Method for determining air vibration isolation system and establishing its simulation model

Abstract

The present application relates to a method for determining an air vibration isolation system and a method for establishing a simulation model thereof. The method for establishing the simulation model of the air vibration isolation system comprises: obtaining parameters to be calibrated of the air vibration isolation system, calibrating the parameters to be calibrated based on the Markov Chain Monte Carlo method, and constructing an air vibration isolation system simulation model based on the calibrated parameters to be calibrated. The method for establishing the simulation model of the air vibration isolation system optimizes the model parameters using the Markov Chain Monte Carlo method, and establishes a model based on the optimized parameters of the air vibration isolation system simulation model, thereby reducing the uncertainty of the air vibration isolation system simulation model, improving the simulation accuracy of the air vibration isolation system simulation model, and making the simulation results of the air vibration isolation system simulation model closer to the actual situation.

Description

Technical Field

[0001] The present application relates to the technical field of air vibration isolation systems for sonic drilling rigs, and in particular to a method and device for establishing a simulation model of an air vibration isolation system, as well as a method and device for determining an air vibration isolation system, a computer device, a computer-readable storage medium, and a computer program product. Background Art

[0002] Acoustic vibration drilling technology is a high-efficiency, fluid-free, hydraulically driven drilling tool. This eliminates the need for water, making it an irreplaceable advantage in arid, water-scarce areas and loose formations. An acoustic vibration drill rig utilizes two (or more) symmetrical eccentric shafts (blocks) driven by a hydraulic motor to generate superimposed high-frequency vibration forces in the vertical direction. The horizontal forces are offset by the eccentric shafts (blocks) moving in opposite directions. The vertical vibration forces are high in intensity and cyclical, causing overall vibration during drilling, reducing equipment stability and service life, and hindering the long-term stability of the drill rig. Vibration isolators protect the drill rig, ensuring maximum vibration energy is delivered to the drill pipe, minimizing damage to the rig.

[0003] As a new type of vibration isolation structure, air springs have the advantages of light weight, low noise, low natural frequency, stiffness that can be controlled by initial internal pressure, ability to withstand large loads, and ability to isolate high-frequency vibrations. To apply the air vibration isolation system to acoustic drilling rigs, it is first necessary to establish a mathematical model that can quantitatively characterize the vibration isolation response of the air vibration isolation system during the drilling process, and then analyze its dynamic mechanical characteristics, establish a simulation model, and finally compare and verify it with the indoor test results.

[0004] However, the simulation accuracy of the currently established air vibration isolation system simulation model is low. Summary of the Invention

[0005] Based on this, it is necessary to provide a method and device for establishing an air vibration isolation system simulation model, as well as a method and device for determining an air vibration isolation system, a computer device, a computer-readable storage medium and a computer program product, which can improve the simulation accuracy of the established air vibration isolation system simulation model in order to solve the above technical problems.

[0006] In a first aspect, a method for establishing an air vibration isolation system simulation model is provided, comprising: obtaining parameters to be calibrated of the air vibration isolation system; calibrating the parameters to be calibrated based on a Markov chain Monte Carlo method; and constructing an air vibration isolation system simulation model based on the calibrated parameters to be calibrated.

[0007] In one embodiment, the parameters to be calibrated include an initial air pressure value of the air chamber of the air vibration isolation system and an excitation frequency of the air vibration isolation system.

[0008] In one embodiment, the Markov chain Monte Carlo method is used to calibrate the parameters to be calibrated, including: determining the prior distribution of the parameters to be calibrated; defining the number of Markov chains as N and the length of each chain as H; randomly generating the starting point of each Markov chain from the prior distribution of the parameters. , and calculate the joint likelihood model of each Markov chain; where, is the i-th parameter sample to be calibrated in the j-th chain; based on the joint likelihood model of each Markov chain, an evolution operation is performed on the parameter samples to be calibrated on each Markov chain until the Markov chain reaches the convergence standard, and the calibrated parameters to be calibrated are obtained based on the converged Markov chain.

[0009] In one embodiment, the method of obtaining the calibrated parameters to be calibrated based on the converged Markov chain includes: determining the parameters to be calibrated having a frequency greater than a preset frequency as target parameters to be calibrated based on the frequencies of each group of parameters to be calibrated of the converged Markov chain; and using the range of parameters to be calibrated formed by the target parameters to be calibrated as the calibrated parameters to be calibrated.

