Installation and processing method and device of reactor vibration isolation system

Abstract

The present invention discloses an installation and processing method and device for a reactor vibration isolation system. The method includes: simulating a vibration isolation system model of the reactor vibration isolation system to obtain damping response data of the damper under different operating conditions; dynamically adjusting the control parameters of the damper according to the analysis results of the damper performance influencing factors; generating an installation strategy for the reactor vibration isolation system according to the damper performance influencing factors and control parameters; determining the vibration state data of the reactor vibration isolation system during the linkage height adjustment process according to the installation strategy; determining the abnormal vibration data of the reactor vibration isolation system according to the vibration state data; correcting the installation strategy according to the abnormal vibration data to obtain a corrected installation strategy; installing the reactor vibration isolation system according to the modified installation strategy. The present invention solves the technical problem in the related art that during the installation and height adjustment process of the reactor vibration isolation system, the performance changes of the damper cannot be effectively monitored, resulting in potential safety hazards.

Description

Technical Field

[0001] The present invention relates to the technical field of reactor vibration isolation processing, and in particular to an installation processing method and device for a reactor vibration isolation system. Background Art

[0002] During the installation and adjustment of the reactor vibration isolation system, the performance change of the damper is a key technical issue. As an important component of the vibration isolation system, the stability of the damper's performance directly affects the vibration isolation effect of the entire vibration isolation system.

[0003] However, in the actual process of system linkage height adjustment, the damping coefficient, damping force and other parameters of the damper may change, resulting in its inability to play the expected role in actual work. This performance change may be caused by many factors: slight deformation of the mechanical structure, aging of the damping material, the influence of temperature and humidity, etc. Especially in the long-term operation process, the performance degradation of the damper may gradually accumulate and appear. If this problem is not discovered and solved in time, the overall performance of the vibration isolation system will be affected, and it may even cause safety hazards.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The embodiment of the present invention provides an installation and processing method and device for a reactor vibration isolation system, so as to at least solve the technical problem in the related art that during the installation and height adjustment process of the reactor vibration isolation system, the performance change of the damper cannot be effectively monitored, resulting in safety hazards.

[0006] According to one aspect of an embodiment of the present invention, a method for installing and processing a reactor vibration isolation system is provided, comprising: simulating and analyzing a vibration isolation system model of the reactor vibration isolation system to obtain damping response data of a damper under different operating conditions of the reactor vibration isolation system; processing the damping response data to obtain a damper performance influencing factor, wherein the damper performance influencing factor is an influencing factor whose degree of influence on the performance of the damper is greater than an influence degree threshold; dynamically adjusting a control parameter of the damper according to an analysis result of the damper performance influencing factor; generating an installation strategy for the reactor vibration isolation system according to the damper performance influencing factor and the control parameter; determining vibration state data of the reactor vibration isolation system during a linkage height adjustment process according to the installation strategy; determining abnormal vibration data of the reactor vibration isolation system according to the vibration state data; modifying the installation strategy according to the abnormal vibration data to obtain the modified installation strategy; and installing the reactor vibration isolation system according to the modified installation strategy.

[0007] Optionally, a vibration isolation system model of the reactor vibration isolation system is simulated and analyzed to obtain damping response data of the damper under different operating conditions of the reactor vibration isolation system, including: generating a physical model of the reactor vibration isolation system; generating the vibration isolation system model according to the physical model, wherein the vibration isolation system model is used to describe the behavior of the reactor vibration isolation system in a dynamic environment; obtaining initial conditions of the working environment of the reactor vibration isolation system; and under the initial conditions, performing simulation analysis on the vibration isolation system model to obtain the damping response data.

[0008] Optionally, the damping response data is processed to obtain damper performance influencing factors, including: preprocessing the damping response data to obtain preprocessed damping response data; inputting the preprocessed damping response data into a damping response data identification model to process the preprocessed damping response data using the damping response data identification model to obtain a processing result, wherein the damping response data identification model is a model obtained by machine learning training using multiple sets of training data, each of the multiple sets of training data includes: sample damping response data and sample processing results corresponding to the sample damping response data, the processing results are the results of importance ranking and quantitative analysis of multiple performance influencing factors of the damper; and determining the damper performance influencing factors whose degree of influence on the damper is greater than the influence degree threshold according to the processing results.

[0009] Optionally, dynamically adjusting the control parameters of the damper according to the analysis results of the damper performance influencing factors includes: obtaining a vibration signal of the reactor vibration isolation system, wherein the vibration signal includes: vibration frequency, amplitude and damping ratio key parameters; analyzing the vibration signal according to a preset working range of the damper to obtain an analysis result; when it is determined that the analysis result indicates that the current working state of the reactor vibration isolation system is not in the preset working range, dynamically adjusting the control parameters of the damper according to the vibration signal and the damper performance influencing factors.

[0010] Optionally, dynamically adjusting the control parameters of the damper according to the vibration signal and the factors affecting the damper performance includes: adjusting the damping coefficient and damping force control parameters of the damper through the actuator of the reactor vibration isolation system so that the vibration signal of the reactor vibration isolation system remains within the preset working range.

[0011] Optionally, generating an installation strategy for the reactor vibration isolation system according to the damper performance influencing factors and the control parameters includes: generating a three-dimensional geometric model of the reactor vibration isolation system; meshing the three-dimensional geometric model by finite element analysis to obtain a meshing result of the three-dimensional geometric model; obtaining a mesh model data file of the reactor vibration isolation system according to the meshing result; inputting the mesh model data file into finite element analysis software, and after defining material properties, boundary conditions and load conditions of the reactor vibration isolation system, using the finite element analysis software to perform simulation analysis on the reactor vibration isolation system based on the mesh model data file to obtain natural frequency values ​​and vibration mode vectors of different orders of the reactor vibration isolation system; obtaining vibration characteristics and vibration isolation effects of the reactor vibration isolation system according to the natural frequency values ​​and vibration mode vectors of different orders; and determining the installation strategy according to the vibration characteristics and the vibration isolation effects.

[0012] Optionally, determining the installation strategy according to the vibration characteristics and the vibration isolation effect includes: obtaining an optimal damping coefficient and a damping force range of the damper; inputting the optimal damping coefficient and the damping force range into a finite element analysis model, so as to use the finite element analysis model to obtain the vibration characteristics and the vibration isolation effect of the reactor vibration isolation system under different damping parameters according to the optimal damping coefficient and the damping force range; generating a mapping relationship between the different damping parameters and the vibration isolation performance corresponding to the vibration characteristics and the vibration isolation effect; analyzing multiple initial installation strategies of the reactor vibration isolation system to obtain vibration isolation performance difference information of the multiple initial installation strategies; selecting an intermediate installation strategy according to the vibration isolation performance difference information; obtaining the damping parameter combination with the highest matching degree with the current installation strategy in the intermediate installation strategy by searching the mapping relationship; generating a parameter optimization model of the reactor vibration isolation system according to the natural frequency, vibration transmissibility and displacement response index of the reactor vibration isolation system, and taking the installation position and damping parameters of the reactor as design variables; and obtaining the installation strategy according to the parameter optimization model.

[0013] Optionally, determining abnormal vibration data of the reactor vibration isolation system according to the vibration state data includes: performing time domain analysis on time domain data in the vibration state data using wavelet transform to obtain wavelet coefficients and wavelet energy distribution characteristics in the vibration state data, and extracting frequency spectrum characteristics in the vibration state data using Fourier transform; obtaining a vibration feature vector corresponding to the vibration state data according to the wavelet coefficients, the wavelet energy distribution characteristics and the frequency spectrum characteristics; inputting the vibration feature vector into a vibration state recognition model to perform abnormal recognition on the vibration state data using the vibration state recognition model to obtain an abnormal recognition result, wherein the vibration state recognition model is a model obtained by machine learning training using multiple groups of training data, each of the multiple groups of training data includes: a sample vibration feature vector and a sample recognition result corresponding to the sample vibration feature vector; when it is determined according to the abnormal recognition result that abnormal vibration exists in the reactor vibration isolation system, the abnormal vibration data is obtained.

[0014] Optionally, installing the reactor vibration isolation system according to the modified installation strategy includes: acquiring current vibration data of the reactor vibration isolation system; processing the current vibration data using a sliding window method to obtain a vibration characteristic vector; comparing the vibration characteristic vector with a preset vibration level threshold to obtain a comparison result; when the comparison result indicates that the vibration characteristic vector exceeds the preset vibration level threshold, generating a control instruction based on the modified installation strategy; and sending the control instruction to an actuator of the reactor vibration isolation system, so as to use the actuator to install the reactor vibration isolation system according to the control instruction.

[0015] According to another aspect of an embodiment of the present invention, there is also provided an installation processing device for a reactor vibration isolation system, comprising: a simulation analysis unit, used for performing simulation analysis on a vibration isolation system model of the reactor vibration isolation system to obtain damping response data of a damper under different operating conditions of the reactor vibration isolation system; a processing unit, used for processing the damping response data to obtain a damper performance influencing factor, wherein the damper performance influencing factor is an influencing factor having an influence degree on the performance of the damper greater than an influence degree threshold; and a dynamic adjustment unit, used for dynamically adjusting the damper performance influencing factor according to the analysis result of the damper performance influencing factor. adjusting the control parameters of the damper; a generating unit, used to generate an installation strategy of the reactor vibration isolation system according to the damper performance influencing factors and the control parameters; a first determining unit, used to determine the vibration state data of the reactor vibration isolation system during the linkage height adjustment according to the installation strategy; a second determining unit, used to determine the abnormal vibration data of the reactor vibration isolation system according to the vibration state data; a correcting unit, used to correct the installation strategy according to the abnormal vibration data to obtain the corrected installation strategy; an installing unit, used to install the reactor vibration isolation system according to the modified installation strategy.

[0016] Optionally, the simulation and analysis unit includes: a first generation module, used to generate a physical model of the reactor vibration isolation system; a second generation module, used to generate the vibration isolation system model according to the physical model, wherein the vibration isolation system model is used to describe the behavior of the reactor vibration isolation system in a dynamic environment; a first acquisition module, used to obtain the initial conditions of the working environment of the reactor vibration isolation system; and a first simulation and analysis module, used to perform simulation analysis on the vibration isolation system model under the initial conditions to obtain the damping response data.

[0017] Optionally, the processing unit includes: a preprocessing module, used to preprocess the damping response data to obtain the preprocessed damping response data; a first processing module, used to input the preprocessed damping response data into a damping response data identification model, so as to process the preprocessed damping response data using the damping response data identification model to obtain a processing result, wherein the damping response data identification model is a model obtained by machine learning training using multiple groups of training data, each of the multiple groups of training data includes: sample damping response data and sample processing results corresponding to the sample damping response data, and the processing results are the results of importance ranking and quantitative analysis of multiple performance influencing factors of the damper; a first determination module, used to determine the damper performance influencing factors whose degree of influence on the damper is greater than the influence degree threshold according to the processing results.

[0018] Optionally, the dynamic adjustment unit includes: a second acquisition module, used to acquire a vibration signal of the reactor vibration isolation system, wherein the vibration signal includes: vibration frequency, amplitude and damping ratio key parameters; an analysis module, used to analyze the vibration signal according to a preset working range of the damper to obtain an analysis result; and a dynamic adjustment module, used to dynamically adjust the control parameters of the damper according to the vibration signal and the damper performance influencing factors when it is determined that the analysis result indicates that the current working state of the reactor vibration isolation system is not in the preset working range.

[0019] Optionally, the dynamic adjustment module includes: an adjustment submodule, used to adjust the damping coefficient and damping force control parameters of the damper through the actuator of the reactor vibration isolation system so that the vibration signal of the reactor vibration isolation system remains within the preset working range.

[0020] Optionally, the generation unit includes: a third generation module, which is used to generate a three-dimensional geometric model of the reactor vibration isolation system; a meshing module, which is used to mesh the three-dimensional geometric model by finite element analysis to obtain a meshing result of the three-dimensional geometric model; a third acquisition module, which is used to obtain a mesh model data file of the reactor vibration isolation system according to the meshing result; a second simulation analysis module, which is used to input the mesh model data file into finite element analysis software, and after defining the material properties, boundary conditions and load conditions of the reactor vibration isolation system, use the finite element analysis software to perform simulation analysis on the reactor vibration isolation system based on the mesh model data file to obtain natural frequency values ​​and vibration mode vectors of different orders of the reactor vibration isolation system; a fourth acquisition module, which is used to obtain the vibration characteristics and vibration isolation effect of the reactor vibration isolation system according to the natural frequency values ​​of different orders and the vibration mode vectors; and a second determination module, which is used to determine the installation strategy according to the vibration characteristics and the vibration isolation effect.

