Spring energy storage detection method, system, device and medium for GIS circuit breaker

Abstract

The present application relates to the field of information detection technology, and discloses a method, system, device and medium for detecting the spring energy storage of a GIS circuit breaker. Based on the grating reflection wavelengths obtained at multiple measuring points on the outer surface of the GIS circuit breaker, the stress concentration coefficient and the material structure change index corresponding to the internal spring of the GIS circuit breaker are determined, wherein the material structure change index characterizes the grain size change rate; based on the stress concentration coefficient and the material structure change index, the remaining life of the spring is predicted to determine the predicted remaining life of the spring; in the case where the predicted remaining life is lower than the preset remaining life threshold, the real-time energy storage density of the spring energy storage system in the GIS circuit breaker is determined; the predicted energy storage demand corresponding to the real-time energy storage density is determined, and the mechanical-hydraulic energy storage ratio of the spring energy storage system is dynamically adjusted according to the predicted energy storage demand. The beneficial effect is to timely detect spring anomalies and extend its service life.

Description

Technical Field

[0001] The present application relates to the field of information detection technology, and in particular, to a method, system, device and medium for detecting the spring energy storage of a GIS circuit breaker. Background Art

[0002] The spring energy storage system of a GIS circuit breaker faces potential technical problems during long-term operation. Frequent charge-discharge cycles and continuous mechanical stress can cause the spring material to gradually fail. This process is concealed and gradual, and it is difficult to detect in time by conventional detection. Such failure may cause the energy storage function to fail.

[0003] Therefore, there is an urgent need to develop a real-time and non-destructive detection technology that can dynamically adjust the energy storage ratio, so as to detect spring anomalies in time and extend its service life. Summary of the Invention

[0004] The present application provides a method, system, device and medium for detecting the spring energy storage of a GIS circuit breaker, which solves the technical problem of difficult to detect the gradual failure of the spring material in time, and achieves the technical effect of detecting spring anomalies in time and extending its service life.

[0005] To achieve the above object, the main technical solutions adopted by the present application include:

[0006] In a first aspect, an embodiment of the present application provides a method for detecting the spring energy storage of a GIS circuit breaker, the method comprising:

[0007] Based on the grating reflection wavelengths obtained from multiple measurement points on the outer surface of the GIS circuit breaker, determine the stress concentration coefficient and the material structure change index corresponding to the spring inside the GIS circuit breaker, wherein the material structure change index characterizes the grain size change rate;

[0008] Based on the stress concentration coefficient and the material structure change index, predict the remaining life of the spring, and determine the predicted remaining life of the spring;

[0009] In the case where the predicted remaining life is lower than a preset remaining life threshold, determine the real-time energy storage density of the spring energy storage system in the GIS circuit breaker;

[0010] Determine the predicted energy storage demand corresponding to the real-time energy storage density, and dynamically adjust the mechanical-hydraulic energy storage ratio of the spring energy storage system according to the predicted energy storage demand.

[0011] A method for detecting the spring energy storage of a GIS circuit breaker provided in this embodiment obtains the stress concentration coefficient and the material structure change index of the internal spring of the GIS circuit breaker by monitoring the change in the grating reflection wavelength. These parameters will be used to predict the remaining life of the spring, where the stress concentration coefficient reflects the location where cracks may occur, and the material structure change index affects its fatigue resistance and strength. Considering these factors comprehensively can improve the accuracy of the remaining life prediction. When the predicted remaining life is lower than the preset threshold, it is necessary to evaluate the state of the spring energy storage system by determining the real-time energy storage density. The real-time energy storage density is defined as the ratio of the total stored energy in the spring energy storage system to the total volume of the hydraulic cylinder. According to the prediction of the real-time energy storage density, the mechanical-hydraulic energy storage ratio is further adjusted to optimize the energy storage distribution ratio between the spring and the hydraulic cylinder, ensuring that the spring energy storage system is in the best operating state.

[0012] Optionally, determining the stress concentration coefficient and the material structure change index corresponding to the internal spring of the GIS circuit breaker based on the grating reflection wavelengths obtained from multiple measurement points on the outer surface of the GIS circuit breaker includes:

[0013] According to the grating reflection wavelength, use the Bragg wavelength drift principle to calculate the strain value and the maximum strain value of each of the measurement points on the outer surface of the GIS circuit breaker;

[0014] Use a spatial interpolation algorithm to process the strain values to generate a continuous strain distribution field;

[0015] Perform gradient calculation on each spatial point in the strain distribution field to obtain the gradient value and the maximum gradient value of each spatial point;

[0016] Determine the stress concentration area as the spatial points corresponding to the gradient values greater than the preset gradient threshold;

[0017] According to the maximum strain value, the maximum gradient value, and the area of the stress concentration area, determine the stress concentration coefficient and the material structure change index through a preset strain distribution-stress model.