[0010] In one embodiment, the prior distribution of the initial air pressure value is 0.2 MPa to 0.8 MPa, and the prior distribution of the excitation frequency is Hertz to hertz.

[0011] In a second aspect, a method for determining an air vibration isolation system is provided, the method comprising: performing a dynamic mechanical characteristic analysis on an air vibration isolation system determined by an air vibration isolation system simulation model; the air vibration isolation system simulation model is established based on the above-mentioned method; based on the dynamic mechanical characteristic analysis results, the air vibration isolation system simulation model is corrected until the dynamic mechanical characteristic analysis results meet the preset dynamic mechanical performance requirements; based on the corrected air vibration isolation system simulation model, the parameters of the air vibration isolation system that meet the dynamic mechanical performance requirements are determined.

[0012] In a third aspect, a device for establishing an air vibration isolation system simulation model is provided, the device comprising: an acquisition module for acquiring parameters to be calibrated of the air vibration isolation system; a calibration module for calibrating the parameters to be calibrated based on the Markov chain Monte Carlo method; and a construction module for constructing the air vibration isolation system simulation model based on the calibrated parameters to be calibrated.

[0013] In a fourth aspect, a device for determining an air vibration isolation system is provided, the device comprising: an analysis module for performing dynamic mechanical characteristic analysis on an air vibration isolation system determined by an air vibration isolation system simulation model; the air vibration isolation system simulation model is established based on the above-mentioned method; a correction module for correcting the air vibration isolation system simulation model based on the dynamic mechanical characteristic analysis results until the dynamic mechanical characteristic analysis results meet the preset dynamic mechanical performance requirements; a determination module for determining parameters of the air vibration isolation system that meet the dynamic mechanical performance requirements based on the corrected air vibration isolation system simulation model.

[0014] In a fifth aspect, a computer device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method for establishing an air vibration isolation system simulation model as described in any one of the first aspects above is implemented, or when the processor executes the computer program, the method for determining an air vibration isolation system as described in any one of the second aspects above is implemented.

[0015] In a sixth aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, it implements the method for establishing an air vibration isolation system simulation model as described in any of the first aspects above, or when the computer program is executed by a processor, it implements the method for determining an air vibration isolation system as described in any of the second aspects above.

[0016] The method for establishing an air vibration isolation system simulation model obtains parameters of the air vibration isolation system to be calibrated, calibrates the parameters to be calibrated using the Markov Chain Monte Carlo method, and constructs the air vibration isolation system simulation model based on the calibrated parameters to be calibrated. This method for establishing an air vibration isolation system simulation model optimizes the model parameters using the Markov Chain Monte Carlo method and establishes the model based on the optimized parameters of the air vibration isolation system simulation model. This reduces the uncertainty of the air vibration isolation system simulation model, improves the simulation accuracy of the air vibration isolation system simulation model, and makes the simulation results of the air vibration isolation system simulation model more realistic. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 1 is a flow chart of a method for establishing an air vibration isolation system simulation model in one embodiment;

[0018] Figure 2 Schematic diagram of a flow chart of a calibration method in one embodiment;

[0019] Figure 3 Schematic diagram of a flow chart of a calibration method in another embodiment;

[0020] Figure 41 is a flow chart of a method for obtaining parameters to be calibrated in one embodiment;

[0021] Figure 5 1 is a flow chart of a method for obtaining an evaluation model in one embodiment;

[0022] Figure 6 is a schematic diagram of a three-dimensional model of an air vibration isolation system in one embodiment;

[0023] Figure 7 1 is a flow chart of a method for determining an air vibration isolation system in one embodiment;

[0024] Figure 8 1. It is a structural block diagram of a device for establishing a simulation model of an air vibration isolation system in one embodiment;

[0025] Figure 9 is a structural block diagram of a determination device for an air vibration isolation system in one embodiment;

[0026] Figure 10 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0028] The inventors have found that due to the complexity of the air vibration isolation system and the limited observation data, when the air vibration isolation system is described in mathematical language, there is a certain uncertainty in the established simulation model and its simulation results, which leads to a large error between the simulation results and the actual results. In view of this, an embodiment of the present application provides a method for establishing a simulation model of an air vibration isolation system. The simulation model of the air vibration isolation system established by this method has simulation results that are closer to the actual results and can more accurately characterize the dynamic mechanical characteristics of the air vibration isolation system during the use of the sonic drilling rig.