[0021] Optionally, the second determination module includes: a first acquisition submodule, used to obtain the optimal damping coefficient and damping force range of the damper; a second acquisition submodule, used to input the optimal damping coefficient and the damping force range into a finite element analysis model, so as to use the finite element analysis model to obtain the vibration characteristics and the vibration isolation effect of the reactor vibration isolation system under different damping parameters according to the optimal damping coefficient and the damping force range; a first generation submodule, used to generate a mapping relationship between the different damping parameters and the vibration isolation performance corresponding to the vibration characteristics and the vibration isolation effect; an analysis submodule, used to analyze multiple initial vibration isolation parameters of the reactor vibration isolation system; The invention discloses a method for analyzing the installation strategies to obtain the vibration isolation performance difference information of the multiple initial installation strategies; a screening submodule is used to screen out the intermediate installation strategies according to the vibration isolation performance difference information; a searching submodule is used to obtain the damping parameter combination with the highest matching degree with the current installation strategy in the intermediate installation strategies by searching the mapping relationship; a second generating submodule is used to generate a parameter optimization model of the reactor vibration isolation system according to the natural frequency, vibration transmissibility and displacement response index of the reactor vibration isolation system, and taking the installation position and damping parameters of the reactor as design variables; and a third acquiring submodule is used to obtain the installation strategy according to the parameter optimization model.

[0022] Optionally, the second determination unit includes: a time domain analysis module, which is used to perform time domain analysis on the time domain data in the vibration state data using wavelet transform to obtain wavelet coefficients and wavelet energy distribution characteristics in the vibration state data, and extract frequency spectrum characteristics in the vibration state data using Fourier transform; a fifth acquisition module, which is used to obtain a vibration feature vector corresponding to the vibration state data according to the wavelet coefficients, the wavelet energy distribution characteristics and the frequency spectrum characteristics; an abnormality identification module, which is used to input the vibration feature vector into a vibration state identification model to perform abnormality identification on the vibration state data using the vibration state identification model to obtain an abnormality identification result, wherein the vibration state identification model is a model obtained by machine learning training using multiple groups of training data, and each of the multiple groups of training data includes: a sample vibration feature vector and a sample identification result corresponding to the sample vibration feature vector; a sixth acquisition module, which is used to obtain the abnormal vibration data when it is determined that the reactor vibration isolation system has abnormal vibration according to the abnormality identification result.

[0023] Optionally, the installation unit includes: a seventh acquisition module, used to acquire current vibration data of the reactor vibration isolation system; a second processing module, used to process the current vibration data using a sliding window method to obtain a vibration characteristic vector; a comparison module, used to compare the vibration characteristic vector with a preset vibration level threshold to obtain a comparison result; a third generation module, used to generate a control instruction based on the revised installation strategy when the comparison result indicates that the vibration characteristic vector exceeds the preset vibration level threshold; an installation module, used to send the control instruction to the actuator of the reactor vibration isolation system, so as to use the actuator to install the reactor vibration isolation system according to the control instruction.

[0024] According to another aspect of the embodiments of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein the program executes any one of the above-mentioned methods for installing and processing a reactor vibration isolation system.

[0025] According to another aspect of the embodiments of the present invention, a processor is provided, and the processor is used to run a program, wherein the program executes any one of the above-mentioned installation processing methods for the reactor vibration isolation system when running.

[0026] According to another aspect of the embodiments of the present invention, a computer program product is provided, comprising computer instructions, which, when executed by a processor, execute any one of the above-mentioned methods for installing a reactor vibration isolation system.

[0027] In an embodiment of the present invention, a vibration isolation system model of a reactor vibration isolation system is simulated and analyzed to obtain damping response data of a damper under different operating conditions of the reactor vibration isolation system; the damping response data is processed to obtain a damper performance influencing factor, wherein the damper performance influencing factor is an influencing factor having an influence degree on the performance of the damper greater than an influence degree threshold; the control parameters of the damper are dynamically adjusted according to the analysis results of the damper performance influencing factor; an installation strategy of the reactor vibration isolation system is generated according to the damper performance influencing factor and the control parameter; vibration state data of the reactor vibration isolation system in a process of linkage height adjustment according to the installation strategy is determined; abnormal vibration data of the reactor vibration isolation system is determined according to the vibration state data; the installation strategy is corrected according to the abnormal vibration data to obtain a corrected installation strategy; and the reactor vibration isolation system is installed according to the modified installation strategy. Through the technical solution provided by the present invention, it is achieved that by establishing a mathematical model and performing simulation analysis, the damper response data under different operating conditions is obtained, the vibration isolation system is monitored and dynamically adjusted to cope with changes in operating conditions and vibration anomalies, the stable operation of the system is ensured, the safety of the reactor is improved, and the technical problem in the related art that during the installation and height adjustment of the reactor vibration isolation system, the performance changes of the damper cannot be effectively monitored, resulting in potential safety hazards. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0029] Figure 1 It is a hardware structure block diagram of a mobile terminal of an installation processing method of a reactor vibration isolation system according to an embodiment of the present invention;

[0030] Figure 2 is a flow chart of an installation processing method of a reactor vibration isolation system according to an embodiment of the present invention;

[0031] Figure 3 Schematic diagram of an installation and processing device for a reactor vibration isolation system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0032] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only 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 ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0033] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0034] As introduced in the background technology, in the related art, during the installation and height adjustment process of the reactor vibration isolation system, the performance change of the damper cannot be effectively monitored, which poses a safety hazard. In view of this defect, an installation processing method and device for a reactor vibration isolation system, a computer-readable storage medium, a processor, and a computer program product are provided in an embodiment of the present invention.

[0035] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0036] The method embodiments provided in the embodiments of the present invention can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 1 is a hardware structure block diagram of a mobile terminal of a method for installing and processing a reactor vibration isolation system according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0037] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the installation and processing method of the reactor vibration isolation system in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, the above method is implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the above-mentioned network include but are not limited to the Internet, corporate intranets, local area networks, mobile communication networks and combinations thereof. The transmission device 106 is used to receive or send data via a network. The above-mentioned specific examples of the network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0038] According to an embodiment of the present invention, a method embodiment of an installation processing method for a reactor vibration isolation system is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0039] Figure 2 is a flow chart of an installation processing method of a reactor vibration isolation system according to an embodiment of the present invention. Figure 2 As shown, the installation and processing method of the reactor vibration isolation system includes the following steps:

[0040] Step S202 , performing simulation analysis on the vibration isolation system model of the reactor vibration isolation system to obtain damping response data of the damper under different operating conditions of the reactor vibration isolation system.

[0041] Optionally, the damping response data may include, but is not limited to: vibration frequency, amplitude, damping ratio, and temperature.

[0042] In this embodiment, a vibration isolation system model of the reactor vibration isolation system may be established, and then the vibration isolation system model may be simulated and analyzed to obtain damping response data of the damper under different operating conditions of the reactor vibration isolation system.

[0043] Step S204: Process the damping response data to obtain a damper performance influencing factor, wherein the damper performance influencing factor is an influencing factor whose degree of influence on the performance of the damper is greater than an influence degree threshold.

[0044] Here, the random forest regression algorithm can be used to process the damping response data and identify the key factors affecting the damper performance.

[0045] Step S206: dynamically adjusting the control parameters of the damper according to the analysis results of the factors affecting the damper performance.

[0046] In this embodiment, the control parameters may be dynamically adjusted according to the analysis results of the factors affecting the damper performance to optimize the performance of the vibration isolation system.

[0047] Step S208: generating an installation strategy for the reactor vibration isolation system according to the damper performance influencing factors and control parameters.

[0048] In this embodiment, the vibration characteristics of the vibration isolation system under different installation conditions can be simulated in combination with the finite element analysis method, and the optimal installation scheme of the reactor vibration isolation system can be determined in combination with the damper performance analysis results.

[0049] Step S210, determining vibration state data of the reactor vibration isolation system during the process of linkage height adjustment according to the installation strategy.

[0050] Step S212: determining abnormal vibration data of the reactor vibration isolation system according to the vibration state data.

[0051] Step S214, correcting the installation strategy according to the abnormal vibration data to obtain a corrected installation strategy.

[0052] Step S216: Install the reactor vibration isolation system according to the modified installation strategy.

[0053] As can be seen from the above, in the embodiment of the present invention, a mathematical model of the reactor vibration isolation system is established, simulation analysis is performed according to design parameters and working environment, and response data of the damper under different operating conditions are obtained; the random forest regression algorithm is used to process the damping response data, identify the key factors affecting the damper performance, and quantify the degree of influence of the key factors on the damping behavior; the real-time monitoring data is used to dynamically adjust the control parameters of the damper according to the analysis results of the factors affecting the damper performance, and the performance of the vibration isolation system is optimized; the finite element analysis method is combined to simulate the vibration characteristics of the vibration isolation system under different installation conditions, and the optimal installation scheme of the reactor vibration isolation system is determined in combination with the damper performance analysis results; the long short-term memory network algorithm is used to implement The vibration state of the vibration isolation system during the linkage height adjustment process is evaluated and predicted in real time, and the vibration response data is input to discover potential vibration anomalies. When the risk of vibration anomaly is predicted, the early warning mechanism is triggered to form and transmit the optimized vibration isolation system height adjustment plan to the control unit to guide the automated height adjustment operation. During the height adjustment operation, the vibration data of the vibration isolation system is monitored and obtained in real time, and the actual vibration level is compared with the expected target. According to the comparison result, the control command is adjusted to return to the previous stable state. By establishing a mathematical model and simulation analysis, the damper response data under different operating conditions is obtained, and the vibration isolation system is monitored and dynamically adjusted to cope with changes in operating conditions and vibration anomalies. The purpose of ensuring the stable operation of the system is ensured, thereby improving the safety of the reactor.

[0054] Therefore, the above technical solution provided by the embodiment of the present invention solves the technical problem in the related art that during the installation and height adjustment process of the reactor vibration isolation system, the performance change of the damper cannot be effectively monitored, which poses a safety hazard.

[0055] According to the above-mentioned embodiment of the present invention, a vibration isolation system model of a reactor vibration isolation system is simulated and analyzed to obtain damping response data of a damper under different operating conditions of the reactor vibration isolation system, including: generating a physical model of the reactor vibration isolation system; generating a vibration isolation system model according to the physical model, wherein the vibration isolation system model is used to describe the behavior of the reactor vibration isolation system in a dynamic environment; obtaining initial conditions of the working environment of the reactor vibration isolation system; and under the initial conditions, performing simulation analysis on the vibration isolation system model to obtain damping response data.

[0056] In this embodiment, a physical model of the reactor vibration isolation system can be established. For example, the physical model consists of a mass block with a mass of 50 kg, a spring with a spring stiffness of 2000 N / m, and a damper with a damping coefficient of 150 Ns / m. According to the physical model of the reactor vibration isolation system, differential equations and boundary conditions are included to describe the behavior of the reactor vibration isolation system in a dynamic environment; and the initial conditions of the working environment are set, and simulation analysis is performed. The reactor vibration isolation system under the set working environment conditions is dynamically simulated through the simulation program. The simulation analysis results are collected, including the response data of the vibration isolation system (i.e., the reactor vibration isolation system) under different operating conditions.

[0057] The above differential equation is: , where m is the mass of the mass block kg, c is the damping coefficient of the damper Ns / m, k is the stiffness of the spring N / m, and x is the displacement of the mass block m. is the first derivative of displacement, i.e. velocity m / s, is the second-order derivative of displacement, i.e., acceleration m / s^2, F(t) is the external force N acting on the mass block, and the boundary conditions may be that both the initial displacement and initial velocity are 0. Write a simulation program, such as using MATLAB to write a program that uses the fourth-order Runge-Kutta method to solve the above differential equation. Set the initial conditions of the working environment, such as the working temperature is 25°C, the pressure is 1atm, and the load frequency is 60Hz. Perform simulation analysis, such as using the aforementioned simulation program to simulate the response of the vibration isolation system under a 60Hz frequency load. Collect simulation analysis results, such as the simulation shows that under a 60Hz frequency load, the maximum amplitude of the vibration isolation system is 0.5m. Analyze the response data through data processing software, such as using Excel or Python's Pandas library to perform statistical analysis on amplitude, frequency, displacement and other data under different load frequencies. Adjust the parameters in the simulation model, such as adjusting the damping coefficient to 200Ns / m and the spring stiffness to 2500N / m, and re-perform simulation analysis. By comparing the simulation results under different parameter settings, it is found that the adjusted damping coefficient and spring stiffness reduce the maximum amplitude of the reactor vibration isolation system to 0-3m under the same load frequency, thus selecting this set of parameters as the best design solution.

[0058] According to the above embodiment of the present invention, the damping response data is processed to obtain the damper performance influencing factors, including: preprocessing the damping response data to obtain preprocessed damping response data; inputting the preprocessed damping response data into a damping response data identification model to process the preprocessed damping response data using the damping response data identification model to obtain a processing result, wherein the damping response data identification model is a model obtained by machine learning training using multiple sets of training data, each of the multiple sets of training data includes: sample damping response data and sample processing results corresponding to the sample damping response data, the processing results are the results of importance ranking and quantitative analysis of multiple performance influencing factors of the damper; and determining the damper performance influencing factors whose influence on the damper is greater than an influence threshold according to the processing results.

