Anti-seismic power distribution cabinet system based on computer analog simulation technology

Abstract

The invention relates to the technical field of power distribution cabinets, in particular to an anti-seismic power distribution cabinet system based on a computer analog simulation technology. The system comprises a seismic wave analogue simulation module, a stress monitoring module, a data analysis module, an adjustment control module and a safety early warning module. The seismic wave simulation module generates seismic waveforms through a simulation technology and constructs a database, the stress monitoring module monitors the stress state of the power distribution cabinet in an earthquake in real time, the data analysis module evaluates the safety of the power distribution cabinet by using a Gaussian process algorithm, and the adjustment control module optimizes a supporting and damping structure based on a reinforcement learning algorithm. And the safety early warning module triggers multi-stage early warning when the stress exceeds a threshold value, prompts protection and records abnormal data. According to the invention, multiple functions of earthquake simulation, real-time monitoring, intelligent control, early warning and the like are integrated, anti-seismic protection of the power distribution cabinet is realized, the anti-seismic capability of the power distribution cabinet is improved, the automation level and the intelligent level of the system are increased, and the stability and the safety of the power distribution cabinet are effectively guaranteed.

Description

Technical Field

[0001] The invention relates to the technical field of power distribution cabinets, and in particular to an anti-seismic power distribution cabinet system based on computer simulation technology. Background Art

[0002] In today's power system, distribution cabinets are key devices for power distribution and are widely used in industrial, commercial and civil buildings. With the increase in the height and density of modern buildings, the threat of earthquake disasters to the distribution system is becoming increasingly serious. Earthquakes not only damage the structure of the distribution cabinet, but may also cause a series of problems such as power short circuits and power outages, affecting power supply safety and stable operation of equipment. Therefore, improving the seismic resistance of distribution cabinets has become an important research direction in the field of power equipment manufacturing. However, there are still the following problems: the existing technology cannot accurately simulate complex seismic waveforms, the seismic design is not comprehensive enough, and it is difficult to meet the needs of various earthquake intensities; the traditional system lacks real-time monitoring function, and it is difficult to obtain the stress state of the equipment in time during an earthquake, which limits the performance of seismic resistance; the existing system lacks effective analysis and prediction of the stress response of the equipment, and cannot accurately evaluate the safety under different earthquake intensities; the traditional system cannot automatically adjust the support structure and shock absorption device, and lacks the ability to flexibly respond to earthquakes. Summary of the invention

[0003] To solve the above problems, the present invention provides a seismic-resistant distribution cabinet system based on computer simulation technology, which solves the problem of how to improve the seismic resistance of the distribution cabinet under different earthquake intensities and ensure power supply safety through accurate earthquake waveform simulation, real-time stress monitoring and automatic adjustment of the supporting structure, thereby realizing seismic protection of the distribution cabinet and improving the seismic resistance and safety of the distribution cabinet.

[0004] To achieve the above object, the technical solution adopted by the present invention is: An anti-seismic power distribution cabinet system based on computer simulation technology, comprising a seismic wave simulation module, a stress monitoring module, a data analysis module, a regulation control module and a safety warning module which are sequentially connected in communication; The seismic wave simulation module is used to construct a seismic waveform database through computer simulation technology, generate seismic waveforms of different intensities and types, simulate the vibration conditions of an actual seismic environment, and generate seismic load data; The stress monitoring module is used to monitor the stress state of the power distribution cabinet under the action of the seismic waveform in real time based on the seismic load data, and collect stress data; The data analysis module is used to predict the stress response of the distribution cabinet under earthquake action based on the stress data through the Gaussian process variational inference algorithm, evaluate the safety of the distribution cabinet under different earthquake intensities, and generate a data analysis report for the distribution cabinet; The adjustment control module is used to automatically adjust the support structure and the shock absorbing device of the power distribution cabinet by optimizing the control strategy using a reinforcement learning algorithm based on the power distribution cabinet data analysis report; The safety warning module is used to perform multi-level evaluation on the monitored stress state using a fuzzy logic inference algorithm. When the stress value exceeds a set safety threshold, a warning is triggered to prompt the user to take protective measures and record abnormal state data.

[0005] Furthermore, the operation process of the seismic wave simulation module includes the following steps: Through the computer simulation platform, a multi-source earthquake database is integrated to collect earthquake waveform data under different magnitudes and source conditions, and the data is preprocessed to build a high-precision earthquake waveform database; Based on finite element modeling, the frequency, amplitude, period and other parameters of the seismic waveform are regulated to generate multiple types of seismic waveforms; Based on the multi-type earthquake waveforms, a virtual earthquake environment is constructed through computer simulation technology, and the propagation and attenuation process of the earthquake wave in space is constructed in real time through a visual simulation tool to simulate the stress conditions of the distribution cabinet in different earthquake environments; Adaptive optimization algorithms are used to automatically adjust waveform parameters according to different geological conditions, and seismic load data is generated through numerical calculations; The simulation results are calibrated through real-time feedback and the time-series dynamic seismic load data is output.

[0006] Furthermore, the earthquake load data includes earthquake acceleration time history, earthquake spectrum characteristics, source distance, propagation path, magnitude and earthquake duration.

[0007] Furthermore, the stress data includes stress distribution, strain, structural deformation and displacement data.