[0018] In this embodiment, the strain value of each measurement point is first calculated using the principle of Bragg wavelength drift. Subsequently, a spatial interpolation algorithm is employed to process these strain values to generate a continuous strain distribution field, thereby effectively inferring the strain distribution on the outer surface of the entire GIS circuit breaker and providing accurate spatial data. Next, gradient calculations are performed on the strain distribution field to obtain the gradient values at each spatial point, which reflect the rate of strain change and help determine the stress concentration regions in the structure. By setting a gradient threshold, the stress concentration regions in the strain distribution field are identified and marked. These regions may be critical stress concentration sites in the structure and require special attention and analysis. Finally, a mapping relationship between the strain distribution pattern and the internal spring stress state is established using a preset strain distribution-stress model, and then the stress concentration coefficient and the material structure change index are predicted, which helps to monitor and analyze the performance of the spring in the GIS circuit breaker in real time.

[0019] Optionally, predicting the remaining life of the spring based on the stress concentration coefficient and the material structure change index to determine the predicted remaining life of the spring includes:

[0020] Obtaining an image of the spring and extracting the microscopic damage features of the image, where the microscopic damage features characterize the damage and defects of the spring;

[0021] Classifying the microscopic damage features to determine different damage levels;

[0022] Determining the stress accumulation factor and fatigue index corresponding to the spring;

[0023] Inputting the stress concentration coefficient, the material structure change index, the damage level, the stress accumulation factor, and the fatigue index into a preset random forest tree algorithm model to obtain the predicted remaining life of the spring.

[0024] In this embodiment, by obtaining an image of the spring and then performing image preprocessing and Otsu threshold segmentation to extract microscopic damage features, these features can accurately characterize the damage and defect conditions of the spring. Then, the extracted microscopic damage features are classified to determine different damage levels (low, medium, high). Then, the stress accumulation factor and fatigue index of the spring in the past cycles are predicted to obtain the predicted stress accumulation factor and fatigue index. Finally, the stress concentration coefficient, the material structure change index, the damage level, the stress accumulation factor, and the fatigue index are used as inputs, and the random forest algorithm model is used to predict the remaining life of the spring.

[0025] Optionally, in the case where the predicted remaining life is lower than a preset remaining life threshold, determining the real-time energy storage density of the spring energy storage system in the GIS circuit breaker includes:

[0026] When the predicted remaining life is lower than the preset remaining life threshold, an energy conversion model between the spring and the hydraulic cylinder in the spring energy storage system is constructed;

[0027] Calculate the energy conversion data during the energy storage process according to the energy conversion model;

[0028] Determine the real-time energy storage density according to the energy conversion data and the total volume of the hydraulic cylinder.

[0029] In this embodiment, when the predicted remaining life is lower than the preset remaining life threshold, by adjusting the energy storage ratio, the dependence on the spring is reduced, thereby reducing the stress of the spring and prolonging its service life. By constructing an energy conversion model between the spring and the hydraulic cylinder, the real-time energy storage density can be obtained according to the real-time energy conversion data, so as to adjust the energy storage ratio in combination with the real-time energy storage density to ensure that the spring energy storage system operates within the optimal performance range and optimize the overall performance of the spring energy storage system.

[0030] Optionally, the construction of the energy conversion model between the spring and the hydraulic cylinder in the spring energy storage system includes:

[0031] Obtain the spring compression displacement, the force exerted on the spring, the hydraulic cylinder pressure, and the total volume of the hydraulic cylinder;

[0032] Establish a force-displacement relationship function of the spring according to the force and the spring compression displacement;

[0033] Establish a pressure-volume relationship function of the hydraulic cylinder according to the hydraulic cylinder pressure and the total volume;

[0034] Integrate the force-displacement relationship function and the pressure-volume relationship function to obtain the energy conversion model.

[0035] In this embodiment, through the data obtained by the sensor and combined with the mechanical characteristics, a force-displacement relationship function and a pressure-volume relationship function are constructed, which can accurately calculate the mechanical energy storage and hydraulic energy storage. Through the integrated mechanical energy storage and hydraulic energy storage after adjustment, a comprehensive energy conversion model can be obtained, which can better predict and optimize the energy efficiency and performance of the entire spring energy storage system.