[0059] In this embodiment, the damping response data of the reactor vibration isolation system under different working conditions can be obtained in advance, and the damping response data includes data of vibration frequency, amplitude, damping ratio, and temperature, and a data set is constructed, and the data in the data set is preprocessed; the preprocessed data set is randomly divided into a training set and a test set, the training set is used to train a random forest regression model, and the test set is used to evaluate the performance of the random forest regression model; a random forest regression algorithm is used to set the hyperparameters of the random forest regression model according to the characteristics of the damping response data, and the optimal hyperparameter combination is obtained through grid search or Bayesian optimization method; the random forest regression model with the optimal hyperparameter combination is used to fit the training set data, and the performance index of the random forest regression model on the training set is measured; the trained random forest regression model is used to rank the importance and quantitatively analyze the key factors affecting the damper performance, that is, the damping response data identification model is obtained by training in the above manner.

[0060] After the damping response data identification model is trained according to the above method, the model can be used to rank the importance and quantitatively analyze the key factors affecting the damper performance. According to the average impurity reduction or average precision reduction index of the features, the key factors affecting the damping response, including vibration frequency and damping ratio, can be identified, and the importance weight of each key factor can be given.

[0061] For example, the damping response data of the reactor vibration isolation system under different working conditions can be collected, including data in multiple dimensions such as vibration frequency, amplitude, damping ratio, temperature, etc., to build a complete data set, and preprocess the data, such as removing outliers and normalizing, to ensure data quality and consistency. The preprocessed damping response data is randomly divided into a training set and a test set. The training set is used to train the random forest regression model, and the test set is used to evaluate the performance and generalization ability of the model. The cross-validation method is used to perform random division of the data set and model training and testing multiple times to obtain the statistical distribution of the model performance.

[0062] Next, the random forest regression algorithm can be selected as the machine learning model. According to the characteristics of the damping response data, the hyperparameters of the model are set, such as the number of decision trees, the maximum depth, and the minimum number of samples for node splitting. Use grid search to select hyperparameters, set the search range of the number of decision trees to [50, 100, 200, 500], the search range of the maximum depth to 5, 10, 20, None, and the search range of the minimum number of samples for node splitting to [2, 5, 10]. Take the mean square error (MSE) or mean absolute error (MAE) as the optimization target, and select the hyperparameter combination that minimizes the MSE or MAE on the validation set as the optimal combination. Use the random forest regression model with the optimal hyperparameter combination to fit the training set data, measure the performance indicators of the model on the training set, such as the mean square error, determination coefficient, etc., to determine whether the model has overfitting or underfitting problems, and if necessary, increase the amount of data for the model.

[0063] Then, the trained random forest regression model is applied to the test set data to evaluate the prediction performance of the random forest regression model on unknown data, calculate the generalization error and confidence interval of the random forest regression model, and judge whether the random forest regression model has good generalization ability and robustness. If the performance does not meet the requirements, the random forest regression model needs to be further optimized.

[0064] Subsequently, the trained random forest regression model was used to rank and quantify the key factors affecting the damper performance. The feature importance was measured according to the average variance reduction of the feature, and the key factors that had the greatest impact on the damping response, such as vibration frequency and damping ratio, were identified, and the importance weights of each factor were given. Based on the importance analysis results of the key factors, the design parameters and control strategies of the reactor vibration isolation system were optimized.

[0065] If the vibration frequency is the most important influencing factor, it can be optimized and the natural frequency f of the reactor vibration isolation system can be calculated. It can be estimated by the theoretical formula f=1 / (2π√(L / k)), where L is the inductance of the reactor and k is the stiffness of the vibration isolation system. According to the main excitation frequency under actual working conditions, the appropriate reactor inductance is selected to make the natural frequency of the vibration isolation system far away from the excitation frequency. Generally speaking, the natural frequency can be made 1 / 2 or 1 / √2 times the excitation frequency to obtain a good vibration isolation effect. For reactors with determined inductance, the quality factor of the reactor can be optimized by adjusting parameters such as the number of coil turns, coil diameter, and core material. While ensuring sufficient inductance, the quality factor of the reactor can be improved, energy loss can be reduced, and vibration isolation efficiency can be improved. When collecting damping response data of the reactor vibration isolation system, an accelerometer and a data acquisition card can be used. The sampling frequency is set to 1kHz. 10 seconds of data are collected under each working condition, and a total of 10,000 data points are obtained. The collected data were preprocessed. The 3σ criterion was first used to remove outliers, and then the minimum-maximum normalization method was used to normalize the data to the interval [0,1] to ensure the scale consistency between different features. The preprocessed data were randomly divided into training and test sets in a ratio of 8:2. The 5-fold cross-validation method was used to repeat the data set division and model training and testing 5 times, and the average of the 5 results was taken as the final performance indicator of the model. When selecting hyperparameters, random search was first used to determine the better hyperparameter range, and then grid search was used to fine-tune within this range. For example, the optimal range of the number of decision trees obtained by random search was [100,200], and the optimal range of the maximum depth was [10,20]. In this range, grid search was used with the step sizes set to 50 and 5 respectively. The optimal hyperparameter combination was finally obtained as follows: the number of decision trees was 150, the maximum depth was 15, and the minimum number of samples for node splitting was 5. The random forest regression model was trained using this hyperparameter combination. The mean square error on the training set was 0.05, the determination coefficient was 0.95, and the mean square error on the test set was 0.08, and the determination coefficient was 0.92, indicating that the random forest regression model has good fitting ability and generalization ability. According to the results of feature importance analysis, the importance score of vibration frequency is 0.6, and the importance score of damping ratio is 0.3. Both are the most important factors affecting the performance of the damper. For the key factor of vibration frequency, the natural frequency of the reactor vibration isolation system is calculated to be 5Hz, while the main excitation frequency under actual working conditions is 10Hz. Therefore, the reactor inductance value is selected to be 20mH, so that the natural frequency of the vibration isolation system is 10 / √2≈7.07Hz, which forms a good staggered with the excitation frequency. At the same time, by optimizing the number of turns and diameter of the reactor coil, the quality factor is increased from 50 to 100, further reducing the energy loss.

[0066] According to the above embodiment of the present invention, dynamically adjusting the control parameters of the damper according to the analysis results of the factors affecting the damper performance includes: obtaining a vibration signal of the reactor vibration isolation system, wherein the vibration signal includes: vibration frequency, amplitude and damping ratio key parameters; analyzing the vibration signal according to the preset working range of the damper to obtain an analysis result; when it is determined that the analysis result indicates that the current working state of the reactor vibration isolation system is not in the preset working range, dynamically adjusting the control parameters of the damper according to the vibration signal and the factors affecting the damper performance.

[0067] In this embodiment, a sensor is used to monitor the vibration signal of the vibration isolation system in real time, including key parameters of vibration frequency, amplitude and damping ratio; according to a preset optimal working range of the damper, the real-time monitoring data is analyzed to determine whether the current working state of the vibration isolation system is in the optimal range, and if not in the optimal range, the adaptive control strategy is started; the adaptive control strategy dynamically adjusts the parameters of the PID controller according to the frequency and amplitude of the vibration signal, generates control instructions, and adjusts the damping coefficient and damping force control parameters of the damper in real time through the actuator, so that the vibration response of the vibration isolation system is always kept within the optimal working range; in the process of adjusting the control parameters, the Kalman filter algorithm is used to filter the data collected by the sensor, and the control parameters are dynamically adjusted according to the actual response of the vibration isolation system.

[0068] For example, based on the analysis results of the factors affecting the performance of the damper, the vibration signal of the vibration isolation system can be monitored in real time using sensors, including key parameters such as vibration frequency, amplitude, and damping ratio. Industrial Ethernet protocols such as PROFINET and EtherCAT can be used to connect the sensor node to the controller through a bus to achieve real-time data transmission. The sensor node converts the analog quantity into a digital quantity through A / D conversion, packages it into a standard data frame, and sends it to the controller through the Ethernet protocol. After receiving the data, the controller unpacks and parses the data according to the predetermined parsing rules, converts the data into physical values ​​for subsequent control algorithms, and performs preprocessing such as outlier filtering and data synchronization on the data to ensure data quality and reliability. The adaptive control algorithm embedded in the controller uses a combination of experimental testing and theoretical analysis to test the vibration isolation performance of the damper at different vibration frequencies and amplitudes, and obtains the corresponding relationship curve between key parameters such as damping ratio and attenuation coefficient and vibration signals. According to the specific application requirements of the vibration isolation system, the optimal working range of the damper is determined, and the mapping relationship table between vibration frequency, amplitude and damper parameters is obtained. According to the real-time monitoring data, it is judged whether the working state of the current vibration isolation system is in the optimal range. If it is not in the optimal range, the adaptive control strategy is started.

[0069] The above adaptive control strategy adopts fuzzy PID control algorithm, selects the frequency error E and frequency error change rate EC of vibration signal as input of fuzzy controller, and the correction values ​​ΔKp, ΔKi, ΔKd of damping ratios Kp, Ki, Kd as output, establishes fuzzy control rule table, and adopts Mamdani reasoning method and center of gravity defuzzification method to dynamically adjust the parameters of PID controller according to the frequency and amplitude of vibration signal, generate control instructions, and adjust the damping coefficient, damping force and other control parameters of damper in real time through actuator, so that the vibration response of vibration isolation system is always kept in the optimal working range. In the process of adjusting control parameters, Kalman filter algorithm is used to filter the data collected by sensor, eliminate noise interference, and improve data accuracy and reliability. At the same time, adaptive weight update strategy is adopted, and gradient descent method is used to define performance index function J to represent the sum of square errors between actual response and expected response of the system. By taking partial derivatives of J with respect to each weight coefficient w, gradient vector is obtained, and weight coefficient is updated along the negative gradient direction until J converges to the minimum value, so as to realize adaptive optimization of control strategy. Through continuous iterative optimization of the adaptive control algorithm, the control accuracy and response speed are continuously improved, the vibration attenuation performance of the vibration isolation system is significantly improved, and the vibration transmissibility is reduced by more than 20%; and the reinforcement learning method is used to regard the vibration isolation system as an intelligent agent, the output of the controller as the action of the intelligent agent, and the actual response of the vibration isolation system as the feedback of the environment. Through the Q-learning algorithm, the optimal control strategy is found, and the state, action, reward value and other information of each time are stored in the experience playback buffer in the form of tuples, and the data is sampled and updated using the priority sampling method to improve the sample utilization efficiency and learning speed. The Q value table or policy network finally obtained is used as the optimal control policy library and stored in the controller memory for rapid adaptation and optimization control under unknown working conditions. The policy library is regularly updated and maintained to adapt to long-term changes in the system and performance degradation.

[0070] When monitoring the vibration signal of the vibration isolation system in real time, an acceleration sensor and a data acquisition card are selected. The sensitivity of the sensor is 100mV / g, the frequency response range is 0.1Hz10kHz, the sampling frequency is set to 10kHz, and 1024 data points are collected in each sampling cycle. The data is transmitted to the controller at a rate of 100Mbps through the PROFINET protocol.

[0071] After receiving the data, the controller parses it according to the predefined data frame format, performs spectrum analysis on the data through the FFT algorithm, obtains the characteristic parameters of the vibration signal such as frequency and amplitude, and detects and filters the data through the set threshold value to remove the data points beyond the normal range. According to the test results, the optimal damping ratio range of the damper is 0.20.4, the optimal attenuation coefficient range is 24, the corresponding vibration frequency range is 10Hz50Hz, and the vibration amplitude range is 0.1g-0.5g. The optimal control parameters corresponding to the current vibration signal are obtained by looking up the table. The fuzzification process of the fuzzy PID controller adopts a triangular membership function, the domain range is [-3,3], the fuzzy subset is {NB, NM, NS, ZO, PS, PM, PB}, and there are 49 fuzzy control rules in total. The domain range of the output quantities ΔKp, ΔKi, and ΔKd is [-0.3, 0.3]. The center of gravity defuzzification method is used to obtain the accurate value of the control quantity. In the adaptive weight update strategy, the performance index function J=∑(f-f_d)^2+∑(ζ-ζ_d)^2 is set, where f is the actual frequency, f_d is the target frequency, ζ is the actual damping ratio, and ζ_d is the target damping ratio. The Adam algorithm is used to adaptively update the weight coefficient, the learning rate is set to 0.01, the momentum factor is 0.9, and the weight coefficient is updated every 100 sampling cycles. When the optimal control strategy library is generated by reinforcement learning, the state space is set to the combination of vibration frequency and vibration amplitude, the action space is set to the damping coefficient and damping force of the damper, the reward function is set to R=-J, and the Q-learning algorithm is used for learning. The learning rate is set to 0.1, the discount factor is set to 0.9, the experience replay buffer size is set to 10,000, and 64 samples are sampled each time. When the Q value converges or the number of iterations reaches 10,000, the learning is stopped, and the converged Q table is stored as the optimal control strategy library.

[0072] In the above embodiment, the control parameters of the damper are dynamically adjusted according to the vibration signal and the factors affecting the damper performance, including: adjusting the damping coefficient and damping force control parameters of the damper through the actuator of the reactor vibration isolation system so that the vibration signal of the reactor vibration isolation system is maintained within a preset working range.