[0029] Please refer to Figure 1 , which shows a method for establishing an air vibration isolation system simulation model provided by an embodiment of the present application, such as Figure 1 As shown, the method may include steps S102 to S106.

[0030] S102: Obtain parameters of the air vibration isolation system to be calibrated.

[0031] S104, calibrating the parameters to be calibrated based on the Markov Chain Monte Carlo method.

[0032] S106: Constructing an air vibration isolation system simulation model based on the calibrated parameters to be calibrated.

[0033] Monte Carlo simulation (MCS) is a computer simulation method that uses random numbers, also known as stochastic simulation. MCS can be categorized into static MC methods (uniform sampling) and dynamic MC methods (importance sampling) based on different sampling methods. Importance sampling methods increase the frequency of occurrence of random variables with high contribution rates (high probability densities). Markov Chain Monte Carlo (MCMC) is a representative importance sampling method. A Markov chain is a discrete-time random process that mathematically exhibits the Markov property, characterized by irreducibility, aperiodicity, and stationary distribution. MCMC can effectively simulate the posterior probability distribution of parameters through parameter sampling. By considering parameter uncertainty in air vibration isolation system simulation models using MCMC, the uncertainty of the air vibration isolation system simulation model can be reduced, thereby improving simulation accuracy.

[0034] It should be noted that the vibration isolation performance of the air vibration isolation system is related to multiple parameters of the air vibration isolation system. One or more parameters are determined from the parameters of the air vibration isolation system as parameters to be calibrated, and the parameters to be calibrated are calibrated based on the MCMC method. The air vibration isolation system simulation model is constructed based on the calibrated parameters to be calibrated. This can reduce the uncertainty of the model parameters and improve the simulation accuracy of the model, thereby improving the accuracy of guidance for designing and improving the air vibration isolation structure of the sonic drilling rig.

[0035] In one embodiment, the parameters to be calibrated may include the initial air pressure value of the air chamber of the air vibration isolation system and the excitation frequency of the air vibration isolation system. It should be noted that the air chamber of the air vibration isolation system includes an upper air chamber and a lower air chamber, and the initial air pressure values ​​of the upper and lower air chambers of the air vibration isolation system are equal. Therefore, the initial air pressure value of the air chamber described in this embodiment refers to both the initial air pressure value of the upper air chamber and the initial air pressure value of the lower air chamber.

[0036] The method for establishing an air vibration isolation system simulation model provided in this embodiment optimizes model parameters through the Markov chain Monte Carlo method, and establishes a model based on the optimized parameters of the air vibration isolation system simulation model, thereby reducing the uncertainty of the air vibration isolation system simulation model and improving the simulation accuracy of the air vibration isolation system simulation model. This makes the simulation results of the air vibration isolation system simulation model closer to the actual situation, and improves the accuracy of guidance for designing and improving the air vibration isolation structure of the sonic drilling rig.

[0037] As can be seen from the above embodiments, using the MCMC method to calibrate the parameters to be calibrated can reduce the uncertainty of the model parameters. Therefore, the following embodiments provide a calibration method to achieve this. This is illustrated using the initial air pressure value and the excitation frequency of the air vibration isolation system as examples.

[0038] Please refer to Figure 2 , which shows a calibration method provided by an embodiment of the present application, such as Figure 2 As shown, the steps include steps S202 to S208, which calibrate the parameters to be calibrated based on the Markov Chain Monte Carlo method.

[0039] S202, determining the prior distribution of the parameters to be calibrated.

[0040] S204, define the number of Markov chains as N, and the length of each chain as H.

[0041] S206, randomly generate the starting point of each Markov chain from the parameter prior distribution , and calculate the joint likelihood model of each Markov chain; where, is the i-th parameter sample to be calibrated in the j-th chain (i=1, 2…, H, j=1, 2…, N);

[0042] S208 , performing an evolution operation on the parameter samples to be calibrated on each Markov chain based on the joint likelihood model of each Markov chain until the Markov chain reaches a convergence criterion, and obtaining the calibrated parameters to be calibrated based on the converged Markov chain.

[0043] The prior distribution of the parameters to be calibrated is the initial range of the parameters to be calibrated, which can be determined based on the data of the existing air vibration isolation system. In one embodiment, the prior distribution of the initial air pressure value of the air chamber of the air vibration isolation system can be 0.2 MPa to 0.8 MPa, and the prior distribution of the excitation frequency of the air vibration isolation system can be Hertz to hertz.