[0036] Optionally, the determination of the predicted energy storage demand corresponding to the real-time energy storage density and the dynamic adjustment of the mechanical-hydraulic energy storage ratio of the spring energy storage system according to the predicted energy storage demand include:

[0037] According to the real-time energy storage density, use a preset long short-term memory network to predict the energy storage demand in the future time period, and obtain the predicted energy storage demand corresponding to each time point in the future time period;

[0038] Calculate the energy storage change from the current state to the next state for the predicted energy storage demand corresponding to any time point;

[0039] With the goal of maximizing the energy storage efficiency, dynamically program all the energy storage changes to determine the optimal path;

[0040] Determine the optimal mechanical - hydraulic energy storage ratio for each time point according to the optimal path;

[0041] Dynamically adjust the mechanical - hydraulic energy storage ratio of the spring energy storage system according to the optimal mechanical - hydraulic energy storage ratio.

[0042] In this embodiment, by using the long - short - term memory network, the energy storage demand at each time point in the future time period can be predicted, ensuring the accuracy of the prediction results. Then, the optimal path is determined through dynamic programming to optimize the energy storage change process and achieve the maximization of energy storage efficiency. Then, according to the determined optimal path, the mechanical - hydraulic energy storage ratio at each time point is determined to achieve the best energy storage configuration at different time points, ensuring that the performance of the energy storage system reaches the optimum at each time point. By dynamically adjusting the mechanical - hydraulic energy storage ratio, the system can respond to the changes in energy storage demand in real time, making the system always maintain high efficiency and stability during actual operation.

[0043] Optionally, the method further includes:

[0044] Obtain the reflected wave signal of the spring in the GIS circuit breaker;

[0045] Conduct waveform analysis on the reflected wave signal and extract the waveform feature vector;

[0046] Calculate the Euclidean distance between the waveform feature vector and the standard waveform feature vector in the preset standard feature library;

[0047] When the Euclidean distance is greater than the preset distance threshold, determine that the spring has micro - damage.

[0048] In this embodiment, by obtaining the reflected wave signal, conducting waveform analysis and feature vector extraction on it, an accurate description of the internal structure and state of the spring is achieved. This method helps to identify the working state and potential problems of the spring. Further, by comparing the waveform feature vector with the data in the standard feature library and calculating the Euclidean distance, it is possible to accurately judge whether the spring has micro - damage, so as to quickly locate the abnormality and effectively evaluate the health state of the spring.

[0049] Optionally, the waveform feature vector includes amplitude, frequency and phase; the method further includes:

[0050] Determine the amplitude attenuation rate according to the ratio of the amplitude to the initial amplitude;

[0051] Determine a frequency offset according to the difference between the frequency and the initial frequency;

[0052] Determine a phase difference according to the difference between the phase and the initial phase;

[0053] Based on the amplitude decay rate, the frequency offset, and the phase difference, determine a total health score corresponding to the spring.

[0054] In this embodiment, by measuring and calculating the amplitude decay rate, frequency offset, and phase difference of the reflected wave signal, the health state of the spring can be accurately evaluated. The comprehensive scores of various parameters classify the spring health state into different levels (severely abnormal, moderately abnormal, slightly abnormal, or normal). It can provide a clear health condition to help decision-makers take corresponding measures according to the actual health condition.

[0055] Optionally, the method further includes:

[0056] Obtain an original strain signal for the spring in the GIS circuit breaker;

[0057] Perform time-frequency analysis on the original strain signal to extract time-domain features and frequency-domain features;

[0058] Input the time-domain features and the frequency-domain features into a preset strain signal feature - elastic modulus model to obtain an estimated value of the elastic modulus;

[0059] Determine the degree of reduction of the elastic modulus corresponding to the estimated value of the elastic modulus;

[0060] In the case where the degree of reduction of the elastic modulus exceeds a preset reduction threshold, determine that the spring is abnormal.

[0061] In this embodiment, by extracting time-domain features and frequency-domain features from the obtained original strain signal, the changes and features of the signal can be effectively characterized. These features provide important information for subsequent estimation of the elastic modulus, which helps to accurately identify the state of the spring. By mapping the time-domain and frequency-domain features to the estimated value of the elastic modulus through the strain signal feature - elastic modulus model, the estimated value of the elastic modulus can be accurately obtained. Calculating the degree of reduction of the elastic modulus can quantify the performance change of the spring and provide a clear criterion to evaluate whether the spring is abnormal. By setting a threshold to determine whether the spring is abnormal, automatic monitoring and alarm can be achieved. If the degree of reduction of the elastic modulus exceeds the preset threshold, an alarm will be triggered. This mechanism can timely detect the performance degradation of the spring, thereby preventing potential failures or equipment damage and ensuring the reliability and safety of the equipment.