[0073] In this embodiment, the damping coefficient and the damping force control parameters of the damper can be adjusted by the actuator, so that the vibration signal of the reactor vibration isolation system is maintained within a preset working range.

[0074] According to the above-mentioned embodiment of the present invention, generating an installation strategy for a reactor vibration isolation system based on damper performance influencing factors and control parameters may include: generating a three-dimensional geometric model of the reactor vibration isolation system; meshing the three-dimensional geometric model by finite element analysis to obtain a meshing result of the three-dimensional geometric model; obtaining a mesh model data file of the reactor vibration isolation system according to the meshing result; inputting the mesh model data file into finite element analysis software, and after defining material properties, boundary conditions and load conditions of the reactor vibration isolation system, using the finite element analysis software to perform simulation analysis on the reactor vibration isolation system based on the mesh model data file to obtain natural frequency values ​​and vibration mode vectors of different orders of the reactor vibration isolation system; obtaining vibration characteristics and vibration isolation effects of the reactor vibration isolation system according to natural frequency values ​​and vibration mode vectors of different orders; and determining an installation strategy according to the vibration characteristics and vibration isolation effects.

[0075] In this embodiment, a three-dimensional geometric model of the vibration isolation system can be established, and the three-dimensional geometric model can be meshed using the finite element method to generate a mesh model data file; the mesh model data file is imported into the finite element analysis software to define material properties, boundary conditions and load conditions; the modal analysis method is used to calculate the natural frequency and vibration mode of the vibration isolation system, obtain the natural frequency values ​​of different orders and the corresponding vibration mode vectors, and judge the vibration characteristics and vibration isolation effect of the vibration isolation system based on the shape and energy distribution of the vibration mode; the optimal damping coefficient and damping force range of the reactor damper are obtained, and input into the finite element model to obtain the vibration isolation system under different damping parameters. The vibration characteristics and vibration isolation effects of the vibration isolation system are obtained to form a database of corresponding relationships between damping parameters and vibration isolation performance; by comparing and analyzing the differences in vibration isolation performance of different installation schemes, the installation schemes are preliminarily screened out; the installation schemes preliminarily screened out are matched with the damper performance analysis results, and the damping parameter combination that best matches the current installation scheme is found by querying the database of corresponding relationships between damping parameters and vibration isolation performance; a multi-objective optimization algorithm is adopted, and the natural frequency, vibration transmissibility and displacement response indicators of the vibration isolation system are taken as optimization targets, and the installation position and damping parameters of the reactor are taken as design variables, a parameter optimization model of the vibration isolation system is established, and the optimal installation scheme of the reactor vibration isolation system is found out.

[0076] For example, according to the structural parameters and material properties of the reactor vibration isolation system, a three-dimensional geometric model of the vibration isolation system is established, and the finite element method is used to mesh the model to generate a mesh model data file containing node and unit information. The mesh model is imported into the finite element analysis software to define material properties, boundary conditions and load conditions, etc. The material properties include the elastic modulus, Poisson's ratio, density, etc. of the reactor coil and magnetic core, the boundary conditions include the displacement constraint of the reactor installation point and the fixed constraint of the vibration isolation platform, and the load conditions include the frequency, amplitude and direction of the vibration excitation. The modal analysis method is used to calculate the natural frequency and vibration mode of the vibration isolation system, and the natural frequency values ​​and corresponding vibration mode vectors of different orders are obtained. The vibration characteristics and vibration isolation effect of the vibration isolation system are judged according to the shape and energy distribution of the vibration mode. At the same time, the harmonic response analysis method is used to apply unit simple harmonic excitation to the vibration isolation system at different frequencies, and the displacement, velocity, acceleration and other response quantities of the system are calculated to obtain the frequency response function of the vibration isolation system, so as to evaluate the attenuation capacity and isolation effect of the vibration isolation system to external vibration.

[0077] According to the results of the damper performance analysis, the optimal damping coefficient and damping force range of the reactor damper are obtained and input into the finite element model as parameters. Through parametric modeling and batch calculation, the vibration characteristics and vibration isolation effects of the vibration isolation system under different damping parameters are obtained, and a corresponding relationship database between damping parameters and vibration isolation performance is formed. In the specific implementation, Latin hypercube sampling and other methods are first used to generate enough sample points in the parameter space, and then the sample points are substituted into the vibration isolation system model for calculation to obtain the parameter-performance data set. Then, the functional relationship between the damping parameter and the vibration isolation performance index is established through multivariate regression analysis, which is stored in a relational database or NoSQL database, and an efficient index and query mechanism is established to facilitate subsequent parameter matching and optimization design. For different reactor installation schemes, such as bottom installation, side installation, distributed installation, etc., corresponding finite element models are established respectively, and modal analysis and harmonic response analysis are performed to obtain characteristic parameters such as the natural frequency, vibration mode, and frequency response function of the vibration isolation system under different installation schemes.

[0078] By comparing the differences in natural frequency, vibration mode orthogonality, frequency response curve and modal strain energy distribution of different schemes, and combining the design constraints for comprehensive evaluation, the better reactor installation scheme is preliminarily screened out. The selected installation scheme is matched with the damper performance analysis results. By querying the database of the correspondence between damping parameters and vibration isolation performance, the damping parameter combination that best matches the current installation scheme is found, the optimal working point of the reactor damper is determined, and it is used as the initial design parameter for subsequent multi-objective optimization design. In the multi-objective optimization design of the vibration isolation system, the optimization targets are the natural frequency, vibration transmissibility, displacement response and other indicators, and the reactor installation position, damping parameters and other design variables are used to establish an optimization mathematical model.

[0079] The multi-objective optimization algorithm such as genetic algorithm is used to solve the problem. The design variables are first encoded to form chromosome individuals, and then the initial population is generated. Through genetic operations such as fitness evaluation, selection, crossover and mutation, the Pareto optimal solution set is iteratively searched until the set convergence condition is reached. Finally, a satisfactory solution is selected from the Pareto optimal solution set as the optimal design parameter combination of the vibration isolation system. The reliability analysis and robustness evaluation of the optimal installation scheme are carried out. By introducing random disturbances on the design parameters, the uncertainty and changes of the reactor installation position and damping parameters are simulated, and the probability distribution and confidence interval of the vibration isolation system performance are calculated. The reliability and robustness of the optimal installation scheme are judged, and necessary corrections and improvements are made to obtain the optimal installation scheme of the reactor vibration isolation system that meets the vibration isolation performance requirements and has high reliability and robustness. In order to evaluate the reliability and robustness of the obtained optimal design parameters, it is necessary to further carry out uncertainty analysis and robustness optimization.

[0080] According to the above embodiment of the present invention, the installation strategy is determined according to the vibration characteristics and the vibration isolation effect, including: obtaining the optimal damping coefficient and damping force range of the damper; inputting the optimal damping coefficient and damping force range into the finite element analysis model, so as to use the finite element analysis model to obtain the vibration characteristics and vibration isolation effect of the reactor vibration isolation system under different damping parameters according to the optimal damping coefficient and damping force range; generating a mapping relationship between different damping parameters and the vibration isolation performance corresponding to the vibration characteristics and the vibration isolation effect; analyzing multiple initial installation strategies of the reactor vibration isolation system to obtain vibration isolation performance difference information of the multiple initial installation strategies; screening out the intermediate installation strategy according to the vibration isolation performance difference information; obtaining the damping parameter combination with the highest matching degree with the current installation strategy in the intermediate installation strategy by searching the mapping relationship; generating a parameter optimization model of the reactor vibration isolation system according to the natural frequency, vibration transmissibility and displacement response index of the reactor vibration isolation system, and taking the installation position and damping parameters of the reactor as design variables; and obtaining the installation strategy according to the parameter optimization model.

[0081] Here, the uncertainty of the design parameters can be modeled first, such as using a probability distribution model to describe the random fluctuations of the reactor installation position, damping parameters, etc., and then a large number of random sample points are generated by the Monte Carlo simulation method, which are substituted into the vibration isolation system model for calculation to obtain the probability distribution of the vibration isolation performance index, and calculate the reliability index such as failure probability and reliability index. The reliability index and sensitivity index are introduced into the original multi-objective optimization model, and the robust optimization model is reconstructed. The genetic algorithm and other methods are used to solve the problem, and the robust optimal solution that can still maintain good vibration isolation performance under random disturbances is obtained as the final design scheme of the reactor vibration isolation system. The design scheme is verified by engineering tests to test its vibration isolation performance and reliability level under actual working conditions. If necessary, optimization iterations are performed until the design requirements are met. When establishing the finite element model of the reactor vibration isolation system, hexahedral units are used to mesh the reactor coils and magnetic cores. The unit size is set to 1mm, and the generated mesh model contains about 500,000 nodes and 300,000 units. In terms of material properties, the elastic modulus of the reactor coil is 110GPa, the Poisson's ratio is 0.33, and the density is 8.96g / cm³; the elastic modulus of the magnetic core is 200GPa, the Poisson's ratio is 0.28, and the density is 7.80g / cm³. The boundary conditions are set as follows: all nodes on the bottom mounting surface of the reactor constrain 6 degrees of freedom, and the nodes on the top connecting the vibration isolation platform constrain the X, Y, RX, RY, and RZ degrees of freedom. The load condition is set as follows: a 1g sine sweep excitation is applied to the vibration isolation platform, and the frequency range is 0.1Hz100Hz. The modal analysis calculation shows that the first 10 natural frequencies are 3.5Hz, 7.8Hz, 15.6Hz, etc., and the vibration mode includes translation in the X, Y, and Z directions and rotation around the X, Y, and Z axes. In the harmonic response analysis, the vibration isolation system has a resonance peak near 5Hz, the peak transmission rate reaches 10 times, and the vibration above 20Hz is significantly attenuated, and the attenuation rate is better than -40dB / dec. According to the hysteresis loop and stress-strain curve of the reactor, the optimal range of the damping coefficient is 500-1500N·s / m, and the optimal range of the damping force is 200-800N. The Latin hypercube sampling method is used to randomly generate 100 groups of sample points within the above parameter range, and the sample points are substituted into the finite element model for batch calculation to obtain performance data such as frequency response curve and vibration transmissibility surface. The functional relationship between the damping coefficient, damping force, natural frequency and vibration transmissibility is established by using polynomial regression and radial basis function interpolation, and stored in the MySQL relational database. The B+ tree index is used to accelerate parameter query.

[0082] Considering the three schemes of reactor bottom installation, side installation and distributed installation, parameterized finite element models were established respectively. The first three natural frequencies were obtained by modal analysis, and the energy distribution ratio between the reactor and the vibration isolation platform was obtained by strain energy analysis. The peak value and attenuation rate of the frequency response curve were scored. The comprehensive sorting results showed that the performance of the distributed installation scheme was the best, the natural frequency was higher than 50Hz, the strain energy of the reactor accounted for more than 90%, and the vibration transmissibility was better than -50dB / dec. The initial design parameters of the distributed installation scheme were matched with the best damping parameters in the database, and the damping force was 400N. With the natural frequency, vibration transmissibility and displacement response as the optimization targets, and the reactor installation coordinates (X, Y, Z) and damping parameters (C, F) as the design variables, the NSGA-II algorithm was used for multi-objective optimization, with a population size of 50, a genetic generation of 100, a crossover probability of 0.8, and a mutation probability of 0.1. The Pareto optimal solution set converged to 6 non-dominated solutions. Considering the ±5mm processing error of reactor installation and the ±10% fluctuation of damping coefficient, 1000 sample points were randomly selected by Monte Carlo method, the reliability of Pareto solution set was calculated, and Sobol sensitivity analysis was performed. It was found that the Z-direction installation position and damping coefficient of the reactor were the key parameters affecting the vibration isolation performance. Based on this, a robust optimization model was constructed and solved by sequential quadratic programming algorithm. The robust optimal solution was obtained: the reactor installation coordinates were (0,0,50) mm, the damping coefficient was 850 N·s / m, the damping force was 380 N, the vibration transmissibility was better than -55 dB in the 3σ interval, and the reliability reached 99.73%. The prototype test of the scheme was carried out, and the test results were in good agreement with the simulation predictions, and all performance indicators met the design requirements.

[0083] According to the above embodiment of the present invention, abnormal vibration data of the reactor vibration isolation system is determined according to the vibration state data, including: using wavelet transform to perform time domain analysis on time domain data in the vibration state data to obtain wavelet coefficients and wavelet energy distribution characteristics in the vibration state data, and using Fourier transform to extract frequency spectrum characteristics in the vibration state data; obtaining a vibration feature vector corresponding to the vibration state data according to the wavelet coefficients, wavelet energy distribution characteristics and frequency spectrum characteristics; inputting the vibration feature vector into a vibration state recognition model to perform abnormal recognition on the vibration state data using the vibration state recognition model to obtain an abnormal recognition result, wherein the vibration state recognition model is a model obtained by machine learning training using multiple sets of training data, and each of the multiple sets of training data includes: a sample vibration feature vector and a sample recognition result corresponding to the sample vibration feature vector; when it is determined according to the abnormal recognition result that the reactor vibration isolation system has abnormal vibration, abnormal vibration data is obtained.