[0044] It should be noted that the MCMC method is based on the Bayesian theoretical framework, establishes a Markov chain with a stationary distribution, and then samples the stationary distribution. During the evolution of the Markov chain, it fully searches within the space of the probability distribution of the target function, and continuously adjusts the search strategy based on previous sampling information, fully sampling in areas with high probability density, and finally obtains a posterior probability distribution that converges to the target function. It is understandable that the number N of Markov chains and the length H of each chain can be set as needed, and this embodiment of the application does not limit this. Optionally, N is 3 and H is 50,000.

[0045] In one embodiment, the likelihood model may be a first formula, which is:

[0046]

[0047] Where N is the number of observations, f(θ) is the simulation output of the simulation model when the parameter to be calibrated is θ, ∑ represents the error structure of the observations, y is the indoor test data of the air vibration isolation system when the parameter to be calibrated is θ, and L(θ|y) is the likelihood function between the indoor test data and the simulation output.

[0048] Please refer to Figure 3 , which shows a calibration method provided by an embodiment of the present application, which uses DREAM zs The algorithm realizes the converged Markov chain, such as Figure 3 As shown, it may include steps S302 to S314.

[0049] S302, define the prior distribution of the initial air pressure value as 0.2MPa to 0.8MPa, and the prior distribution of the excitation frequency as Hz to Hz.

[0050] S304: Define the number of Markov chains as N and the length of each chain as H.

[0051] S306, randomly generate the starting point of each Markov chain from the parameter prior distribution , and calculate the joint likelihood model for each Markov chain.

[0052] The description of the likelihood model is detailed in the above embodiment and will not be repeated here.

[0053] S308, based on the second formula and Sure ,in, is the i-th parameter sample to be calibrated in the j-th chain (i=1, 2…, H, j=1, 2…, N), the second formula is:

[0054]

[0055] Among them, δ is the number of different chains for generating candidate points, r1(m) and r2(n) represent different Markov chains, I d is the identity matrix, e d and ε d is a random number, γ(δ, ) is the jump scale, Represents the value that replaces the parameter dimension when evolving the subspace.

[0056] S310, replace each element in each chain with the crossover probability, and then calculate the joint likelihood function and each sample If the acceptance rate indicates acceptance, it is recorded as ; If the acceptance rate indicates no acceptance, then = .

[0057] In one embodiment, the acceptance rate α is:

[0058]

[0059] in, is the posterior distribution density with the parameter θ to be calibrated.

[0060] S312, determining whether the Markov chain meets the convergence condition, if so, executing step S314; if not, returning to step S308.

[0061] In one embodiment, the convergence condition includes the Markov chain passing the Gelman-Rubin convergence criterion, that is, R sta <1.2, and the number of iterations of the Markov chain reaches H. Among them, R sta for:

[0062]

[0063] Where W is the intra-chain variance, B is the inter-chain variance, N is the number of observations, and H is the length of each chain.

[0064] S314, obtaining the calibrated parameters to be calibrated based on the converged Markov chain.

[0065] Based on DREAM zs The algorithm can determine the posterior distribution of the initial air pressure value of the air chamber and the posterior distribution of the excitation frequency. According to indoor verification, the simulation results of the air vibration isolation system simulation model established based on the posterior distribution of the initial air pressure value and the posterior distribution of the excitation frequency are closer to the experimental results than the simulation results of the air vibration isolation system simulation model established based on the prior distribution of the initial air pressure value and the prior distribution of the excitation frequency. This shows that constructing an air vibration isolation system simulation model based on the calibrated initial air pressure value and excitation frequency of the air chamber can improve the simulation accuracy.

[0066] It should be noted that the evaluation model can be used to determine whether the simulation accuracy of the air vibration isolation system simulation model constructed based on the calibrated parameters to be calibrated is higher than the simulation accuracy of the air vibration isolation system simulation model constructed based on the uncalibrated parameters. In one embodiment, the evaluation model can be the vibration isolation transmissibility of the air vibration isolation system. The vibration isolation transmissibility is:

[0067]

[0068] Where, ξ is the damping ratio of the air vibration isolation system, λ= , where ω is the excitation frequency of the air vibration isolation system, is the natural frequency of the air vibration isolation system, the natural frequency It is related to the initial pressure value of the air chamber. It should be noted that the natural frequency It is related to the stiffness coefficient K, which is related to the initial pressure value of the air chamber, so the natural frequency Associated with the initial air pressure value of the air chamber. Specifically,

[0069] ,

[0070] ,

[0071] Among them, K is the stiffness coefficient, A is the effective area of ​​the air vibration isolation system, n is the polytropic index, n is 1 in the isothermal process, n is 1.4 in the adiabatic process, and the value range of n in the dynamic process is 1-1.4, P0 is the initial air pressure value of the air vibration isolation system, and V0 is the initial volume of the air chamber of the air vibration isolation system.