[0008] Furthermore, the operation process of the data analysis module includes the following steps: Preprocessing the stress data, removing noise using a multi-resolution wavelet transform method, and removing abnormal data points using a multi-parameter collaborative filtering algorithm; Based on the preprocessed stress data, the Gaussian process variational inference algorithm is used to establish the stress response model under seismic load conditions; The stress response model is used to predict the stress response of the distribution cabinet under different earthquake scenarios, and the stress distribution diagram of the distribution cabinet under different earthquake environments is output in real time to capture the stress peak and high-risk areas; Based on the stress distribution prediction results, the seismic performance of the distribution cabinet is evaluated at three levels: components, substructures, and overall structures. The deformation, strain concentration areas, and potential failure locations of each level of structure under vibration are analyzed to form a hierarchical safety assessment report. A visual data analysis report is generated based on the safety assessment results. The visual data analysis report includes a stress concentration trend diagram, a strain deformation analysis diagram, a seismic response characteristic curve, and a structural seismic resistance score of the distribution cabinet.

[0009] Furthermore, the formula of the stress response model is as follows: ; in, Indicates the stress change of the distribution cabinet structure under the action of earthquake load, and is used to monitor the stress state of the distribution cabinet under the action of earthquake waveform in real time; Indicates the degree of direct influence of earthquake acceleration on stress; represents the earthquake acceleration time history; It represents the frequency influence coefficient, which controls the effect of the frequency of seismic waves on stress; represents the earthquake frequency response term; Indicates a point on the distribution cabinet and stress concentration location The distance between represents the spatial attenuation coefficient; It indicates the effect of source parameters on stress response; M indicates earthquake magnitude; D indicates the distance from the source; Represents the earthquake source distance attenuation coefficient.

[0010] Furthermore, the operation process of the regulation control module includes the following steps: Obtaining the data analysis report of the power distribution cabinet and extracting key stress parameters; Based on the extracted key stress parameters, the reinforcement learning algorithm is used to build the seismic adjustment control model of the distribution cabinet. By dynamically adjusting the control strategy, multiple simulations and optimizations are performed on the supporting structure and shock absorption device. During the optimization process, the control parameters are adaptively adjusted through the anti-seismic effect of the reinforcement learning model in real time feedback; Finally, the optimal anti-seismic support and shock absorption parameter configuration is generated, and the support structure and shock absorption device of the power distribution cabinet are adjusted in real time; the shock absorption device adopts a multi-level damping design, automatically switches the damping mode according to different earthquake magnitudes, gives priority to reducing displacement at low earthquake magnitudes, and increases energy absorption effect at high earthquake magnitudes; The optimized seismic configuration parameters are stored in the system database, and an adjustment optimization result report is generated.

[0011] Furthermore, the formula of the seismic adjustment control model of the power distribution cabinet is as follows: ; Among them, U represents the seismic adjustment control parameter of the distribution cabinet, which is used for the dynamic adjustment of the supporting structure and the shock absorbing device; a, b and c represent the control coefficients; Indicates the change in stress; represents the time interval; A represents the earthquake acceleration; X represents the displacement value of the key structural position of the distribution cabinet; represents the acceleration at the key position; k represents the structural stiffness coefficient; represents the stress gradient; Indicates the additional stability factor of the distribution cabinet under the action of gravity.

[0012] Furthermore, the safety warning module specifically adopts multi-level fuzzy logic rules to set different levels of warning signals for different stress thresholds, including warning, warning, danger and extreme danger levels.

[0013] The beneficial effects of the present invention are: The present invention can generate earthquake waveforms of different intensities and types through the seismic wave simulation module, and accurately simulate the real earthquake load. This enables the system to adapt to a variety of seismic environments and improves the scientificity and reliability of the seismic design of the distribution cabinet. The stress monitoring module monitors the stress state of the distribution cabinet under vibration in real time based on the seismic load data, and can quickly and accurately collect stress data, which helps to timely grasp the state changes of the equipment in the earthquake and provide accurate data support for subsequent analysis. The data analysis module uses advanced algorithms to predict and evaluate stress data, and can generate detailed stress response data analysis reports, which is convenient for evaluating the safety of the distribution cabinet under earthquakes of different intensities. This predictive analysis capability helps to improve the risk management level of the system and identify potential dangers in advance. By adjusting the reinforcement learning algorithm of the control module, the system can intelligently optimize the control strategy according to the analysis results, dynamically adjust the support structure and shock absorption device of the distribution cabinet, thereby enhancing the seismic resistance. This intelligent adjustment mechanism not only improves the stability of the equipment in earthquakes, but also improves the autonomous adaptability of the system. The safety warning module uses fuzzy logic reasoning algorithm to perform multi-level evaluation of the stress state. When the stress exceeds the safety threshold, the system automatically triggers an early warning to prompt the user to take measures. This module also records abnormal states for post-analysis. This early warning function can protect equipment safety during earthquakes and effectively reduce losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a module schematic diagram of a seismic distribution cabinet system based on computer simulation technology of the present invention.

[0015] Figure 2 It is a flowchart of the operation process of the seismic wave simulation module provided by one embodiment of the present invention.

[0016] Figure 3It is a flowchart of the operation process of the data analysis module provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0017] See also Figure 1-3 As shown, the present invention relates to an earthquake-resistant power distribution cabinet system based on computer simulation technology.