[0084] In this embodiment, the vibration state of the vibration isolation system during the linkage height adjustment process can be evaluated and predicted in real time through the long short-term memory network algorithm, and the vibration response data can be input to discover potential vibration anomalies.

[0085] Specifically, multiple vibration sensors can be arranged at the position of the vibration isolation system, and the vibration sensors are used to collect the vibration response data of the vibration isolation system in the process of linkage height adjustment in real time, and the collected vibration response data is preprocessed; the time domain data collected by the vibration sensor is analyzed in the time and frequency domains by wavelet transform, and the wavelet coefficients and wavelet energy distribution characteristics of the vibration signal are extracted. At the same time, the spectrum characteristics of the vibration signal are extracted by Fourier transform, and the vibration feature vector is constructed in combination with the statistical characteristics as the input of the long short-term memory network model; a deep learning model based on the long short-term memory network is built, and the input of the model is a vibration feature vector sequence, and the output is the vibration state prediction result of the vibration isolation system, including whether the vibration is abnormal and the type and degree of the abnormality; the long short-term memory network model is trained using the historical vibration data of the vibration isolation system under different working conditions, and the training data needs to contain samples of normal vibration and various abnormal vibrations; the trained long short-term memory network model is deployed to the edge computing unit of the vibration isolation system to predict the vibration data collected in real time.

[0086] For example, multiple vibration sensors are arranged at key positions of the vibration isolation system to collect vibration response data of the vibration isolation system in the process of linkage height adjustment in real time. The sampling frequency of the sensor is set to 1kHz, and 1024 data points are collected in each sampling cycle. The collected data is transmitted to the edge computing unit in real time through industrial Ethernet for preprocessing. Wavelet transform is used to analyze the time domain data collected by the vibration sensor in the time and frequency domain, and the wavelet coefficients and wavelet energy distribution characteristics of the vibration signal are extracted. At the same time, Fourier transform is used to extract the spectral characteristics of the vibration signal, and combined with statistical features such as mean, variance, peak, etc., a multi-dimensional vibration feature vector is constructed as the input of the subsequent long short-term memory network model. A deep learning model based on the long short-term memory network (LSTM) is built. The model adopts a three-layer hidden layer structure, and each hidden layer contains 128 LSTM neurons. The input layer receives a vibration feature vector sequence of length T, and each time step corresponds to a feature vector; the output layer adopts a fully connected structure to output the vibration state prediction result of the vibration isolation system. The hidden layers are connected by forward propagation, and the output is nonlinearly transformed using the tanh activation function. A Dropout layer is added after each hidden layer to randomly discard the output of some neurons to prevent the model from overfitting. When training the LSTM network model, the cross entropy loss function is used to measure the difference between the predicted results and the true labels, and the Adam optimizer is used to update the model parameters, and the learning rate is set to 0.001. The historical vibration data of the vibration isolation system under different working conditions are used to train the LSTM network model. The training data needs to contain samples of normal vibration and various abnormal vibrations, and data enhancement and balancing are performed. Common methods of data enhancement include random cropping, translation, scaling and other operations on time domain signals, random masking, perturbation and other transformations on the spectrum of vibration signals in the frequency domain, and the use of generative adversarial network (GAN) models to generate realistic virtual abnormal samples. The purpose of data balancing is to eliminate the disparity in the number of normal samples and abnormal samples. Common methods include undersampling normal samples, oversampling abnormal samples, and using the SMOTE algorithm to interpolate and generate new virtual abnormal samples. During the training process, the model is evaluated by cross-validation method, and the model's hyperparameters are continuously adjusted until the model's performance indicators on the validation set, such as accuracy and recall, reach the predetermined threshold. The trained long short-term memory network model is deployed to the edge computing unit of the vibration isolation system to predict the vibration data collected in real time. When predicting vibration anomalies, in addition to inputting the feature vector sequence of the vibration signal, the operating parameters of the vibration isolation system, such as reactor current, damping coefficient, temperature, etc., can also be used as auxiliary inputs, and the multi-head attention mechanism is used for feature fusion to extract comprehensive feature representation.

[0087] In addition, piezoelectric accelerometers can be arranged at four key positions of the vibration isolation system, with a sampling frequency of 2kHz. Each sensor collects 2048 data points per second, and transmits the data to the edge computing unit in real time through the Profinet industrial Ethernet protocol. The vibration signal is decomposed into five layers using Daubechies4 wavelet, and the root mean square value of the wavelet coefficient and the wavelet energy spectrum entropy are extracted as features. The spectral center and spectral peak of the vibration signal are extracted through 512-point fast Fourier transform to construct a 36-dimensional vibration feature vector. A four-layer long short-term memory network model is built. The first layer contains the vibration feature vector sequence, the second to fourth layers each contain 128 LSTM units, and the last layer Softmax classifier outputs the predicted probability of the vibration state. The long short-term memory network model is trained with the Adam optimizer for 50 epochs, the batch size is 32, the cross entropy loss function, and the L2 regularization coefficient is 0.001. The long short-term memory network model is trained using 10,000 historical vibration data, including 8,000 normal samples and 2,000 abnormal samples. Data enhancement uses frequency domain translation (±1Hz), time domain cropping (random cropping of 10% length) and Gaussian white noise (signal-to-noise ratio 20dB) of vibration signals, and the oversampling ratio of abnormal samples is 5 times. The accuracy of the long short-term memory network model on the test set reached 98.5%, and the recall rate of abnormal samples reached 95.2%. The trained long short-term memory network model was run on the vibration isolation system, and the collected vibration data was predicted once every 0.1 seconds. At the same time, 8 operating parameters such as reactor current, damping coefficient, and temperature were used as auxiliary inputs, and feature fusion was performed through the five-head attention mechanism. The prediction confidence threshold was set to 0.9.

[0088] According to the above embodiment of the present invention, installing the reactor vibration isolation system according to the modified installation strategy includes: acquiring current vibration data of the reactor vibration isolation system; processing the current vibration data by using a sliding window method to obtain a vibration characteristic vector; comparing the vibration characteristic vector with a preset vibration level threshold to obtain a comparison result; when the comparison result indicates that the vibration characteristic vector exceeds the preset vibration level threshold, generating a control instruction based on the modified installation strategy; and sending the control instruction to an actuator of the reactor vibration isolation system, so as to use the actuator to install the reactor vibration isolation system according to the control instruction.

[0089] In this embodiment, when the risk of abnormal vibration is predicted, the early warning mechanism is triggered, and an optimized vibration isolation system height adjustment plan is formed and transmitted to the control unit to guide the automated height adjustment operation.

[0090] When the long short-term memory network model predicts that the vibration isolation system has an abnormal vibration risk, the early warning mechanism is immediately triggered, and the abnormal risk information and vibration characteristic parameters are sent to the expert knowledge base system; the expert knowledge base system combines the current operating parameters and historical operation data of the vibration isolation system to generate an optimized vibration isolation system height adjustment plan; the optimized vibration isolation system height adjustment plan is sent to the control unit of the vibration isolation system; the control unit sends control instructions to each actuator for the automated height adjustment operation of the vibration isolation system; during the execution of the height adjustment operation, the control unit collects the feedback signals of each sensor in real time, and compares them with the expected values ​​in the optimized vibration isolation system height adjustment plan to monitor the execution effect of the height adjustment operation.

[0091] For example, when the long short-term memory network model predicts that the vibration isolation system has a risk of abnormal vibration, the early warning mechanism is immediately triggered to send the abnormal risk information and key vibration characteristic parameters to the expert knowledge base system. The expert knowledge base system adopts a rule-based reasoning method to match the pre-set abnormality handling rules according to the type, frequency, amplitude and other characteristics of the vibration abnormality, and combines the working parameters and historical operation data of the current vibration isolation system to generate an optimized vibration isolation system height adjustment plan. While generating the optimized vibration isolation system height adjustment plan, the expert system will use the digital twin model of the vibration isolation system to simulate and verify the plan. The digital twin model is constructed through CAD models, multi-physics field simulation, real-time data mapping and other technologies, and can reproduce the geometric structure, physical characteristics and operating status of the vibration isolation system with high precision and visualization. In the simulation verification, the expert system inputs the parameter adjustment and control instructions of the optimization plan into the digital twin model, simulates the execution process of the plan, and predicts and analyzes the vibration response, displacement state and other key performance indicators of the vibration isolation system, while evaluating the energy consumption, efficiency, safety and other factors of the plan to optimize the economy and reliability of the plan. The simulation verification results are fed back to the expert system to further correct and improve the optimized vibration isolation system height adjustment plan to improve the pertinence and effectiveness of the plan. The optimized vibration isolation system height adjustment plan contains multiple levels of height adjustment strategies and operation instructions, such as reactor damping parameter adjustment, vibration isolation platform posture adjustment, vibration active control algorithm parameter optimization, etc., and the strategies are prioritized according to the severity and urgency of the abnormality. The optimized vibration isolation system height adjustment plan is sent to the control unit of the vibration isolation system in a structured form. The control unit parses and converts the plan to generate a series of executable control instruction sequences, and reasonably arranges the execution order and time nodes according to the priority and time requirements of the instructions. The control unit communicates with the actuator through industrial Ethernet and uses the OPCUA protocol to achieve data interoperability and exchange. For control instructions with high real-time requirements, such as reactor damping control, the EtherCAT industrial real-time Ethernet protocol is used to achieve millisecond-level control response through synchronous cycle mode; for control instructions with high safety requirements, such as large-scale movement of the vibration isolation platform, functional safety protocols such as PROFIsafe are used to ensure reliable transmission and execution of control instructions through redundant design and error detection mechanism.

[0092] Among them, the actuator adjusts the corresponding control parameters or performs corresponding actions according to the received instructions, such as adjusting the current size of the reactor, changing the displacement of the hydraulic cylinder, etc., to realize the automatic height adjustment of the vibration isolation system. During the height adjustment operation, the control unit collects the feedback signals of various sensors in real time, such as vibration sensors, displacement sensors, etc., compares them with the expected values ​​in the optimized vibration isolation system height adjustment plan, and monitors the effect of the height adjustment operation. If there is a large deviation between the actual effect and the expected value, the control unit will automatically call the PID control algorithm or the adaptive control algorithm to dynamically adjust the control parameters of the actuator to ensure the accuracy and stability of the height adjustment operation. After the height adjustment operation is completed, the control unit will conduct a comprehensive detection of the state of the vibration isolation system, including vibration level, posture state, electrical parameters, etc., and compare the detection results with the state before the abnormality occurs to evaluate the effect of the height adjustment operation.

[0093] It should be noted that in order to determine whether the height adjustment operation is truly successful, the control unit will continuously collect vibration data and system status data for a period of time, such as 1 hour, after the height adjustment operation is completed, perform statistical analysis on the data, calculate the mean and standard deviation of key indicators such as vibration level and posture deviation, and compare them with the statistical values ​​under historical normal working conditions. If the mean and fluctuation range of each indicator are restored to normal levels and remain stable for multiple consecutive cycles, the height adjustment operation is considered successful, and a height adjustment report is generated and sent to the upper management system and relevant personnel. If there are still abnormal indicators, a secondary warning is triggered, and an optimization plan is regenerated for the next round of height adjustment.