[0062] In a second aspect, an embodiment of the present application provides a spring energy storage detection system for a GIS circuit breaker, and the system includes:

[0063] A concentration coefficient and change index determination module, configured to determine a stress concentration coefficient corresponding to a spring inside the GIS circuit breaker and a material structure change index based on grating reflection wavelengths obtained from multiple measuring points on the outer surface of the GIS circuit breaker, wherein the material structure change index characterizes the grain size change rate;

[0064] A predicted remaining life module, configured to predict the remaining life of the spring based on the stress concentration coefficient and the material structure change index, and determine the predicted remaining life of the spring;

[0065] A stored energy density determination module, configured to determine the real-time stored energy density of a spring energy storage system in the GIS circuit breaker when the predicted remaining life is lower than a preset remaining life threshold;

[0066] A dynamic adjustment module, configured to determine a predicted energy storage demand corresponding to the real-time stored energy density, and dynamically adjust the mechanical-hydraulic energy storage ratio of the spring energy storage system according to the predicted energy storage demand.

[0067] A spring energy storage detection system for a GIS circuit breaker provided in this embodiment obtains a stress concentration coefficient and a material structure change index of a spring inside the GIS circuit breaker by monitoring changes in grating reflection wavelengths. These parameters will be used to predict the remaining life of the spring, where the stress concentration coefficient reflects the locations where cracks may occur, and the material structure change index affects its fatigue resistance and strength. Considering these factors comprehensively can improve the accuracy of remaining life prediction. When the predicted remaining life is lower than a preset threshold, it is necessary to evaluate the state of the spring energy storage system by determining the real-time stored energy density. The real-time stored energy density is defined as the ratio of the total stored energy in the spring energy storage system to the total volume of the hydraulic cylinder. According to the prediction of the real-time stored energy density, the mechanical-hydraulic energy storage ratio is further adjusted to optimize the energy storage distribution ratio between the spring and the hydraulic cylinder, ensuring that the spring energy storage system is in an optimal operating state.

[0068] Optionally, the concentration coefficient and change index determination module includes:

[0069] According to the grating reflection wavelength, use the Bragg wavelength drift principle to calculate the strain value and the maximum strain value of each of the measuring points on the outer surface of the GIS circuit breaker;

[0070] Use a spatial interpolation algorithm to process the strain values to generate a continuous strain distribution field;

[0071] Perform gradient calculation on each spatial point in the strain distribution field to obtain the gradient value and the maximum gradient value of each spatial point;

[0072] Determine the spatial points corresponding to the gradient values greater than a preset gradient threshold as stress concentration regions;

[0073] According to the maximum strain value, the maximum gradient value and the area of ​​the stress concentration region, the stress concentration coefficient and the material structure change index are determined through a preset strain distribution-stress model.

[0074] This embodiment first uses the Bragg wavelength drift principle to calculate the strain value of each measuring point. Subsequently, a spatial interpolation algorithm is used to process these strain values ​​to generate a continuous strain distribution field, thereby effectively inferring the strain distribution of the entire GIS circuit breaker outer surface and providing accurate spatial data. Next, the strain distribution field is gradient calculated to obtain the gradient value of each spatial point. These values ​​reflect the rate of strain change and help determine the stress concentration area in the structure. By setting the gradient threshold, the stress concentration areas in the strain distribution field are identified and marked. These areas may be key stress concentration areas in the structure and require special attention and analysis. Finally, a mapping relationship between the strain distribution pattern and the internal spring stress state is established using a preset strain distribution-stress model, and then the stress concentration coefficient and material structure change index are predicted, which helps to monitor and analyze the performance of the spring in the GIS circuit breaker in real time.

[0075] Optionally, the remaining life prediction module includes:

[0076] Acquire an image of the spring, and extract microscopic damage features of the image, wherein the microscopic damage features characterize damage and defects of the spring;

[0077] Classifying the microscopic damage characteristics to determine different damage levels;

[0078] Determining a stress accumulation factor and a fatigue index corresponding to the spring;

[0079] The stress concentration factor, the material structure change index, the damage level, the stress accumulation factor and the fatigue index are input into a preset random forest tree algorithm model to obtain the predicted remaining spring life.

[0080] This embodiment obtains an image of the spring, and then performs image preprocessing and Otsu threshold segmentation to extract microscopic damage features, which can accurately characterize the damage and defects of the spring. The extracted microscopic damage features are then classified to determine different damage levels (low, medium, and high). The stress accumulation factor and fatigue index of the spring in the past cycle are then predicted to obtain the predicted stress accumulation factor and fatigue index. Finally, the stress concentration factor, material structure change index, damage level, stress accumulation factor, and fatigue index are used as input to predict the remaining life of the spring using the random forest algorithm model.