[0018] Example An anti-seismic power distribution cabinet system based on computer simulation technology, comprising a seismic wave simulation module, a stress monitoring module, a data analysis module, a regulation control module and a safety warning module which are sequentially connected in communication; The seismic wave simulation module is used to construct a seismic waveform database through computer simulation technology, generate seismic waveforms of different intensities and types, simulate the vibration conditions of an actual seismic environment, and generate seismic load data; The operation process of the seismic wave simulation module includes the following steps: Through the computer simulation platform, a multi-source earthquake database is integrated to collect earthquake waveform data under different magnitudes and source conditions, and the data is preprocessed to build a high-precision earthquake waveform database; It should be noted that the seismic wave simulation module first integrates earthquake databases from multiple sources through a computer simulation platform, including global seismic observation stations and historical earthquake records, covering earthquake data under conditions of different magnitudes, focal depths, focal mechanisms, etc. The module collects a rich type of earthquake data, including waveforms of small to medium earthquakes and extremely large earthquakes, in order to provide reliable data under conditions of different magnitudes. In order to ensure the accuracy of the data, the module filters, denoises, and smoothes the collected data, and standardizes the data format, and finally constructs a high-precision seismic waveform database. The preprocessed data covers important information such as earthquake acceleration, velocity, and displacement at different magnitudes for subsequent waveform generation.

[0019] Based on finite element modeling, the frequency, amplitude, period and other parameters of the seismic waveform are regulated to generate multiple types of seismic waveforms; Specifically, after obtaining the seismic waveform database, the module uses the finite element modeling method to regulate the key parameters of the seismic waves (such as frequency, amplitude, period, etc.) to generate seismic waveforms that meet various simulation requirements. By changing the frequency range of the waveform, the frequency characteristics of seismic waves propagating in different strata are simulated, making the waveform suitable for simulation of different geological conditions. According to the amplitude characteristics of seismic waves under different magnitudes, the amplitude of the generated waveform is precisely controlled to ensure that the simulated waveform is consistent with the force conditions during the actual earthquake. The simulation module allows dynamic adjustment of the waveform period to generate seismic waves with different periods, which is suitable for simulating the vibration process of short-period and long-period earthquakes.

[0020] Based on the multi-type earthquake waveforms, a virtual earthquake environment is constructed through computer simulation technology, and the propagation and attenuation process of the earthquake wave in space is constructed in real time through a visual simulation tool to simulate the stress conditions of the distribution cabinet in different earthquake environments; Specifically, the module first establishes a three-dimensional physical model based on seismic wave propagation. The model mainly considers the following factors: Geological layer characteristics: The simulation model includes the material properties of different geological layers, such as density, elastic modulus, and wave velocity changes. These parameters have an important influence on the propagation rate and attenuation characteristics of seismic waves.

[0021] Source location and depth: By setting the depth, location and direction of the source, the propagation characteristics of seismic waves under different source conditions can be simulated. These factors have a significant impact on the distribution and attenuation of seismic wave energy. Deep sources usually cause larger-scale vibrations, while shallow sources may lead to stronger near-field effects.

[0022] Reflection and refraction effects: The model handles the reflection and refraction of seismic waves at the interface of different media. For example, when seismic waves propagate between layers with different densities and wave velocities, reflection and refraction occur, causing the waveform characteristics to change. These reflection and refraction effects are accurately simulated in the model to ensure that the waveform matches the actual propagation situation.

[0023] Based on the three-dimensional earthquake propagation model, the module uses visual simulation tools to dynamically display the propagation process of seismic waves in real time, forming an intuitive image of seismic wave propagation and attenuation. The propagation path of seismic waves in different medium layers will be clearly displayed in the visual interface, and users can observe the movement trajectory of seismic waves in three-dimensional space and the fluctuations in each area. The module visualizes the energy distribution and attenuation of seismic waves during propagation, allowing users to intuitively see the decrease in seismic wave energy. In particular, when seismic waves propagate to denser areas, the attenuation phenomenon is more obvious, and this attenuation information can provide a basis for seismic design. On the visual interface, users can monitor key parameters such as amplitude, frequency, acceleration, and velocity of seismic waves in real time. The dynamic changes of these parameters can reflect the actual effect of seismic wave propagation and provide real-time data support for subsequent stress monitoring modules.

[0024] The module can generate an earthquake environment that conforms to the actual situation according to different earthquake conditions.

[0025] Near-field and far-field earthquakes: By adjusting the source distance, the module can simulate near-field and far-field earthquake conditions. Near-field earthquake simulations will show more violent high-frequency vibrations, while far-field earthquakes will reflect the attenuation process of low-frequency vibrations.

[0026] Shallow and deep sources: By controlling the source depth, the module is able to generate seismic wave characteristics under shallow and deep source conditions. Shallow sources show greater vibration in the near field and the simulation effect is more impactful, while deep sources cause uniform vibrations over a wide area.

[0027] Multiple source effects: To simulate compound earthquakes, the module allows you to set multiple source locations to generate the superposition effect of compound seismic waves, which is used to evaluate the seismic performance of the distribution cabinet in a complex seismic environment.