[0094] At the same time, it is also necessary to combine the long-term operation data of the vibration isolation system to track and evaluate the long-term effectiveness of the height adjustment operation, respond to possible secondary abnormalities or performance degradation problems in a timely manner, and form a closed-loop health management and optimization mechanism. All height adjustment operation data, including abnormal information, optimization schemes, control instructions, execution feedback, effect evaluation, etc., will be recorded in the system log and database for subsequent statistical analysis and optimization improvement. The association rules between different working conditions, abnormal types, and optimization schemes are mined through the Apriori or FP-growth frequent item set mining algorithm to summarize the experience mode of height adjustment operation; the GSP or PrefixSpan timing pattern mining algorithm is used to find the best timing and process of height adjustment operation and optimize the timeliness of height adjustment operation. The association rules and timing patterns mined can be embedded in the expert system knowledge base to form a self-learning and self-optimizing intelligent system. In addition, the Q-learning or DQN reinforcement learning algorithm can be used to learn the optimal height adjustment strategy of the vibration isolation system under different abnormalities, and it has a certain generalization ability for unknown abnormalities, so as to realize the intelligent and adaptive optimization of height adjustment operation and continuously improve the efficiency and effect of height adjustment operation. When the long short-term memory network model predicts that the vibration abnormality risk of the vibration isolation system is higher than 0.8, the early warning mechanism is triggered immediately. The expert system uses the Drools rule engine to match the preset 100 exception handling rules according to the abnormal frequency range, such as 10-20Hz, amplitude range, such as 5-10mm, and other features. Combined with the operating parameters of the last hour, such as speed and temperature, an optimization adjustment plan containing 10 levels and 20 operations is generated. Using Ansys finite element simulation and Matlab / Simulink multi-domain modeling, a digital twin model of the vibration isolation system is built, and the optimization plan is converted into control instructions. 30 iterative simulations are performed on the digital twin model to evaluate the vibration reduction effect, energy efficiency, safety, etc. of the plan, and the parameters of the plan are optimized to improve the vibration reduction effect by more than 5%. The optimization plan is converted into JSON format and transmitted to the control unit through the OPCUA protocol. The control unit parses the plan, generates 50 EtherCAT control instructions, and sends them to the actuator. According to the instruction, the actuator adjusts the damping coefficient of reactor A from 1000N·s / m to 1200N·s / m, the displacement of hydraulic cylinder B from 5mm to 8mm, and the P value of PID controller from 2.0 to 2.5, realizing the automatic height adjustment of vibration isolation system. During the height adjustment process, the control unit collects data from 6 vibration sensors and 4 displacement sensors at a sampling frequency of 100Hz, extracts the spectral characteristics of vibration signals through short-time Fourier transform, estimates the mean and variance of displacement signals through Kalman filtering, compares the characteristic values ​​with the expected values ​​of the optimization scheme, and corrects the control parameters of the actuator when the deviation exceeds 10% to ensure the height adjustment accuracy.After the height adjustment is completed, the control unit continuously collects system status data for 2 hours, extracts 20 key indicators such as vibration mean, posture deviation, etc., calculates their mean and standard deviation, and performs a t-test with the statistical values ​​of historical normal working conditions, with a confidence level of 95%. When all indicators are within the normal range and stable for 3 consecutive cycles, the height adjustment operation is determined to be successful, and a height adjustment report in PDF format is generated. The 2GB height adjustment operation data is stored in the Hadoop distributed file system, and the Apriori algorithm is used for association rule mining, with a support of 20% and a confidence of 80%. The rule "When the vibration amplitude is greater than 5mm, the success rate of scheme A is 85%" is obtained. The PrefixSpan algorithm is used for time series pattern mining, with a Gap of 3 and a support of 20%, and the pattern "The efficiency of adjusting the damping parameters first and then adjusting the displacement parameters is 10% higher than the reverse order" is obtained. The 20 association rules and 10 time series patterns are embedded in the expert system knowledge base. At the same time, the DQN reinforcement learning algorithm was adopted, with the vibration level as the state, the height adjustment plan as the action, and the vibration attenuation as the reward. After 10,000 training times, it converged to an average reward value of 0.95, realizing the self-learning optimization of the height adjustment strategy of the vibration isolation system.

[0095] In addition, it should be noted that during the height adjustment operation, the vibration data of the vibration isolation system is monitored and obtained in real time, the actual vibration level is compared with the expected target, and the control instructions are adjusted according to the comparison results to return to the previous stable state.

[0096] Here, the control unit collects the vibration sensor data of each measuring point of the vibration isolation system in real time, and transmits the vibration sensor data to the edge computing unit; the edge computing unit uses a sliding window algorithm to process the received vibration data in real time to form a set of vibration feature vectors; the vibration feature vector is compared with a preset vibration level threshold, and when the feature value exceeds the alarm threshold, the early warning mechanism is triggered; when the early warning mechanism is triggered, an early warning message is generated and sent to the control unit, and after receiving the early warning message, the control unit queries the corresponding control strategy from the knowledge base, generates the corresponding control instruction, and sends it to the corresponding actuator; at the same time, the control unit monitors the vibration level change trend of the vibration isolation system to determine whether the vibration level shows an abnormal trend; if the vibration level continues to rise and the increase exceeds the preset threshold, the control unit sends a rollback instruction to restore the parameter setting of the actuator to the previous stable state.

[0097] For example, the control unit collects vibration sensor data from each measuring point of the vibration isolation system in real time through industrial Ethernet, including vibration acceleration, velocity, displacement and other signals. The sampling frequency is 1kHz, the data packet length is variable, and the maximum does not exceed 1024 bytes. It is transmitted to the edge computing unit through the OPCUA protocol. The edge computing unit uses a sliding window algorithm to process the received vibration data in real time. The window length is 1 second and the sliding step is 0.1 second. The vibration signal in each window is first denoised by wavelet transform. The db4 wavelet basis function is selected to decompose the signal into 5 layers. The threshold is adaptively determined according to the energy distribution of the wavelet coefficients, and the high-frequency components with lower energy are regarded as noise for removal. The denoised signal is then feature extracted to extract time domain features such as mean, peak, RMS, kurtosis, etc., and frequency domain features such as main frequency, band energy, power spectrum entropy, etc., to form a feature vector sequence. The extracted vibration feature vector is compared with the pre-set vibration level threshold. The threshold setting comprehensively considers factors such as equipment type, working condition, historical data, etc. Statistical methods such as normal distribution assumption are used, and μ+3σ of vibration data under historical normal working conditions is used as the alarm threshold, and μ+5σ is used as the fault threshold; or a class of SVM machine learning methods are used to train the optimal classification hyperplane based on historical normal data, and the distance from the hyperplane to the data point is used as the measure of the degree of abnormality. When comparing, the Mahalanobis distance is used to calculate the distance between the current feature vector and the threshold. The larger the distance, the closer or more it exceeds the threshold. When the Mahalanobis distance exceeds the alarm threshold, the early warning mechanism is triggered, and an early warning message is automatically generated, including the warning time, location, level, key vibration indicators, etc., and sent to the control unit through the message queue. After receiving the early warning message, the control unit retrieves the Top-N historical cases with the highest similarity to the current vibration abnormal features from the case-based reasoning (CBR) knowledge base. The cases store expert experience and decision rules such as abnormal features, cause analysis, and treatment measures, and are stored in XML or JSON format. The handling measures of the retrieved cases are analyzed and optimized to form a set of control instructions for the current abnormality, such as reducing the motor speed, increasing the damping coefficient, etc., and then sent to the corresponding actuator. At the same time, the execution effect of the instructions is fed back to the knowledge base, the original cases are evaluated and updated, and the knowledge of abnormality handling is continuously accumulated and optimized. The control unit also monitors the change trend of the vibration level of the vibration isolation system in real time, calculates the mean and standard deviation of the vibration characteristic quantity by online recursion, and sets the following rules: when the vibration level exceeds the alarm threshold for three consecutive sampling cycles and continues to rise, it is predicted that it may exceed the fault threshold in the next cycle; or when the vibration level exceeds the alarm threshold for five consecutive sampling cycles but does not reach the fault threshold; or when the cumulative increase in the vibration level exceeds 50% within 10 sampling cycles, the fallback operation is triggered.

[0098] When rolling back, according to the historical state data, the most recent stable state or the top-3 state with the lowest vibration level is selected as the target, and the fuzzy control or reinforcement learning method is used to adaptively adjust the rolling back step size to balance the vibration level recovery effect and production efficiency loss. The control unit sends a rollback command to restore the actuator parameters to the target state, continuously monitors the vibration level changes, and stops rolling back when it returns to the normal range and remains stable; if it still cannot be restored after rolling back to the initial state, the fault diagnosis process is triggered to analyze the cause of the fault and formulate targeted disposal measures. During the entire height adjustment operation, vibration data, control instructions, execution feedback and other information are stored in a time series database such as InfluxDB. The Grafana visualization tool is used to view the vibration level change curve, spectrum waterfall diagram, etc. in real time, and the system operation status is intuitively presented. At the same time, the isolation forest or One-ClassSVM anomaly detection algorithm is used to detect and alarm the real-time data to timely discover abnormal fluctuations in the reactor vibration isolation system. After the adjustment is completed, an adjustment report is generated to compare and analyze the vibration level before and after the adjustment, the execution of control instructions, the trend of key parameter changes, etc. The statistical hypothesis test methods such as t test and F test are used to analyze the significance of the adjustment effect and evaluate the effectiveness of the adjustment operation. The evaluation indicators include vibration level recovery rate, exceedance rate, production efficiency loss rate, fault diagnosis accuracy, etc. The report is automatically sent to relevant personnel for review in the form of a PDF document to continuously optimize the adjustment effect and equipment management level. The control unit collects data from 4 vibration sensors and 2 displacement sensors through industrial Ethernet. The sampling rate is 2kHz, and the maximum length of each data packet is 512 bytes. It is encapsulated in JSON format and sent to the edge computing unit through the MQTT protocol in a reliable transmission mode of QoS level 1. The data transmission adopts the MQTT (Message Queuing Telemetry Transport) protocol and is reliably transmitted in a QoS (Quality of Service) level 1 mode to ensure that the data is delivered to the edge computing unit, and the message is not lost even in the case of unstable network. The edge computing unit uses a sliding window algorithm with a length of 2 seconds and a step size of 0.2 seconds to segment the vibration signal. The signal in each window is decomposed by 5 layers of sym4 wavelet packets, and the optimal number of decomposition layers is automatically determined by comparing the variance of the reconstruction coefficients. Soft threshold filtering is implemented on the high-frequency coefficients of the first 3 layers, and the threshold is set to 3 times the standard deviation of the noise to achieve adaptive denoising and effectively suppress irrelevant noise interference.

[0099] Then, 18 time domain features, such as mean, variance, kurtosis, and 12 frequency domain features, such as spectrum peak and center frequency, are extracted from the denoised vibration signal to form a 30-dimensional feature vector for subsequent abnormality detection and diagnosis. The one-class SVM support vector machine algorithm is used to train the historical normal data, and the radial basis function kernel function is set with parameters nu=0.05 and gamma=0.1 to obtain the optimal classification hyperplane. The distance from the current feature vector to the hyperplane is calculated. If the distance exceeds 2.5 times the standard deviation σ, an alarm is triggered. If it exceeds 3.5σ, it is judged as a fault state.

[0100] Next, the five cases with the closest Euclidean distance to the current vibration feature vector are retrieved from the knowledge base in XML format, and control instructions are generated according to the treatment measures of the cases, such as reducing the motor frequency from 50Hz to 45Hz and increasing the damping coefficient from 500N·s / m to 800N·s / m. If the alarm threshold is exceeded for three consecutive window periods and the distance is increasing, and the rate of change is greater than 10%, or the alarm threshold is exceeded for five consecutive window periods, or the cumulative increase in 10 consecutive window periods exceeds 30%, the fallback mechanism is triggered. The fuzzy PID control algorithm is adopted. The input is the deviation between the current vibration level and the target level and its rate of change. The output is the backoff step length. The domain is [-5,5]. The fuzzy subsets {NB, NM, NS, ZO, PS, PM, PB} represent negative big (NegativeBig), negative medium (NegativeMedium), negative small (NegativeSmall), zero (Zero), positive small (PositiveSmall), positive medium (PositiveMedium), positive big (PositiveBig), the rule base contains 49 rules, the center of gravity is defuzzified, and the output backoff step length is between 0.8-1.2. According to the backoff step length, the actuator parameters are gradually restored to one of the three historical states with the smallest Mahalanobis distance of the vibration characteristic vector. After each step back, two window periods are monitored. If the vibration level returns to the normal range, the backoff is stopped, otherwise it continues to backoff until it is restored or reaches the initial state. If the initial state still cannot be restored, the fault tree analysis method is used to match 12 typical fault modes according to the vibration spectrum characteristics, generate a fault report and recommend maintenance measures. The data of the heightening process is written into the InfluxDB time series database at a frequency of one data point per minute. Grafana is used to draw the vibration level control chart, mark the alarm line and the upper and lower control limits, and the fallback process is represented by a segment chart for heightening process monitoring. The isolation forest algorithm is used for unsupervised anomaly detection, with 100 trees, a subsampling ratio of 0.2, and anomaly score threshold of 0.6. Abnormal data points are found and marked. After the heightening is completed, 8 heightening effect evaluation indicators are extracted, including the average and standard deviation change rate of the vibration level before and after the heightening, the number of alarms and failures, and the production efficiency loss rate. The paired t test and F test are used with a significance level of α=0.05 to generate a 40-page PDF format report, including the heightening process time series data chart, statistical test result table, key control parameter change curve, fault discovery and processing record, etc. After the report is reviewed and approved, it is converted into HTML format and stored in the knowledge base for subsequent heightening decision-making reference under similar working conditions.

[0101] Through the technical solution provided by the above embodiment of the present invention, by establishing a mathematical model and simulation analysis, the damper response data under different operating conditions is obtained, the vibration isolation system is monitored and dynamically adjusted to cope with changes in operating conditions and vibration anomalies, and the stable operation of the system is ensured; the adjustment and optimization problems of the vibration isolation system under dynamic conditions are solved, and the stability and reliability of the system are ensured. The adaptability and response speed of the vibration isolation system are improved, the risks and losses caused by excessive vibration are reduced, and the prediction and processing capabilities of vibration anomalies are improved.

[0102] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0103] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0104] According to an embodiment of the present invention, there is also provided a reactor vibration isolation system installation processing device for implementing the reactor vibration isolation system installation processing method. Figure 3 Schematic diagram of an installation and processing device of a reactor vibration isolation system according to an embodiment of the present invention. Figure 3 As shown, the installation processing device of the reactor vibration isolation system includes: a simulation analysis unit 301, a processing unit 303, a dynamic adjustment unit 305, a generation unit 307, a first determination unit 309, a second determination unit 311, a correction unit 313 and an installation unit 315. The installation processing device of the reactor vibration isolation system is described below.