[0072] Please refer to Figure 4 , which shows a method for obtaining parameters to be calibrated provided by an embodiment of the present application, such as Figure 4 As shown, the steps of obtaining the calibrated parameters to be calibrated based on the converged Markov chain include steps S402 to S404.

[0073] S402 , according to the frequencies of the groups of parameters to be calibrated of the converged Markov chain, determining the parameters to be calibrated whose frequencies are greater than a preset frequency as target parameters to be calibrated.

[0074] It is understandable that the posterior samples of MCMC include a groups in total, where a is:

[0075] a=N*(Hb),

[0076] b is the length of the warm-up period, N is the number of Markov chains, and H is the length of each chain. As you can understand, the warm-up period is the initial sampling phase, during which no posterior distribution results are available. The likelihood function for each set of samples is calculated only after the warm-up period. It should be noted that the preset frequency can be set as needed to determine the target parameters to be calibrated from the posterior samples of the MCMC.

[0077] S404: The parameter range to be calibrated consisting of the target parameters to be calibrated is used as the parameters to be calibrated after calibrating.

[0078] It should be noted that the parameters to be calibrated include the initial air pressure value and the excitation frequency. The initial air pressure value after calibration should be the parameter to be calibrated consisting of the target initial air pressure value, and the excitation frequency after calibration should be the parameter to be calibrated consisting of the target excitation frequency. For example, assuming the target initial air pressure values ​​are 0.3MPa, 0.6MPa, and 0.4MPa, the initial air pressure value after calibration is 0.3MPa to 0.6MPa; assuming the target excitation frequencies are 90Hz, 100Hz, and 80Hz, the excitation frequency after calibration is 80Hz to 100Hz.

[0079] In one embodiment, the target parameter to be calibrated is the parameter to be calibrated with the highest frequency among the groups of parameters to be calibrated, and the parameter to be calibrated after calibration is less than or equal to the first value and greater than or equal to the second value. The first value is the sum of the target parameter to be calibrated and the preset value, and the second value is the difference between the target parameter to be calibrated and the preset value. It should be noted that the preset value can be set as needed, as long as it is ensured that the parameter to be calibrated after calibration is a narrow range where a peak value appears. For example, assuming that the target initial air pressure value is 0.4MPa and the preset value is 0.1MPa, the initial air pressure value after calibration is 0.3MPa to 0.5MPa.

[0080] In one embodiment, a probability density distribution histogram of the parameters to be calibrated after identification and calibration by the MCMC algorithm is statistically analyzed, and a first interval is searched in the probability density distribution histogram of the parameters to be calibrated. The first interval is the range of parameters to be calibrated after calibration. The first interval is the range of parameters to be calibrated corresponding to a probability density greater than a preset probability density. In this way, a range with a significant frequency peak and a narrow convergence interval is determined as the parameters to be calibrated after calibration.

[0081] Please refer to Figure 5 , which shows a method for obtaining an evaluation model provided by an embodiment of the present application, such as Figure 5 As shown, the acquisition method may include steps S502 to S508.

[0082] S502: Determine a three-dimensional model of the air vibration isolation system.

[0083] In one embodiment, the air vibration isolation system mainly consists of a box body 602, a core shaft 604, a base 606, end covers (including an upper end cover 608a and a lower end cover), an exciter base 610, a V-shaped combined sealing ring 612, an O-shaped sealing ring 614, a one-way valve 616, etc. The box body 602 is evenly divided into two air chambers 618 (an upper air chamber and a lower air chamber) by a piston, and each air chamber 618 has a hole for inputting compressed air; the piston and the box body 602 adopt a straight-through labyrinth seal to separate the gas in the two air chambers 618; the box body 602 and the upper end cover 608a and the base 606 are connected together by bolts and sealed with an O-shaped sealing ring 614; the core shaft 604 and the upper end cover 608a and the base 606 are sealed with a V-shaped combined sealing ring 612. Figure 6 As shown, it shows a schematic diagram of the three-dimensional model of the air vibration isolation system.

[0084] S504 , generalizing based on the three-dimensional model of the air vibration isolation system to obtain a mechanical model of the air vibration isolation system.