[0081] Optionally, the module for determining energy storage density includes:

[0082] When the predicted remaining life is lower than a preset remaining life threshold, an energy conversion model between the spring and the hydraulic cylinder in the spring energy storage system is constructed;

[0083] Calculate the energy conversion data during the energy storage process according to the energy conversion model;

[0084] Determine the real-time energy storage density according to the energy conversion data and the total volume of the hydraulic cylinder.

[0085] In this embodiment, when the predicted remaining life is lower than the preset remaining life threshold, by adjusting the energy storage ratio, the dependence on the spring is reduced, thereby reducing the stress of the spring and prolonging its service life. By constructing an energy conversion model between the spring and the hydraulic cylinder, the real-time energy storage density can be obtained according to the real-time energy conversion data, so as to adjust the energy storage ratio in combination with the real-time energy storage density subsequently to ensure that the spring energy storage system operates within the optimal performance range and optimize the overall performance of the spring energy storage system.

[0086] Optionally, constructing the energy conversion model between the spring and the hydraulic cylinder in the spring energy storage system includes:

[0087] Obtain the spring compression displacement, the force exerted on the spring, the hydraulic cylinder pressure, and the total volume of the hydraulic cylinder;

[0088] Establish a force-displacement relationship function of the spring according to the force and the spring compression displacement;

[0089] Establish a pressure-volume relationship function of the hydraulic cylinder according to the hydraulic cylinder pressure and the total volume;

[0090] Integrate the force-displacement relationship function and the pressure-volume relationship function to obtain the energy conversion model.

[0091] In this embodiment, by using the data obtained by the sensor and combining the mechanical characteristics, a force-displacement relationship function and a pressure-volume relationship function are constructed, which can accurately calculate the mechanical energy storage and the hydraulic energy storage. Through the comprehensively adjusted mechanical energy storage and hydraulic energy storage, a comprehensive energy conversion model can be obtained, which can better predict and optimize the energy efficiency and performance of the entire spring energy storage system.

[0092] Optionally, the dynamic adjustment module includes:

[0093] According to the real-time energy storage density, use a preset long short-term memory network to predict the energy storage demand in the future time period, and obtain the predicted energy storage demand corresponding to each time point in the future time period;

[0094] For the predicted energy storage demand corresponding to any time point, calculate the energy storage change from the current state to the next state;

[0095] Taking the maximization of energy storage efficiency as the goal, dynamically programming all the energy storage changes to determine the optimal path;

[0096] According to the optimal path, determine the optimal mechanical - hydraulic energy storage ratio at each time point;

[0097] Dynamically adjust the mechanical - hydraulic energy storage ratio of the spring energy storage system according to the optimal mechanical - hydraulic energy storage ratio.

[0098] In this embodiment, by using the long - short - term memory network, it is possible to predict the energy storage demand at each time point within a future time period, ensuring the accuracy of the prediction results. Then, the optimal path is determined through dynamic programming, thereby optimizing the energy storage change process and achieving the maximization of energy storage efficiency. Then, according to the determined optimal path, the mechanical - hydraulic energy storage ratio at each time point is determined, so as to achieve the best energy storage configuration at different time points and ensure that the performance of the energy storage system reaches the optimal at each time point. By dynamically adjusting the mechanical - hydraulic energy storage ratio, it can respond to the changes in energy storage demand in real - time, making the system always maintain high efficiency and stability during actual operation.

[0099] Optionally, the system further includes:

[0100] Obtain the reflected wave signal for the spring in the GIS circuit breaker;

[0101] Perform waveform analysis on the reflected wave signal and extract the waveform feature vector;

[0102] Calculate the Euclidean distance between the waveform feature vector and the standard waveform feature vector in the preset standard feature library;

[0103] In the case where the Euclidean distance is greater than the preset distance threshold, determine that there is a microscopic damage to the spring.

[0104] In this embodiment, by obtaining the reflected wave signal, performing waveform analysis and feature vector extraction on it, an accurate description of the internal structure and state of the spring is achieved. This method helps to identify the working state and potential problems of the spring. Further, by comparing the waveform feature vector with the data in the standard feature library and calculating the Euclidean distance, it is possible to accurately judge whether there is a microscopic damage to the spring, thereby quickly locating the abnormality and effectively evaluating the health state of the spring.

[0105] Optionally, the waveform feature vector includes amplitude, frequency and phase; the system further includes:

[0106] Determine the amplitude attenuation rate according to the ratio of the amplitude to the initial amplitude;

[0107] Determine the frequency offset according to the difference between the frequency and the initial frequency;

[0108] Determine the phase difference according to the difference between the phase and the initial phase;

[0109] Based on the amplitude attenuation rate, the frequency offset, and the phase difference, determine the total health score corresponding to the spring.