[0105] The simulation analysis unit 301 is used to perform simulation analysis on the vibration isolation system model of the reactor vibration isolation system to obtain damping response data of the damper under different operating conditions of the reactor vibration isolation system.

[0106] The processing unit 303 is used to process the damping response data to obtain a damper performance influencing factor, wherein the damper performance influencing factor is an influencing factor whose influence on the performance of the damper is greater than an influence degree threshold.

[0107] The dynamic adjustment unit 305 is used to dynamically adjust the control parameters of the damper according to the analysis results of the factors affecting the damper performance.

[0108] The generating unit 307 is used to generate an installation strategy of the reactor vibration isolation system according to the damper performance influencing factors and control parameters.

[0109] The first determining unit 309 is used to determine the vibration state data of the reactor vibration isolation system during the process of linkage height adjustment according to the installation strategy.

[0110] The second determining unit 311 is used to determine abnormal vibration data of the reactor vibration isolation system according to the vibration state data.

[0111] The correction unit 313 is used to correct the installation strategy according to the abnormal vibration data to obtain a corrected installation strategy.

[0112] The installation unit 315 is used to install the reactor vibration isolation system according to the modified installation strategy.

[0113] It should be noted here that the above-mentioned simulation analysis unit 301, processing unit 303, dynamic adjustment unit 305, generation unit 307, first determination unit 309, second determination unit 311, correction unit 313 and installation unit 315 correspond to steps S202 to S216 in the above-mentioned embodiments, and the eight units are the same as the instances and application scenarios implemented by the corresponding steps, but are not limited to the contents disclosed in the above-mentioned embodiments.

[0114] As can be seen from the above, in the scheme recorded in the above-mentioned embodiment of the present invention, the simulation analysis unit can be used to simulate and analyze the vibration isolation system model of the reactor vibration isolation system to obtain the damping response data of the damper under different operating conditions of the reactor vibration isolation system; the processing unit is used to process the damping response data to obtain the damper performance influencing factor, wherein the damper performance influencing factor is an influencing factor whose degree of influence on the performance of the damper is greater than the influence degree threshold; the dynamic adjustment unit is used to dynamically adjust the control parameters of the damper according to the analysis results of the damper performance influencing factor; the generation unit is used to generate the control parameters of the reactor vibration isolation system according to the damper performance influencing factor and the control parameters. The invention discloses an installation strategy; a first determining unit is used to determine vibration state data of the reactor vibration isolation system during the process of linkage height adjustment according to the installation strategy; a second determining unit is used to determine abnormal vibration data of the reactor vibration isolation system according to the vibration state data; a correction unit is used to correct the installation strategy according to the abnormal vibration data to obtain a corrected installation strategy; the reactor vibration isolation system is installed according to the modified installation strategy by using the installation unit, so that the damper response data under different operating conditions is obtained by establishing a mathematical model and performing simulation analysis, and the vibration isolation system is monitored and dynamically adjusted to cope with changes in operating conditions and vibration anomalies, so as to ensure the stable operation of the system and improve the safety of the reactor.

[0115] Therefore, the above technical solution provided by the embodiment of the present invention solves the technical problem in the related art that during the installation and height adjustment process of the reactor vibration isolation system, the performance change of the damper cannot be effectively monitored, which poses a safety hazard.

[0116] Optionally, the simulation and analysis unit includes: a first generation module for generating a physical model of the reactor vibration isolation system; a second generation module for generating a vibration isolation system model based on the physical model, wherein the vibration isolation system model is used to describe the behavior of the reactor vibration isolation system in a dynamic environment; a first acquisition module for acquiring initial conditions of the working environment of the reactor vibration isolation system; and a first simulation and analysis module for performing simulation analysis on the vibration isolation system model under the initial conditions to obtain damping response data.

[0117] Optionally, the processing unit includes: a preprocessing module, which is used to preprocess the damping response data to obtain preprocessed damping response data; a first processing module, which is used to input the preprocessed damping response data into a damping response data identification model, so as to process the preprocessed damping response data using the damping response data identification model to obtain a processing result, wherein the damping response data identification model is a model obtained by machine learning training using multiple groups of training data, each of the multiple groups of training data includes: sample damping response data and sample processing results corresponding to the sample damping response data, and the processing results are the results of importance ranking and quantitative analysis of multiple performance influencing factors of the damper; a first determination module, which is used to determine, based on the processing results, the damper performance influencing factors whose degree of influence on the damper is greater than an influence degree threshold.

[0118] Optionally, the dynamic adjustment unit includes: a second acquisition module, used to acquire the vibration signal of the reactor vibration isolation system, wherein the vibration signal includes: vibration frequency, amplitude and damping ratio key parameters; an analysis module, used to analyze the vibration signal according to a preset working range of the damper to obtain an analysis result; and a dynamic adjustment module, used to dynamically adjust the control parameters of the damper according to the vibration signal and damper performance influencing factors when it is determined that the analysis result indicates that the current working state of the reactor vibration isolation system is not in the preset working range.

[0119] Optionally, the dynamic adjustment module includes: an adjustment submodule, which is used to adjust the damping coefficient and damping force control parameters of the damper through the actuator of the reactor vibration isolation system so that the vibration signal of the reactor vibration isolation system is maintained in a preset working range.

[0120] Optionally, the generation unit includes: a third generation module, which is used to generate a three-dimensional geometric model of the reactor vibration isolation system; a meshing module, which is used to mesh the three-dimensional geometric model by finite element analysis to obtain the meshing result of the three-dimensional geometric model; a third acquisition module, which is used to obtain a mesh model data file of the reactor vibration isolation system according to the meshing result; a second simulation analysis module, which is used to input the mesh model data file into the finite element analysis software, and after defining the material properties, boundary conditions and load conditions of the reactor vibration isolation system, use the finite element analysis software to simulate and analyze the reactor vibration isolation system based on the mesh model data file to obtain the natural frequency values ​​and vibration mode vectors of different orders of the reactor vibration isolation system; a fourth acquisition module, which is used to obtain the vibration characteristics and vibration isolation effect of the reactor vibration isolation system according to the natural frequency values ​​and vibration mode vectors of different orders; and a second determination module, which is used to determine the installation strategy according to the vibration characteristics and vibration isolation effect.

[0121] Optionally, the second determination module includes: a first acquisition submodule, used to obtain the optimal damping coefficient and damping force range of the damper; a second acquisition submodule, used to input the optimal damping coefficient and damping force range into a finite element analysis model, so as to use the finite element analysis model to obtain the vibration characteristics and vibration isolation effects of the reactor vibration isolation system under different damping parameters according to the optimal damping coefficient and damping force range; a first generation submodule, used to generate a mapping relationship between vibration isolation performance corresponding to different damping parameters and vibration characteristics and vibration isolation effects; an analysis submodule, used to analyze multiple initial installation strategies of the reactor vibration isolation system; The first submodule is used to analyze the vibration isolation performance differences of multiple initial installation strategies, and obtain the vibration isolation performance difference information of multiple initial installation strategies; the screening submodule is used to screen out the intermediate installation strategies according to the vibration isolation performance difference information; the search submodule is used to obtain the damping parameter combination with the highest matching degree with the current installation strategy in the intermediate installation strategies by searching the mapping relationship; the second generation submodule is used to generate a parameter optimization model of the reactor vibration isolation system according to the natural frequency, vibration transmissibility and displacement response index of the reactor vibration isolation system, and the installation position and damping parameters of the reactor are used as design variables; the third acquisition submodule is used to obtain the installation strategy according to the parameter optimization model.

[0122] Optionally, the second determination unit includes: a time domain analysis module, which is used to perform time domain analysis on the time domain data in the vibration state data using wavelet transform to obtain wavelet coefficients and wavelet energy distribution characteristics in the vibration state data, and use Fourier transform to extract spectral characteristics in the vibration state data; a fifth acquisition module, which is used to obtain a vibration feature vector corresponding to the vibration state data based on the wavelet coefficients, wavelet energy distribution characteristics and spectral characteristics; an abnormality identification module, which is used to input the vibration feature vector into a vibration state identification model to perform abnormality identification on the vibration state data using the vibration state identification model to obtain an abnormality identification result, wherein the vibration state identification model is a model obtained by machine learning training using multiple groups of training data, and each group of the multiple groups of training data includes: a sample vibration feature vector, and a sample identification result corresponding to the sample vibration feature vector; a sixth acquisition module, which is used to obtain abnormal vibration data when it is determined that there is abnormal vibration in the reactor vibration isolation system based on the abnormality identification result.

[0123] Optionally, the installation unit includes: a seventh acquisition module, used to acquire current vibration data of the reactor vibration isolation system; a second processing module, used to process the current vibration data using a sliding window method to obtain a vibration characteristic vector; a comparison module, used to compare the vibration characteristic vector with a preset vibration level threshold to obtain a comparison result; a third generation module, used to generate a control instruction based on a revised installation strategy when the comparison result indicates that the vibration characteristic vector exceeds the preset vibration level threshold; an installation module, used to send the control instruction to the actuator of the reactor vibration isolation system, so as to use the actuator to install the reactor vibration isolation system according to the control instruction.

[0124] According to another aspect of the embodiments of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium includes a stored program, wherein the program executes any one of the above-mentioned methods for installing and processing a reactor vibration isolation system.

[0125] Optionally, in this embodiment, the computer-readable storage medium may be located in any one of the computer terminals in a computer terminal group in a computer network, or in any one of the communication devices in a communication device group.

[0126] Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for executing the following steps: simulating and analyzing the vibration isolation system model of the reactor vibration isolation system to obtain damping response data of the damper under different operating conditions of the reactor vibration isolation system; processing the damping response data to obtain damper performance influencing factors, wherein the damper performance influencing factors are influencing factors whose degree of influence on the performance of the damper is greater than an influence degree threshold; dynamically adjusting the control parameters of the damper according to the analysis results of the damper performance influencing factors; generating an installation strategy for the reactor vibration isolation system according to the damper performance influencing factors and the control parameters; determining vibration state data of the reactor vibration isolation system during the linkage height adjustment process according to the installation strategy; determining abnormal vibration data of the reactor vibration isolation system according to the vibration state data; correcting the installation strategy according to the abnormal vibration data to obtain a corrected installation strategy; and installing the reactor vibration isolation system according to the modified installation strategy.

[0127] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: generating a physical model of the reactor vibration isolation system; generating a vibration isolation system model based on the physical model, wherein the vibration isolation system model is used to describe the behavior of the reactor vibration isolation system in a dynamic environment; obtaining initial conditions of the working environment of the reactor vibration isolation system; and under the initial conditions, performing simulation analysis on the vibration isolation system model to obtain damping response data.

[0128] Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for executing the following steps: preprocessing the damping response data to obtain preprocessed damping response data; inputting the preprocessed damping response data into a damping response data identification model to process the preprocessed damping response data using the damping response data identification model to obtain a processing result, wherein the damping response data identification model is a model obtained by machine learning training using multiple sets of training data, each of the multiple sets of training data includes: sample damping response data and sample processing results corresponding to the sample damping response data, the processing results are the results of importance ranking and quantitative analysis of multiple performance influencing factors of the damper; and determining, based on the processing results, damper performance influencing factors whose degree of influence on the damper is greater than an influence degree threshold.

[0129] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: obtaining a vibration signal of the reactor vibration isolation system, wherein the vibration signal includes: vibration frequency, amplitude, and key parameters of damping ratio; analyzing the vibration signal according to a preset working range of the damper to obtain an analysis result; when it is determined that the analysis result indicates that the current working state of the reactor vibration isolation system is not in the preset working range, dynamically adjusting the control parameters of the damper according to the vibration signal and factors affecting damper performance.

[0130] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: adjusting the damping coefficient and damping force control parameters of the damper through the actuator of the reactor vibration isolation system so that the vibration signal of the reactor vibration isolation system remains within a preset working range.

[0131] Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for executing the following steps: generating a three-dimensional geometric model of the reactor vibration isolation system; meshing the three-dimensional geometric model by finite element analysis to obtain a meshing result of the three-dimensional geometric model; obtaining a mesh model data file of the reactor vibration isolation system according to the meshing result; inputting the mesh model data file into the finite element analysis software, and after defining the material properties, boundary conditions and load conditions of the reactor vibration isolation system, using the finite element analysis software to perform simulation analysis on the reactor vibration isolation system based on the mesh model data file to obtain natural frequency values ​​and vibration mode vectors of different orders of the reactor vibration isolation system; obtaining the vibration characteristics and vibration isolation effect of the reactor vibration isolation system according to the natural frequency values ​​and vibration mode vectors of different orders; and determining the installation strategy according to the vibration characteristics and vibration isolation effect.