[0085] It can be understood that the mechanical model of the air vibration isolation system refers to a model obtained by ignoring factors that have little influence on the vibration isolation effect in the air vibration isolation system and some factors that cannot actually be changed.

[0086] S506: Analyze the parameters of the air vibration isolation system based on the mechanical model of the air vibration isolation system.

[0087] Analyzing the parameters of the air vibration isolation system based on the generalized model can greatly reduce the difficulty and complexity of the analysis. Optionally, the parameters of the air vibration isolation system may include but are not limited to restoring force, stiffness coefficient, excitation frequency, and amplitude.

[0088] S508: Determine an evaluation model based on the analysis results of the parameters of the air vibration isolation system.

[0089] Optionally, the evaluation model can be used to evaluate the vibration isolation effect of the air vibration isolation system. In one embodiment, the evaluation model can be the vibration isolation transmissibility of the air vibration isolation system. The vibration isolation transmissibility is:

[0090]

[0091] Where, ξ is the damping ratio of the air vibration isolation system, λ= , where ω is the excitation frequency of the air vibration isolation system, is the natural frequency of the air vibration isolation system, the natural frequency It is related to the initial pressure value of the air chamber. It should be noted that the natural frequency It is related to the stiffness coefficient K, which is related to the initial pressure value of the air chamber, so the natural frequency Associated with the initial air pressure value of the air chamber. Specifically,

[0092] ,

[0093] ,

[0094] Among them, K is the stiffness coefficient, A is the effective area of ​​the air vibration isolation system, n is the polytropic index, n is 1 in the isothermal process, n is 1.4 in the adiabatic process, and the value range of n in the dynamic process is 1-1.4, P0 is the initial air pressure value of the air vibration isolation system, and V0 is the initial volume of the air chamber of the air vibration isolation system.

[0095] The air vibration isolation system of a sonic drill achieves its vibration effect by suppressing the vibration response of the isolator to the power head frame, effectively transmitting the vibration energy to the base of the air vibration isolation system. Therefore, the air vibration isolation system of a sonic drill is a passive vibration isolation system. When subjected to simple harmonic excitation, the amplitude-frequency characteristic of the isolator's displacement relative to the base's displacement (vibration isolation transmissibility) is a key indicator of air vibration isolation system performance.

[0096] This example first establishes a three-dimensional model of the air vibration isolation system. During vibration, the volume and pressure of the air chamber of the air vibration isolation system change, and the compression deformation of the air is used to absorb vibration energy, thereby achieving the purpose of vibration isolation. After generalizing the air vibration isolation system, its mechanical model is obtained. Based on the force analysis of the air vibration isolation system during drilling, the transfer function of the air vibration isolation system is constructed. The displacement of the base is used as the input and the displacement of the vibration isolation body is used as the output. The obtained transfer function is:

[0097] ,

[0098] ,

[0099] ,

[0100] in, yes The amplitude of is called the amplitude-frequency characteristic of the air vibration isolation system; yes The phase angle of the system is called the phase-frequency characteristic of the system.

[0101] Since the amplitude-frequency characteristic represents the ratio of the base displacement to the isolator displacement, which is a function that changes with frequency, it can accurately describe the sensitivity of the vibration isolation system to the simple harmonic excitation force, and therefore can be used as an indicator to evaluate the vibration isolation performance of the air vibration isolation system. , by substituting it into the frequency-amplitude characteristic expression, we can get the vibration isolation transmissibility T(λ). Therefore, the vibration isolation transmissibility T(λ) can be used to evaluate the vibration isolation performance of the air vibration isolation system.

[0102] Please refer to Figure 7 , which shows a method for determining an air vibration isolation system provided by an embodiment of the present application. Figure 7 As shown, the determination method may include steps S702 to S706.

[0103] S702 , performing dynamic mechanical characteristic analysis on the air vibration isolation system determined by the air vibration isolation system simulation model.

[0104] S704 , based on the dynamic mechanical analysis characteristic analysis results, the air vibration isolation system simulation model is modified until the dynamic mechanical characteristic analysis results meet the preset dynamic mechanical performance requirements.

[0105] S706 , based on the modified air vibration isolation system simulation model, determine parameters of the air vibration isolation system that meet dynamic mechanical performance requirements.