[0110] In this embodiment, by measuring and calculating the amplitude attenuation rate, the frequency offset, and the phase difference of the reflected wave signal, the health state of the spring can be accurately evaluated. The comprehensive scores of various parameters classify the spring health state into different levels (severely abnormal, moderately abnormal, slightly abnormal, or normal). It can provide a clear health status to help decision-makers take corresponding measures according to the actual health status.

[0111] Optionally, the system further includes:

[0112] Obtain the original strain signal for the spring in the GIS circuit breaker;

[0113] Perform time-frequency analysis on the original strain signal to extract time-domain features and frequency-domain features;

[0114] Input the time-domain features and the frequency-domain features into a preset strain signal feature - elastic modulus model to obtain an estimated value of the elastic modulus;

[0115] Determine the degree of elastic modulus reduction corresponding to the estimated value of the elastic modulus;

[0116] In the case where the degree of elastic modulus reduction exceeds a preset reduction threshold, determine that the spring is abnormal.

[0117] In this embodiment, by extracting time-domain features and frequency-domain features from the obtained original strain signal, the changes and features of the signal can be effectively characterized. These features provide important information for subsequent estimation of the elastic modulus, which helps to accurately identify the state of the spring. By mapping the time-domain and frequency-domain features to the estimated value of the elastic modulus through the strain signal feature - elastic modulus model, the estimated value of the elastic modulus can be accurately obtained. Calculating the degree of elastic modulus reduction can quantify the performance change of the spring, providing a clear standard to evaluate whether the spring is abnormal. By setting a threshold to determine whether the spring is abnormal, automatic monitoring and alarm can be achieved. If the degree of elastic modulus reduction exceeds the preset threshold, an alarm will be triggered. This mechanism can timely detect the performance degradation of the spring, thereby preventing potential failures or equipment damage and ensuring the reliability and safety of the equipment.

[0118] In a third aspect, an embodiment of the present application provides a computer device, including:

[0119] A memory and a processor, which are communicatively connected to each other. Computer instructions are stored in the memory, and the processor executes the computer instructions to perform the spring energy storage detection method of the GIS circuit breaker described above.

[0120] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to cause a computer to execute the spring energy storage detection method of the GIS circuit breaker described above. Description of the Drawings

[0121] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0122] Figure 1 It is a flowchart of a spring energy storage detection method for a GIS circuit breaker provided by an embodiment of the present application;

[0123] Figure 2 It is a flowchart of step S1 provided by an embodiment of the present application;

[0124] Figure 3 It is a flowchart of step S3 provided by an embodiment of the present application;

[0125] Figure 4 It is a flowchart of step S5 provided by an embodiment of the present application;

[0126] Figure 5 It is a flowchart of step S51 provided by an embodiment of the present application;

[0127] Figure 6 It is a flowchart of step S7 provided by an embodiment of the present application;

[0128] Figure 7 It is a flowchart for determining that there is microscopic damage to the spring provided by an embodiment of the present application;

[0129] Figure 8 It is a flowchart for determining the total health state score corresponding to the spring provided by an embodiment of the present application;

[0130] Figure 9 It is a flowchart for determining that the spring is abnormal provided by an embodiment of the present application;

[0131] Figure 10 It is a flowchart for determining the total cumulative damage amount provided by an embodiment of the present application;

[0132] Figure 11 It is a flowchart of step S407 provided by an embodiment of the present application;

[0133] Figure 12 It is a block diagram of a spring energy storage detection system for a GIS circuit breaker provided by an embodiment of the present application;

[0134] Figure 13 It is a schematic structural diagram of a computer device provided by an embodiment of the present application. Specific embodiments

[0135] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0136] According to an embodiment of the present application, an embodiment of a method for detecting spring energy storage of a GIS circuit breaker 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 the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0137] In this embodiment, a method for detecting spring energy storage of a GIS circuit breaker is provided. Figure 1 It is a flowchart of a method for detecting spring energy storage of a GIS circuit breaker provided by an embodiment of the present application. As Figure 1 shown, the process includes the following steps:

[0138] Step S1, based on the grating reflection wavelengths obtained from multiple measurement points on the outer surface of the GIS circuit breaker, determine the stress concentration coefficient and the material structure change index corresponding to the internal spring of the GIS circuit breaker, where the material structure change index characterizes the grain size change rate.