[0028] During the propagation of seismic waves, energy will gradually decay as the distance increases. This decay is mainly manifested as a decrease in the amplitude of the seismic waves and a change in frequency. The module simulates the energy loss of seismic waves as the distance increases during the propagation process, and simulates the magnitude attenuation phenomenon of actual earthquakes by calculating the energy dissipation of seismic waves on each propagation path. Different geological media have different absorption effects on seismic waves. This module simulates the gradual absorption of seismic waves during propagation by setting the absorption coefficients of different media. Hard media usually absorb less seismic waves and reflect more, while soft media absorb more. These effects will be reflected in the simulation model.

[0029] In actual earthquakes, seismic waves tend to propagate along multiple paths and form complex waveform superpositions in different geological conditions and reflection surfaces. The module generates the superposition effect of seismic waves on multiple paths by calculating the reflection and interference effects of seismic waves in different media and boundaries. Users can observe the fluctuation superposition phenomenon of seismic waves in multi-path propagation to understand the possible amplification or weakening areas of seismic waves under complex geological conditions. The module also simulates the scattering effect of seismic waves on the interfaces of various media to show the complex changes in the waveform. This scattering simulation makes the simulation closer to the real earthquake environment and can present the fluctuation changes at different spatial positions, thereby helping to analyze the comprehensive stress state of the distribution cabinet under different seismic source conditions. Adaptive optimization algorithms are used to automatically adjust waveform parameters according to different geological conditions, and seismic load data is generated through numerical calculations; Specifically, through the adaptive optimization algorithm, the seismic waveform parameters are automatically adjusted according to different geological conditions and source parameters to generate load data that is closest to the actual earthquake. The module will automatically optimize the frequency, amplitude and period of the waveform according to the input geological parameters (such as formation hardness, source depth, etc.) to ensure that the waveform meets the characteristics of actual geological conditions. Based on the generated waveform, the seismic load data is generated through numerical calculation technology. The seismic load data includes the force data of the distribution cabinet under different earthquake scenarios, providing data support for the subsequent stress monitoring module.

[0030] By real-time feedback calibration simulation results, time-series dynamic earthquake load data are output; the earthquake load data includes earthquake acceleration time history, earthquake spectrum characteristics, source distance, propagation path, magnitude and earthquake duration.

[0031] The stress monitoring module is used to monitor the stress state of the distribution cabinet under the action of the seismic waveform in real time based on the seismic load data, and collect stress data; the stress data includes stress distribution, strain, structural deformation and displacement data.

[0032] It should be noted that multiple high-precision sensor arrays are installed in the distribution cabinet, including stress sensors, strain sensors and acceleration sensors. Stress sensors are used to measure the stress conditions at key locations, such as stress changes in support frames, shock absorber interfaces, door panels and bases; strain sensors are used to detect deformation of key parts of the distribution cabinet to help identify whether the cabinet has irreversible structural deformation. In addition, acceleration sensors can measure the acceleration of the distribution cabinet during an earthquake to assist in determining the displacement trend and vibration characteristics of the cabinet. The module uses highly sensitive thin film strain gauges and resistive stress sensors to ensure that small stress changes can be obtained under high-frequency vibrations of earthquakes, while maintaining strong anti-interference capabilities.

[0033] The module uses a time synchronization mechanism to synchronize the collected data of each sensor to ensure that all data correspond to the stress state under the seismic load at the same time. This step is particularly important because seismic loads change rapidly and unsynchronized data may cause errors in stress analysis. The collected data is preprocessed in real time using wavelet denoising algorithm and low-pass filtering algorithm to remove noise and high-frequency interference in the sensor signal to ensure data quality. Due to the large amount of data during earthquake action, the module will compress the data in real time and send it to the data analysis module through a high-speed data transmission interface. The compression algorithm uses the LZ77 lossless compression algorithm to reduce the occupancy of the transmission bandwidth while ensuring lossless transmission of data.

[0034] The data analysis module is used to predict the stress response of the distribution cabinet under earthquake action based on the stress data through the Gaussian process variational inference algorithm, evaluate the safety of the distribution cabinet under different earthquake intensities, and generate a data analysis report for the distribution cabinet; The operation process of the data analysis module includes the following steps: Preprocessing the stress data, removing noise using a multi-resolution wavelet transform method, and removing abnormal data points using a multi-parameter collaborative filtering algorithm; Based on the preprocessed stress data, the Gaussian process variational inference algorithm is used to establish the stress response model under seismic load conditions; Specifically, the module first initializes a Gaussian process model and uses the stress response data under earthquake load conditions as the input and output relationship of the model. The model input data is defined as a set of key stress response parameters, which may include magnitude, focal depth, earthquake frequency, loading location, and load time series. The setting of input parameters takes into account various possible earthquake scenarios to ensure the versatility of the model. The output data of the model is mainly the stress response of the distribution cabinet structure under earthquake action, such as stress distribution, stress peak position, deformation amplitude, etc. In this way, the model can output the corresponding stress distribution prediction results under different input earthquake scenarios.

[0035] In order to accurately predict the stress response of the distribution cabinet under different earthquake scenarios, the module uses a composite kernel function, including a combination of radial basis kernel (RBF Kernel) and linear kernel (Linear Kernel). The radial basis kernel is suitable for capturing the nonlinear relationship of stress data, while the linear kernel can maintain the linear trend of the input features, thereby ensuring that the model can simultaneously handle the nonlinear fluctuations and overall trends of seismic stress. To improve the prediction accuracy of the model, the module optimizes the hyperparameters of the kernel function, such as the scale parameter of the RBF kernel and the slope parameter of the linear kernel, through cross-validation. Hyperparameter optimization uses a grid search or Bayesian optimization method to obtain the best combination of kernel parameters, making the model more accurate in predictions under different earthquake scenarios.

