sound pressure distribution map according to the sound pressure area data, thereby obtaining a two-dimensional sound pressure distribution map; Step S324: calculating the sound pressure level of the two-dimensional sound pressure distribution diagram to obtain sound pressure level data; Step S325: mapping the sound pressure distribution of the three-dimensional electrical equipment geometric model according to the sound pressure level data, thereby obtaining sound pressure distribution data.

10. The method for constructing a digital twin model of an electrical equipment sound field according to claim 8, characterized in that: Step S35 is specifically as follows: Step S351: performing noise equipment influence structure identification according to abnormal noise data, thereby obtaining noise equipment influence structure data; Step S352: performing device housing optimization design on the noise device influencing structure data, thereby obtaining device housing optimization data; Step S353: replacing the high damping support material of the equipment with respect to the equipment shell optimization data, thereby obtaining the high damping support material data of the equipment; Step S354: Designing a sound insulation structure for the noise equipment impact structure data, thereby obtaining sound insulation structure data; Step S355: Optimize and integrate equipment noise according to the equipment high-damping support material data and the sound insulation structure data, so as to obtain equipment noise optimization data.

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