[0139] The stress concentration coefficient reflects the degree of stress concentration caused by defects (such as holes, scratches, etc.) or shape inhomogeneities existing in the material. In contrast, the material structure change index describes the change rate of the grain size, which is usually caused by the deformation of the material. For non-intrusive monitoring of GIS circuit breakers, the grating reflection wavelength can be used for analysis to obtain information on these two data.

[0140] Specifically, a fiber Bragg grating sensor can be made of a single-mode fiber, with a grating length of 10 mm, a reflectivity of 80%, and a central wavelength of 1550 nm. One fiber Bragg grating sensor is arranged every 30 degrees along the circumference of the GIS circuit breaker housing. One is arranged every 50 mm along the axial direction to monitor the deformation at different positions, forming a complete grid-like structure covering 360 degrees. A total of 48 measuring points are arranged. The grating reflection wavelengths are measured at multiple measuring points, the sampling frequency is set to 100 Hz, and the wavelength resolution is 1 pm to capture the dynamic changes caused by the operation of the GIS circuit breaker. Then, the collected grating reflection wavelengths are analyzed to determine the stress concentration coefficient and the material structure change index corresponding to the spring inside the GIS circuit breaker. The intensity of the stress concentration area and the trend and degree of the change in the grain size inside the material can be inferred through the shift of the grating reflection wavelength or the change in the spectrum, so as to evaluate the force condition and stress distribution of the spring in the GIS circuit breaker. Machine learning algorithms (such as regression analysis, support vector machines, deep learning models, etc.) can also be used to establish a prediction model, and the stress concentration coefficient and the material structure change index corresponding to the spring are predicted based on the spectral feature information of the grating reflection wavelength. During model training, existing labeled data sets can be used for supervised learning to optimize the model parameters to improve the prediction accuracy.

[0141] Step S3, predict the remaining life of the spring based on the stress concentration coefficient and the material structure change index, and determine the predicted remaining life of the spring.

[0142] Specifically, since stress concentration may occur in the spring during use, that is, the stress on some parts is much higher than that on other parts. These stress concentration points are often the places where fatigue cracks first appear in the spring. A higher stress concentration coefficient means that these points are more likely to crack and be damaged, thus shortening the service life of the spring. As time and operating conditions change, the material structure of the spring may change, and these changes will cause the material structure change index of the spring to change, thereby affecting its fatigue resistance and strength. Therefore, it is necessary to comprehensively consider the stress concentration coefficient and the material structure change index, and their mutual influence jointly determines the remaining life of the spring under given working conditions.

[0143] Based on this, a remaining life prediction model can be established using machine learning or statistical methods according to the stress concentration factor and material structure change index, such as regression analysis, survival analysis (such as the COX model), deep learning model, random forest tree algorithm model, etc. The historical data set is used for model training and optimization to ensure that the model can accurately predict the remaining life. Adjust the parameters of the model to improve the prediction accuracy and generalization ability. Verify the accuracy and reliability of the model by comparing the predicted remaining life of the model with the actual spring failure time. The predicted remaining life here is expressed as a percentage of life, ranging from 0 - 100%, where the brand-new state of the spring is defined as 100% life and the failure state is defined as 0% life.

[0144] Step S5, when the predicted remaining life is lower than the preset remaining life threshold, determine the real-time energy storage density of the spring energy storage system in the GIS circuit breaker.

[0145] Since the warning value that the predicted remaining life is lower than a preset remaining life threshold means that the spring may face the risk of aging performance degradation or even breakage, in order to extend the service life of the spring and ensure the safety of the GIS circuit breaker, it is crucial to determine the real-time energy storage density of the spring energy storage system before adjusting the force action of the spring in the spring energy storage system. Preferably, the preset remaining life threshold is 80%.

[0146] Specifically, the spring energy storage system consists of a spring and a hydraulic cylinder. First, an energy conversion model of force-displacement and pressure-volume can be established by analyzing the mechanical properties of the spring and the relevant parameters of the hydraulic cylinder. The force-displacement relationship of the spring is based on Hooke's law, which describes the functional relationship between force and displacement; while the pressure-volume relationship of the hydraulic cylinder describes the change of the hydraulic cylinder volume under different pressures.

[0147] During the energy storage process, the real-time energy storage density is defined as the ratio of the total stored energy in the spring energy storage system to the total volume of the hydraulic cylinder. To calculate the energy data during the energy storage process, numerical integration of this energy conversion model can be performed using the integration method, such as Simpson's integration method. By integrating the energy conversion models of the spring and the hydraulic cylinder and according to the total volume of the hydraulic cylinder, an accurate value of the real-time energy storage density can be obtained.

[0148] Step S7, determine the predicted energy storage demand corresponding to the real-time energy storage density, and dynamically adjust the mechanical-hydraulic energy storage ratio of the spring energy storage system according to the predicted energy storage demand.