[0036] After the input and output are defined and the kernel function is optimized, the module uses the preprocessed stress data set to train the Gaussian process model. During the training process, the Gaussian process estimates the posterior distribution of the stress response data by probabilistic inference. The model gradually learns the stress distribution characteristics of the distribution cabinet under seismic load conditions. Due to the high computational complexity of the Gaussian process, especially when processing large-scale data sets, the module introduces a variational inference optimization method. Variational inference significantly reduces the computational complexity by introducing an optimizable simplified distribution to approximate the original posterior distribution. Variational inference uses the method of maximizing the evidence lower bound (ELBO) to iteratively update the variational parameters of the Gaussian process, gradually narrowing the gap between the simplified distribution and the true posterior distribution. In order to speed up the optimization process of variational inference, the module uses parallel computing to input stress data in batches according to time series or different seismic conditions, and optimizes the model's stress response prediction under various seismic conditions.

[0037] The stress response model is used to predict the stress response of the distribution cabinet under different earthquake scenarios, and the stress distribution diagram of the distribution cabinet under different earthquake environments is output in real time to capture the stress peak and high-risk areas; Specifically, after establishing the stress response model, the data analysis module uses the model to predict the stress response of the distribution cabinet under different earthquake conditions and outputs the stress distribution in real time. Under a variety of set earthquake scenarios (such as different magnitudes, focal depths, epicenter distances, etc.), the module predicts the stress distribution of the distribution cabinet based on the Gaussian process model. The simulation results can reflect the stress changes of the distribution cabinet under different magnitudes and vibration frequencies. The module generates a stress distribution diagram of the distribution cabinet structure, showing stress peaks and high-risk areas at key locations. By monitoring the changing trend of stress in real time, the system can identify and mark possible structural weak areas for subsequent analysis.

[0038] Based on the stress distribution prediction results, the seismic performance of the distribution cabinet is evaluated at three levels: components, substructures, and overall structures. The deformation, strain concentration areas, and potential failure locations of each level of structure under vibration are analyzed to form a hierarchical safety assessment report. It should be noted that based on the stress distribution prediction results, the module conducts a hierarchical and detailed evaluation of the seismic performance of the distribution cabinet at the following three levels: Component-level assessment: Perform stress and deformation analysis on key components in the distribution cabinet (such as terminal blocks, distribution units, and electronic controllers) to identify possible high stress concentration areas. By predicting the stress response of each component, components that are susceptible to damage under earthquake action can be identified.

[0039] Substructure level assessment: Strain and deformation analysis is performed on each substructure of the distribution cabinet (such as cabinet support frame, side wall plate, base), and its seismic stability and stress distribution characteristics are evaluated. Special attention is paid to the connection points and key support parts of each substructure to determine which positions may experience strain concentration and deformation failure during vibration.

[0040] Overall structural level assessment: Combined with the stress distribution of components and substructures, the overall seismic performance of the distribution cabinet is evaluated. The module evaluates the overall stiffness and stability of the overall structure under strong earthquakes to determine whether there is a risk of overall instability. The overall stress of the structure is analyzed to clarify the seismic safety boundary under different earthquake magnitudes.

[0041] A visual data analysis report is generated based on the safety assessment results. The visual data analysis report includes a stress concentration trend diagram, a strain deformation analysis diagram, a seismic response characteristic curve, and a structural seismic resistance score of the distribution cabinet.

[0042] Furthermore, the formula of the stress response model is as follows: ; in, Indicates the stress change of the distribution cabinet structure under the action of earthquake load, and is used to monitor the stress state of the distribution cabinet under the action of earthquake waveform in real time; Indicates the degree of direct influence of earthquake acceleration on stress; represents the earthquake acceleration time history; It represents the frequency influence coefficient, which controls the effect of the frequency of seismic waves on stress; It represents the earthquake frequency response term. The frequency affects the stress response mode of different parts of the switchgear. In particular, the stress concentration phenomenon is more obvious near the resonant frequency of the structure. Indicates a point on the distribution cabinet and stress concentration location The distance between Represents the spatial attenuation coefficient, controls the attenuation rate of stress response with distance, affects the attenuation effect of shock waves at different locations, and makes stress weaken with increasing distance; It indicates the effect of source parameters on stress response; M indicates earthquake magnitude; D indicates the distance from the source; It represents the attenuation coefficient of the earthquake source distance, which controls the attenuation rate of the magnitude effect with the earthquake source distance D; It represents the base of natural logarithms, a constant in mathematics, an infinite non-repeating decimal, and a transcendental number, with a value of approximately 2.718281828459045.