[0106] The air vibration isolation system simulation model is established according to the establishment method provided in the above embodiment. Therefore, the air vibration isolation system simulation model used in this embodiment has high simulation accuracy. The dynamic mechanical characteristics of the air vibration isolation system are used to describe the sensitivity of the air vibration isolation system to simple harmonic excitation forces. The dynamic mechanical performance requirements can be set as needed and are not limited in this embodiment of the present application. The exemplary ratio of the isolator displacement to the base displacement is less than a preset threshold.

[0107] It should be noted that the dynamic mechanical characteristics of the air vibration isolation system corresponding to the air vibration isolation system simulation model can be determined based on the air vibration isolation system simulation model. If the dynamic mechanical characteristics do not meet the performance requirements, the parameters of the air vibration isolation system simulation model are modified until they meet the dynamic mechanical performance requirements. At this time, the air vibration isolation system corresponding to the air vibration isolation system simulation model can also be considered to meet the dynamic mechanical performance requirements. The air vibration isolation system is designed using the parameters of the air vibration isolation system simulation model to obtain an air vibration isolation system that meets the dynamic mechanical performance requirements. In this embodiment, the simulation results of the air vibration isolation system simulation model established in the above embodiment are used to guide the design and improvement of the structural parameters of the air vibration isolation system, thereby improving the accuracy of the guidance.

[0108] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0109] Based on the same inventive concept, embodiments of the present application also provide a device for establishing an air vibration isolation system simulation model for implementing the aforementioned method for establishing an air vibration isolation system simulation model. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more embodiments of the device for establishing an air vibration isolation system simulation model provided below can be found in the aforementioned limitations of the method for establishing an air vibration isolation system simulation model, and will not be further elaborated here.

[0110] In one embodiment, Figure 8 As shown, a device 800 for establishing an air vibration isolation system simulation model is provided, comprising an acquisition module 802, a calibration module 804, and a construction module 806. Acquisition module 802 is configured to acquire parameters to be calibrated for the air vibration isolation system. Calibration module 804 is configured to calibrate the parameters to be calibrated using a Markov Chain Monte Carlo method. Construction module 806 is configured to construct an air vibration isolation system simulation model based on the calibrated parameters to be calibrated.

[0111] In one embodiment, the calibration module is further configured to determine a prior distribution of the parameter to be calibrated; define the number of Markov chains as N, and the length of each chain as H; randomly generate the starting point of each Markov chain from the prior distribution of the parameter , and calculate the joint likelihood model of each Markov chain; where, is the i-th parameter sample to be calibrated in the j-th chain; based on the joint likelihood model of each Markov chain, an evolution operation is performed on the parameter samples to be calibrated on each Markov chain until the Markov chain reaches the convergence standard, and the calibrated parameters to be calibrated are obtained based on the converged Markov chain.

[0112] In one embodiment, the calibration module is further used to determine the parameters to be calibrated whose frequency is greater than the preset frequency as the target parameters to be calibrated based on the frequencies of each group of parameters to be calibrated of the converged Markov chain; and use the range of parameters to be calibrated formed by the target parameters to be calibrated as the parameters to be calibrated after the calibration.

[0113] Each module in the apparatus for establishing an air vibration isolation system simulation model can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a computer device memory in software form, so that the processor can call and execute the corresponding operations of each module.

[0114] Based on the same inventive concept, embodiments of the present application also provide an apparatus for determining an air vibration isolation system, for implementing the aforementioned method for determining an air vibration isolation system. The solution provided by this apparatus is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more embodiments of the apparatus for determining an air vibration isolation system provided below can be found in the aforementioned method for determining an air vibration isolation system, and will not be further elaborated here.

[0115] In one embodiment, Figure 9 As shown, a device 900 for determining an air vibration isolation system is provided, comprising an analysis module 902, a correction module 904, and a determination module 906. The analysis module 902 is configured to perform a dynamic mechanical characteristic analysis on an air vibration isolation system determined by an air vibration isolation system simulation model. The correction module 904 is configured to correct the air vibration isolation system simulation model based on the dynamic mechanical characteristic analysis results until the dynamic mechanical characteristic analysis results meet preset dynamic mechanical performance requirements. The determination module 906 is configured to determine parameters of the air vibration isolation system that meet the dynamic mechanical performance requirements based on the corrected air vibration isolation system simulation model.

[0116] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 10As shown. The computer device includes a processor, a memory, and a communication interface connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner. The wireless manner can be implemented through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method for establishing an air vibration isolation system simulation model and / or a method for determining an air vibration isolation system is implemented.