[0132] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: obtaining an optimal damping coefficient and damping force range of the damper; inputting the optimal damping coefficient and damping force range into a finite element analysis model, so as to use the finite element analysis model to obtain the vibration characteristics and vibration isolation effects of the reactor vibration isolation system under different damping parameters according to the optimal damping coefficient and damping force range; generating a mapping relationship between vibration isolation performance corresponding to different damping parameters and vibration characteristics and vibration isolation effects; analyzing multiple initial installation strategies of the reactor vibration isolation system to obtain vibration isolation performance difference information of multiple initial installation strategies; screening out intermediate installation strategies according to the vibration isolation performance difference information; obtaining the damping parameter combination with the highest matching degree with the current installation strategy in the intermediate installation strategy by searching the mapping relationship; generating a parameter optimization model of the reactor vibration isolation system according to the natural frequency, vibration transmissibility and displacement response index of the reactor vibration isolation system, and taking the installation position and damping parameters of the reactor as design variables; and obtaining the installation strategy according to the parameter optimization model.

[0133] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: performing time domain analysis on time domain data in the vibration state data using wavelet transform to obtain wavelet coefficients and wavelet energy distribution characteristics in the vibration state data, and extracting spectral characteristics in the vibration state data using Fourier transform; obtaining a vibration feature vector corresponding to the vibration state data based on the wavelet coefficients, wavelet energy distribution characteristics and spectral characteristics; inputting the vibration feature vector into a vibration state recognition model to perform abnormality recognition on the vibration state data using the vibration state recognition model to obtain an abnormality recognition result, wherein the vibration state recognition model is a model obtained by machine learning training using multiple sets of training data, each of the multiple sets of training data includes: a sample vibration feature vector, and a sample recognition result corresponding to the sample vibration feature vector; when it is determined according to the abnormal recognition result that abnormal vibration exists in the reactor vibration isolation system, abnormal vibration data is obtained.

[0134] Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for executing the following steps: obtaining current vibration data of the reactor vibration isolation system; processing the current vibration data using a sliding window method to obtain a vibration characteristic vector; comparing the vibration characteristic vector with a preset vibration level threshold to obtain a comparison result; when the comparison result indicates that the vibration characteristic vector exceeds the preset vibration level threshold, generating a control instruction based on a revised installation strategy; and sending the control instruction to an actuator of the reactor vibration isolation system, so as to use the actuator to install the reactor vibration isolation system according to the control instruction.

[0135] According to another aspect of the embodiments of the present invention, a processor is provided, and the processor is used to run a program, wherein when the program is run, any one of the above-mentioned installation processing methods for the reactor vibration isolation system is executed.

[0136] According to another aspect of the embodiments of the present invention, a computer program product is provided, including computer instructions, which, when executed by a processor, execute any one of the above-mentioned methods for installing and processing a reactor vibration isolation system.

[0137] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0138] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0139] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0140] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0141] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0142] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk, etc. Various media that can store program codes.

[0143] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for installing a reactor vibration isolation system, characterized in that: include: Performing simulation analysis on a vibration isolation system model of a reactor vibration isolation system to obtain damping response data of a damper under different operating conditions of the reactor vibration isolation system; Processing the damping response data to obtain a damper performance influencing factor, wherein the damper performance influencing factor is an influencing factor having an influence degree on the performance of the damper greater than an influence degree threshold; Dynamically adjusting the control parameters of the damper according to the analysis results of the factors affecting the performance of the damper; generating an installation strategy for the reactor vibration isolation system according to the damper performance influencing factors and the control parameters; Determining vibration state data of the reactor vibration isolation system during linkage height adjustment according to the installation strategy; determining abnormal vibration data of the reactor vibration isolation system according to the vibration state data; Modifying the installation strategy according to the abnormal vibration data to obtain a modified installation strategy; Install the reactor vibration isolation system according to the modified installation strategy; The method comprises: obtaining a vibration signal of the reactor vibration isolation system, wherein the vibration signal comprises: vibration frequency, amplitude and damping ratio key parameters; analyzing the vibration signal according to a preset working range of the damper to obtain an analysis result; and dynamically adjusting the control parameters of the damper according to the vibration signal and the damper performance influencing factors when it is determined that the analysis result indicates that the current working state of the reactor vibration isolation system is not within the preset working range. Among them, generating the installation strategy of the reactor vibration isolation system according to the damper performance influencing factors and the control parameters includes: generating a three-dimensional geometric model of the reactor vibration isolation system; meshing the three-dimensional geometric model by finite element analysis to obtain the meshing result of the three-dimensional geometric model; obtaining a mesh model data file of the reactor vibration isolation system according to the meshing result; inputting the mesh model data file into finite element analysis software, and after defining the material properties, boundary conditions and load conditions of the reactor vibration isolation system, using the finite element analysis software to perform simulation analysis on the reactor vibration isolation system based on the mesh model data file to obtain the natural frequency values ​​and vibration mode vectors of different orders of the reactor vibration isolation system; obtaining the vibration characteristics and vibration isolation effect of the reactor vibration isolation system according to the natural frequency values ​​and vibration mode vectors of different orders; and determining the installation strategy according to the vibration characteristics and the vibration isolation effect; Wherein, determining the abnormal vibration data of the reactor vibration isolation system according to the vibration state data includes: using wavelet transform to perform time domain analysis on the time domain data in the vibration state data to obtain wavelet coefficients and wavelet energy distribution characteristics in the vibration state data, and using Fourier transform to extract frequency spectrum characteristics in the vibration state data; obtaining a vibration feature vector corresponding to the vibration state data according to the wavelet coefficients, the wavelet energy distribution characteristics and the frequency spectrum characteristics; inputting the vibration feature vector into a vibration state recognition model to perform abnormal recognition on the vibration state data using the vibration state recognition model to obtain an abnormal recognition result, wherein the vibration state recognition model is a model obtained by machine learning training using multiple sets of training data, each of the multiple sets of training data includes: a sample vibration feature vector and a sample recognition result corresponding to the sample vibration feature vector; when it is determined according to the abnormal recognition result that the reactor vibration isolation system has abnormal vibration, the abnormal vibration data is obtained.

2. The installation method of the reactor vibration isolation system according to claim 1, characterized in that: The vibration isolation system model of the reactor vibration isolation system is simulated and analyzed to obtain the damping response data of the damper under different operating conditions of the reactor vibration isolation system, including: generating a physical model of the reactor vibration isolation system; Generating the vibration isolation system model according to the physical model, wherein the vibration isolation system model is used to describe the behavior of the reactor vibration isolation system in a dynamic environment; Acquiring initial conditions of the working environment of the reactor vibration isolation system; Under the initial conditions, simulation analysis is performed on the vibration isolation system model to obtain the damping response data.

3. The installation method of the reactor vibration isolation system according to claim 1, characterized in that: The damping response data is processed to obtain factors affecting damper performance, including: Preprocessing the damping response data to obtain the preprocessed damping response data; Inputting the preprocessed damping response data into a damping response data identification model, so as to process the preprocessed damping response data using the damping response data identification model to obtain a processing result, wherein the damping response data identification model is a model obtained by machine learning training using multiple sets of training data, each of the multiple sets of training data includes: sample damping response data and sample processing results corresponding to the sample damping response data, and the processing results are the results of importance ranking and quantitative analysis of multiple performance influencing factors of the damper; The damper performance influencing factor having an influencing degree on the damper greater than the influencing degree threshold is determined according to the processing result.

4. The installation method of the reactor vibration isolation system according to claim 1, characterized in that: Dynamically adjusting the control parameters of the damper according to the vibration signal and the damper performance influencing factors, including: The damping coefficient and damping force control parameters of the damper are adjusted by the actuator of the reactor vibration isolation system so that the vibration signal of the reactor vibration isolation system is maintained in the preset working range.

5. The installation method of the reactor vibration isolation system according to claim 1, characterized in that: Determining the installation strategy according to the vibration characteristics and the vibration isolation effect includes: Obtaining an optimal damping coefficient and a damping force range of the damper; Inputting the optimal damping coefficient and the damping force range into a finite element analysis model, so as to obtain the vibration characteristics and the vibration isolation effect of the reactor vibration isolation system under different damping parameters according to the optimal damping coefficient and the damping force range using the finite element analysis model; Generating a mapping relationship between the different damping parameters and the vibration characteristics and the vibration isolation performance corresponding to the vibration isolation effect; Analyzing a plurality of initial installation strategies of the reactor vibration isolation system to obtain vibration isolation performance difference information of the plurality of initial installation strategies; Screening out an intermediate installation strategy based on the vibration isolation performance difference information; Obtaining the damping parameter combination with the highest matching degree with the current installation strategy in the intermediate installation strategy by searching the mapping relationship; According to the natural frequency, vibration transmissibility and displacement response index of the reactor vibration isolation system, and taking the installation position and damping parameters of the reactor as design variables, a parameter optimization model of the reactor vibration isolation system is generated; The installation strategy is obtained according to the parameter optimization model.

6. The installation method of the reactor vibration isolation system according to claim 1, characterized in that: Installing the reactor vibration isolation system according to the modified installation strategy includes: Acquiring current vibration data of the reactor vibration isolation system; Processing the current vibration data using a sliding window method to obtain a vibration feature vector; Comparing the vibration characteristic vector with a preset vibration level threshold to obtain a comparison result; When the comparison result indicates that the vibration characteristic vector exceeds the preset vibration level threshold, generating a control instruction based on the modified installation strategy; The control instruction is sent to an actuator of the reactor vibration isolation system, so that the reactor vibration isolation system is installed by the actuator according to the control instruction.

7. An installation and processing device for a reactor vibration isolation system, characterized in that: include: A simulation analysis unit, used for performing simulation analysis on a vibration isolation system model of the reactor vibration isolation system to obtain damping response data of the damper under different operating conditions of the reactor vibration isolation system; a processing unit, configured to process the damping response data to obtain a damper performance influencing factor, wherein the damper performance influencing factor is an influencing factor having an influence degree on the performance of the damper greater than an influence degree threshold; A dynamic adjustment unit, used for dynamically adjusting the control parameters of the damper according to the analysis results of the factors affecting the performance of the damper; A generating unit, configured to generate an installation strategy for the reactor vibration isolation system according to the damper performance influencing factors and the control parameters; A first determining unit is used to determine vibration state data of the reactor vibration isolation system during a linkage height adjustment process according to the installation strategy; a second determining unit, configured to determine abnormal vibration data of the reactor vibration isolation system according to the vibration state data; A correction unit, configured to correct the installation strategy according to the abnormal vibration data to obtain a corrected installation strategy; An installation unit, used for installing the reactor vibration isolation system according to the modified installation strategy; Wherein, the dynamic adjustment unit includes: a second acquisition module, used to acquire the vibration signal of the reactor vibration isolation system, wherein the vibration signal includes: vibration frequency, amplitude and damping ratio key parameters; an analysis module, used to analyze the vibration signal according to the preset working range of the damper to obtain an analysis result; a dynamic adjustment module, used to dynamically adjust the control parameters of the damper according to the vibration signal and the damper performance influencing factors when it is determined that the analysis result indicates that the current working state of the reactor vibration isolation system is not in the preset working range; Wherein, the generation unit includes: a third generation module, which is used to generate a three-dimensional geometric model of the reactor vibration isolation system; a meshing module, which is used to mesh the three-dimensional geometric model by finite element analysis to obtain the meshing result of the three-dimensional geometric model; a third acquisition module, which is used to obtain the mesh model data file of the reactor vibration isolation system according to the meshing result; a second simulation analysis module, which is used to input the mesh model data file into the finite element analysis software, and after defining the material properties, boundary conditions and load conditions of the reactor vibration isolation system, use the finite element analysis software to perform simulation analysis on the reactor vibration isolation system based on the mesh model data file to obtain the natural frequency values ​​and vibration mode vectors of different orders of the reactor vibration isolation system; a fourth acquisition module, which is used to obtain the vibration characteristics and vibration isolation effect of the reactor vibration isolation system according to the natural frequency values ​​and vibration mode vectors of different orders; a second determination module, which is used to determine the installation strategy according to the vibration characteristics and the vibration isolation effect; Wherein, the second determination unit includes: a time domain analysis module, which is used to perform time domain analysis on the time domain data in the vibration state data by using wavelet transform to obtain the wavelet coefficients and wavelet energy distribution characteristics in the vibration state data, and extract the frequency spectrum characteristics in the vibration state data by using Fourier transform; a fifth acquisition module, which is used to obtain the vibration feature vector corresponding to the vibration state data according to the wavelet coefficients, the wavelet energy distribution characteristics and the frequency spectrum characteristics; an abnormality identification module, which is used to input the vibration feature vector into a vibration state identification model, so as to perform abnormal identification on the vibration state data by using the vibration state identification model, so as to obtain an abnormal identification result, wherein the vibration state identification model is a model obtained by machine learning training using multiple sets of training data, and each of the multiple sets of training data includes: a sample vibration feature vector and a sample identification result corresponding to the sample vibration feature vector; a sixth acquisition module, which is used to obtain the abnormal vibration data when it is determined according to the abnormal identification result that the reactor vibration isolation system has abnormal vibration.

Images