[0043] The adjustment control module is used to automatically adjust the support structure and the shock absorbing device of the power distribution cabinet by optimizing the control strategy using a reinforcement learning algorithm based on the power distribution cabinet data analysis report; The operation process of the regulation control module includes the following steps: Obtaining the data analysis report of the power distribution cabinet and extracting key stress parameters; Based on the extracted key stress parameters, the reinforcement learning algorithm is used to build the seismic adjustment control model of the distribution cabinet. By dynamically adjusting the control strategy, multiple simulations and optimizations are performed on the supporting structure and shock absorption device. Specifically, the module uses the deep Q learning algorithm in reinforcement learning to optimize the seismic control strategy. The algorithm has strong adaptability when dealing with continuous decision-making control and can continuously optimize the adjustment parameters through self-learning. The module initializes the reinforcement learning model, including the definition of state space, action space and reward function. The state space contains the current stress distribution, displacement, deformation of the support structure and the current damping mode of the shock absorber of the distribution cabinet. The action space defines the optional adjustment actions of the control system, such as the adjustment amplitude of the support structure, the damping switching of the shock absorber, and the change of the stiffness of the support point. The design of the reward function is based on the feedback of the seismic effect. For example, in the process of simulating earthquakes, the stability of the support structure, the shock absorption effect, the minimization of displacement and the energy absorption efficiency will affect the reward value. The better the seismic effect, the higher the reward to encourage the system to choose the optimal adjustment strategy.

[0044] The module simulates the earthquake environment at high frequency and uses the reinforcement learning algorithm to repeatedly adjust the parameters of the support structure and shock-absorbing device. During each simulation, the system evaluates the control strategy under different earthquake magnitude conditions and optimizes the adjustment parameters based on the reward function feedback. Under low-magnitude conditions, the module focuses on adjusting the displacement control of the support structure, and the reinforcement learning algorithm gives priority to the control strategy of reducing displacement to ensure that the distribution cabinet remains stable under low vibration conditions. Under high-magnitude conditions, the system pays more attention to the energy absorption effect of the shock-absorbing device and adjusts the damping mode to maximize energy absorption and reduce impact transmission. After multiple simulations, the reinforcement learning model can gradually converge to the optimal seismic support and shock absorption strategy.

[0045] During the optimization process, the control parameters are adaptively adjusted through the anti-seismic effect of the reinforcement learning model in real time feedback; Finally, the optimal anti-seismic support and shock absorption parameter configuration is generated, and the support structure and shock absorption device of the power distribution cabinet are adjusted in real time; the shock absorption device adopts a multi-level damping design, automatically switches the damping mode according to different earthquake magnitudes, gives priority to reducing displacement at low earthquake magnitudes, and increases energy absorption effect at high earthquake magnitudes; It should be noted that after multiple rounds of reinforcement learning optimization, the module generated the optimal support structure and shock absorber configuration, which includes the stiffness setting of the support structure, the multi-level damping configuration of the shock absorber, and the adjustment strategy.

[0046] Multi-level damping design: The shock absorber adopts a multi-level damping design, which automatically switches the damping mode under different earthquake conditions.

[0047] Low-magnitude mode: When a low-magnitude earthquake is detected, the system automatically switches to low-damping mode, giving priority to reducing the displacement of the distribution cabinet and adjusting the supporting structure to the minimum deformation state to reduce the transmission of low-frequency vibrations.

[0048] Medium-high magnitude mode: In the case of medium to high magnitude earthquakes, the system switches to medium-high damping mode, maximizing energy dissipation by increasing the energy absorption effect of the shock absorber to protect the distribution cabinet from impact damage. In this mode, the damper and the support structure work together to achieve the best earthquake resistance.

[0049] The optimized seismic configuration parameters are stored in the system database, and an adjustment optimization result report is generated.

[0050] Furthermore, the formula of the seismic adjustment control model of the power distribution cabinet is as follows: ; Among them, U represents the seismic adjustment control parameter of the distribution cabinet, which is used for the dynamic adjustment of the supporting structure and the shock absorbing device; a, b and c represent the control coefficients, which are used to optimize the seismic response under different earthquake conditions; Indicates the change in stress, reflecting the stress fluctuation of the distribution cabinet structure under the action of earthquake; It represents the time interval, which is used to calculate the rate of change of stress over time; A represents the earthquake acceleration, which reflects the dynamic influence of earthquake fluctuations on the distribution cabinet; X represents the displacement value of the key structural position of the distribution cabinet; It represents the acceleration at the key position, reflecting the rapid response under the earthquake load; k represents the structural stiffness coefficient; represents the stress gradient; It represents the additional stability factor of the distribution cabinet under the action of gravity, m is the mass of the distribution cabinet, g is the acceleration of gravity, and h is the height of the center of gravity.

[0051] The safety warning module is used to use fuzzy logic reasoning algorithm to perform multi-level evaluation on the monitored stress state. When the stress value exceeds the set safety threshold, an early warning is triggered to prompt the user to take protective measures and record abnormal status data. The safety warning module specifically adopts multi-level fuzzy logic rules to set different levels of early warning signals for different stress thresholds, including early warning, warning, danger and extreme danger levels.

[0052] In one embodiment, the safety warning module uses a fuzzy logic reasoning algorithm to analyze the collected stress data in the manner of "input fuzzification - rule reasoning - output defuzzification". First, the collected stress data (including stress value, strain rate, deformation amplitude, etc.) is fuzzified, and the continuous stress data is converted into fuzzy language variables, such as "low stress", "medium stress", "high stress", etc. Then, the preset fuzzy rules are used for reasoning calculation, such as "if the stress value is high and the deformation rate is medium, it is judged to be a dangerous state". Finally, the reasoning result is defuzzified to generate a warning level.