[0117] Those skilled in the art will understand that Figure 10 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0118] In one embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0119] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0120] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0121] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), data processing logic devices based on quantum computing, and the like.

[0122] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0123] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for establishing a simulation model of an air vibration isolation system, characterized in that: include: Obtain the parameters of the air vibration isolation system to be calibrated; Determining a priori distribution of the parameter to be calibrated; Define the number of Markov chains as N and the length of each chain as H; The starting point of each Markov chain is randomly generated from the prior distribution of the parameters , and calculate the joint likelihood model of each Markov chain; where, is the i-th parameter sample to be calibrated in the j-th chain; Performing an evolution operation on the parameter samples to be calibrated on each Markov chain based on the joint likelihood model of each Markov chain until the Markov chain reaches a convergence criterion; According to the frequencies of the various groups of parameters to be calibrated of the converged Markov chain, the parameters to be calibrated having a frequency greater than a preset frequency are determined as target parameters to be calibrated; The parameter range to be calibrated formed by the target parameters to be calibrated is used as the parameters to be calibrated after calibration; An air vibration isolation system simulation model is constructed based on the calibrated parameters to be calibrated.

2. The method for establishing an air vibration isolation system simulation model according to claim 1, characterized in that: The parameters to be calibrated include the initial air pressure value of the air chamber of the air vibration isolation system and the excitation frequency of the air vibration isolation system.

3. The method for establishing an air vibration isolation system simulation model according to claim 2, characterized in that: The prior distribution of the initial air pressure value is 0.2 MPa to 0.8 MPa, and the prior distribution of the excitation frequency is Hertz to hertz.

4. The method for establishing an air vibration isolation system simulation model according to claim 1, wherein: After constructing the air vibration isolation system simulation model based on the calibrated parameters to be calibrated, the method further includes: It is determined according to the evaluation model whether the simulation accuracy of the air vibration isolation system simulation model constructed based on the calibrated parameters to be calibrated is higher than the simulation accuracy of the air vibration isolation system simulation model constructed based on the uncalibrated parameters.

5. The method for establishing an air vibration isolation system simulation model according to claim 4, characterized in that: The evaluation model is the vibration isolation transmissibility of the air vibration isolation system.

6. A method for determining an air vibration isolation system, characterized in that: The method comprises: Performing dynamic mechanical characteristic analysis on the air vibration isolation system determined by the air vibration isolation system simulation model; the air vibration isolation system simulation model is established based on the method described in any one of claims 1-5; Modifying the air vibration isolation system simulation model based on the dynamic mechanical characteristics analysis results until the dynamic mechanical characteristics analysis results meet the preset dynamic mechanical performance requirements; Based on the revised air vibration isolation system simulation model, parameters of the air vibration isolation system that meet the dynamic mechanical performance requirements are determined.

7. A device for establishing a simulation model of an air vibration isolation system, characterized in that: The device comprises: An acquisition module, used to obtain parameters to be calibrated of the air vibration isolation system; The calibration module is used to determine the prior distribution of the parameter to be calibrated; define the number of Markov chains as N and the length of each chain as H; randomly generate the starting point of each Markov chain from the prior distribution of the parameter , and calculate the joint likelihood model of each Markov chain; where, is the i-th parameter sample to be calibrated in the j-th chain; performing an evolution operation on the parameter samples to be calibrated on each Markov chain based on the joint likelihood model of each Markov chain until the Markov chain reaches a convergence criterion; determining, based on the frequency of each group of parameters to be calibrated in the converged Markov chain, the parameters to be calibrated whose frequency is greater than a preset frequency as the target parameters to be calibrated; and using the parameter range to be calibrated formed by the target parameters to be calibrated as the parameters to be calibrated after calibration; A construction module is used to construct an air vibration isolation system simulation model based on the calibrated parameters to be calibrated.

8. A device for determining an air vibration isolation system, characterized in that: The device comprises: An analysis module for performing dynamic mechanical characteristic analysis on an air vibration isolation system determined by an air vibration isolation system simulation model; the air vibration isolation system simulation model is established based on the method according to any one of claims 1 to 5; A correction module, configured to correct the air vibration isolation system simulation model based on the dynamic mechanical characteristics analysis results until the dynamic mechanical characteristics analysis results meet the preset dynamic mechanical performance requirements; The determination module is used to determine the parameters of the air vibration isolation system that meet the dynamic mechanical performance requirements based on the revised air vibration isolation system simulation model.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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