[0053] The module has designed multiple stress level thresholds to ensure that different levels of early warning signals can be adaptively triggered in earthquakes of different intensities. Specifically, it includes the following four early warning levels: Warning level: When the system detects a slight stress beyond the normal range, the warning state is triggered. At this time, the system will remind the user through the interface display and low-frequency sound and light signals, but no additional measures are required.

[0054] Warning level: When the stress rises further and reaches the medium safety threshold of the distribution cabinet, the warning state is triggered, an obvious sound and light alarm signal is issued, and the current state data is automatically stored through the data recording system. The user is advised to check the system status.

[0055] Danger level: When the stress exceeds the high safety threshold, the system enters a dangerous state. The module will issue a high-frequency sound and light alarm, and warn the user to take protective measures immediately through SMS, APP notifications, etc. At the same time, the stress change trend and stress data of each key point are recorded for subsequent analysis.

[0056] Extreme danger level: This level is the highest warning level. When the stress reaches the extreme danger threshold, the warning module will automatically trigger the emergency shutdown program and send a fault signal to the monitoring system in real time. At this time, the system will execute self-protection measures (such as power-off protection, starting anti-seismic support, etc.) to ensure the safety of equipment and personnel.

[0057] When the safety warning module triggers the warning, it automatically records the stress, deformation and displacement data at each moment to form a detailed event log. Each abnormal situation (including warning, warning, danger and extreme danger) will be recorded, including the triggering time, specific stress value, strain state of the equipment and other information, and a detailed historical record table will be generated. These data can not only be used for post-analysis, but also provide data support for subsequent system optimization and maintenance.

[0058] The module transmits warning information to users in a variety of ways, including local device interface display, sound and light alarm, and remote notification (such as SMS or mobile application notification). Users can view the current stress status, warning level, and recommended measures on the interface. In addition, the system also has a user feedback interface, allowing users to feedback the actual situation after the warning is triggered. These feedback data will serve as the basis for system learning to optimize future warning strategies.

[0059] The safety warning module works closely with the adjustment control module to dynamically adjust the support structure and shock absorption device of the power distribution cabinet through real-time stress data and warning level judgment. When the system enters the warning state or above, the adjustment control module will automatically make appropriate protection adjustments to reduce the impact of stress, thereby realizing system adaptive adjustment and improving the overall safety of the power distribution cabinet under earthquake action.

[0060] In summary, the present invention realizes the generation of multi-type, high-precision seismic waveforms through the integration and preprocessing of multi-source seismic databases, ensures that seismic simulation can cover various seismic scenarios, and provides reliable data support for seismic design. The stress monitoring module collects stress, strain and acceleration data under seismic action in real time through a high-precision sensor array, and ensures the timeliness of the data through a synchronization mechanism, accurately reflects the stress state under seismic load, and provides high-quality data for subsequent stress analysis. The data analysis module uses Gaussian process models and multi-parameter collaborative filtering algorithms to achieve accurate prediction of stress response under seismic action. The nonlinear relationship of stress data is captured by a composite kernel function, which improves the prediction accuracy of the model, and can generate a multi-level seismic performance analysis report to effectively identify high-risk areas and weak structures of distribution cabinets.

[0061] The regulation control module of the present invention automatically adjusts the support structure and the shock absorbing device through the reinforcement learning algorithm, and optimizes the seismic configuration parameters in real time according to the vibration characteristics under different magnitude conditions, ensuring that the distribution cabinet maintains the best seismic resistance under different magnitude conditions. The safety warning module sets a multi-level warning level (warning, warning, danger, extreme danger) based on the fuzzy logic reasoning algorithm, and transmits information to the user through a variety of alarm methods. The module can effectively record key data before and after the earthquake, support the adaptive adjustment and subsequent optimization of the system, and improve the overall safety.

[0062] The above implementation modes are merely descriptions of the preferred implementation modes of the present invention, and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary engineering and technical personnel in the field shall fall within the protection scope determined by the claims of the present invention.

Claims

1. A seismic distribution cabinet system based on computer simulation technology, characterized in that: It includes a seismic wave simulation module, a stress monitoring module, a data analysis module, a regulation control module and a safety warning module which are sequentially connected in communication; The seismic wave simulation module is used to construct a seismic waveform database through computer simulation technology, generate seismic waveforms of different intensities and types, simulate the vibration conditions of an actual seismic environment, and generate seismic load data; The stress monitoring module is used to monitor the stress state of the power distribution cabinet under the action of the seismic waveform in real time based on the seismic load data, and collect stress data; The data analysis module is used to predict the stress response of the distribution cabinet under earthquake action based on the stress data through the Gaussian process variational inference algorithm, evaluate the safety of the distribution cabinet under different earthquake intensities, and generate a data analysis report for the distribution cabinet; The adjustment control module is used to automatically adjust the support structure and the shock absorbing device of the power distribution cabinet by optimizing the control strategy using a reinforcement learning algorithm based on the power distribution cabinet data analysis report; The safety warning module is used to perform multi-level evaluation on the monitored stress state using a fuzzy logic inference algorithm. When the stress value exceeds a set safety threshold, a warning is triggered to prompt the user to take protective measures and record abnormal state data.

2. The earthquake-resistant power distribution cabinet system based on computer simulation technology according to claim 1 is characterized in that: The operation process of the seismic wave simulation module includes the following steps: Through the computer simulation platform, a multi-source earthquake database is integrated to collect earthquake waveform data under different magnitudes and source conditions, and the data is preprocessed to build a high-precision earthquake waveform database; Based on finite element modeling, the frequency, amplitude and period parameters of the seismic waveform are regulated to generate multiple types of seismic waveforms; Based on the multi-type earthquake waveforms, a virtual earthquake environment is constructed through computer simulation technology, and the propagation and attenuation process of the earthquake wave in space is constructed in real time through a visual simulation tool to simulate the stress conditions of the distribution cabinet in different earthquake environments; Adaptive optimization algorithms are used to automatically adjust waveform parameters according to different geological conditions, and seismic load data is generated through numerical calculations; The simulation results are calibrated through real-time feedback and the time-series dynamic seismic load data is output.

3. The earthquake-resistant power distribution cabinet system based on computer simulation technology according to claim 2 is characterized in that: The earthquake load data include earthquake acceleration time history, earthquake spectrum characteristics, earthquake source distance, propagation path, earthquake magnitude and earthquake duration.

4. The earthquake-resistant power distribution cabinet system based on computer simulation technology according to claim 1 is characterized in that: The stress data include stress distribution, strain, structural deformation and displacement data.

5. The earthquake-resistant power distribution cabinet system based on computer simulation technology according to claim 1 is characterized in that: The operation process of the data analysis module includes the following steps: Preprocessing the stress data, removing noise using a multi-resolution wavelet transform method, and removing abnormal data points using a multi-parameter collaborative filtering algorithm; Based on the preprocessed stress data, the Gaussian process variational inference algorithm is used to establish the stress response model under seismic load conditions; The stress response model is used to predict the stress response of the distribution cabinet under different earthquake scenarios, and the stress distribution diagram of the distribution cabinet under different earthquake environments is output in real time to capture the stress peak and high-risk areas; Based on the stress distribution prediction results, the seismic performance of the distribution cabinet is evaluated at three levels: components, substructures, and overall structures. The deformation, strain concentration areas, and potential failure locations of each level of structure under vibration are analyzed to form a hierarchical safety assessment report. A visual data analysis report is generated based on the safety assessment results. The visual data analysis report includes a stress concentration trend diagram, a strain deformation analysis diagram, a seismic response characteristic curve, and a structural seismic resistance score of the distribution cabinet.

6. The earthquake-resistant power distribution cabinet system based on computer simulation technology according to claim 5 is characterized in that: The formula of the stress response model is as follows: ； in, Indicates the stress change of the distribution cabinet structure under the action of earthquake load, and is used to monitor the stress state of the distribution cabinet under the action of earthquake waveform in real time; Indicates the degree of direct influence of earthquake acceleration on stress; represents the earthquake acceleration time history; It represents the frequency influence coefficient, which controls the effect of the frequency of seismic waves on stress; represents the earthquake frequency response term; Indicates a point on the power distribution cabinet and stress concentration location The distance between represents the spatial attenuation coefficient; It indicates the effect of source parameters on stress response; M indicates earthquake magnitude; D indicates the distance from the source; represents the attenuation coefficient of the earthquake source distance; Represents the base of natural logarithms.

7. The earthquake-resistant power distribution cabinet system based on computer simulation technology according to claim 1 is characterized in that: The operation process of the regulation control module includes the following steps: Obtaining the data analysis report of the power distribution cabinet and extracting key stress parameters; Based on the extracted key stress parameters, the reinforcement learning algorithm is used to build the seismic adjustment control model of the distribution cabinet. By dynamically adjusting the control strategy, multiple simulations and optimizations are performed on the supporting structure and shock absorption device. During the optimization process, the control parameters are adaptively adjusted through the anti-seismic effect of the reinforcement learning model in real time feedback; Finally, the optimal anti-seismic support and shock absorption parameter configuration is generated, and the support structure and shock absorption device of the power distribution cabinet are adjusted in real time; the shock absorption device adopts a multi-level damping design, automatically switches the damping mode according to different earthquake magnitudes, gives priority to reducing displacement at low earthquake magnitudes, and increases energy absorption effect at high earthquake magnitudes; The optimized seismic configuration parameters are stored in the system database, and an adjustment optimization result report is generated.

8. The earthquake-resistant power distribution cabinet system based on computer simulation technology according to claim 7 is characterized in that: The formula of the seismic adjustment control model of the power distribution cabinet is as follows: ； Among them, U represents the seismic adjustment control parameter of the power distribution cabinet, which is used for the dynamic adjustment of the supporting structure and the shock absorbing device; a, b and c represent the control coefficients; Indicates the change in stress; represents the time interval; A represents the earthquake acceleration; X represents the displacement value of the key structural position of the distribution cabinet; represents the acceleration at the key position; k represents the structural stiffness coefficient; represents the stress gradient; Indicates the additional stability factor of the distribution cabinet under the action of gravity.

9. The earthquake-resistant power distribution cabinet system based on computer simulation technology according to claim 1 is characterized in that: The safety early warning module specifically adopts multi-level fuzzy logic rules to set early warning signals of different levels according to different stress thresholds, including early warning, warning, danger and extreme danger levels.

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