[0149] Specifically, the real-time energy storage density refers to the ratio of the real-time energy storage density in the spring energy storage system to the total volume of the hydraulic cylinder. First, by analyzing the real-time energy storage density and combining the current operating state, a calculation method for predicting the energy storage demand can be derived. For example, the historical energy storage density and operating state information of the GIS circuit breaker, such as the mechanical spring compression amount, hydraulic cylinder pressure, and ambient temperature, can be collected as the input data for the prediction model. Subsequently, the wavelet denoising algorithm is used to process the historical energy storage density and operating state information to eliminate the noise in the data and retain the key features. Further, the Z-score normalization process is performed on the denoised data to ensure that all features have the same numerical range, thereby avoiding the influence of model deviation caused by data scale differences. Finally, the long short-term memory network (LSTM) is applied to perform time series analysis and prediction on the normalized data. By training the LSTM model to learn the patterns of historical data and the current state, the future energy storage demand can be predicted. The prediction results can be adjusted and optimized according to the real-time updated current state information to reflect the changing conditions in the system. Finally, the real-time energy storage density is input into the trained long short-term memory network to obtain the predicted energy storage demand.

[0150] Next, the mechanical-hydraulic energy storage ratio is the ratio of the energy storage distribution between the mechanical spring and the hydraulic cylinder in the spring energy storage system. According to the analysis of the system's real-time energy storage density and the current operating state, the compression amount of the mechanical spring and the working pressure of the hydraulic cylinder can be dynamically adjusted to optimize the energy storage performance of the system. Specifically, the dynamic programming algorithm can be used to calculate the optimal mechanical-hydraulic energy storage ratio to obtain the optimal energy storage distribution plan. Once the optimal energy storage distribution plan is determined, the spring energy storage system can perform adaptive control, that is, adjust according to the real-time energy storage density and operating state to maintain the best working state.

[0151] A method for detecting the spring energy storage of a GIS circuit breaker provided in this embodiment obtains the stress concentration coefficient and the material structure change index of the internal spring of the GIS circuit breaker by monitoring the change in the grating reflection wavelength. These parameters will be used to predict the remaining life of the spring, where the stress concentration coefficient reflects the possible crack locations, and the material structure change index affects its fatigue resistance and strength. Considering these factors comprehensively can improve the accuracy of the remaining life prediction. When the predicted remaining life is lower than the preset threshold, it is necessary to evaluate the state of the spring energy storage system by determining the real-time energy storage density. The real-time energy storage density is defined as the ratio of the total stored energy in the spring energy storage system to the total volume of the hydraulic cylinder. According to the prediction of the real-time energy storage density, the mechanical-hydraulic energy storage ratio is further adjusted to optimize the energy storage distribution ratio between the spring and the hydraulic cylinder to ensure that the spring energy storage system is in the best operating state.

[0152] Figure 2 This is the flowchart of step S1 provided in the embodiment of this application, and this process may include the following steps:

[0153] Step S11: Calculate the strain value and the maximum strain value of each measuring point on the outer surface of the GIS circuit breaker according to the grating reflection wavelength by using the Bragg wavelength drift principle.

[0154] Specifically, compare the measured grating reflection wavelength with the original Bragg wavelength of the grating, calculate the change in wavelength, calculate the strain value of each measuring point on the outer shell by using the Bragg wavelength drift principle, and find the maximum strain value after obtaining all the strain values.

[0155]

[0156] Among them, the strain sensitivity coefficient is 1.2 pm / με, and the linear range is ±4000 με.

[0157] For example, first, measure and record the original Bragg wavelength of the fiber Bragg grating in the unstrained state. During the operation of the GIS circuit breaker, use the fiber Bragg grating sensor to record the grating reflection wavelength of each measuring point. For each measuring point, calculate the difference between the grating reflection wavelength and the original Bragg wavelength, that is, the change in wavelength. Then use the strain sensitivity coefficient of 1.2 pm / με to convert the change in wavelength into a strain value. Note that it is also necessary to ensure that the strain value is within the linear range of the fiber Bragg grating sensor, which is ±4000 με. Repeat the above calculation for all measuring points to obtain the strain value of each measuring point on the outer surface of the GIS circuit breaker. Find the maximum value from the strain values of all measuring points, which represents the maximum strain value recorded during the measurement process.

[0158] Step S13: Process the strain values by using a spatial interpolation algorithm to generate a continuous strain distribution field.

[0159] Specifically, the Kriging interpolation can be used as the spatial interpolation algorithm. When using the spatial interpolation algorithm to process the strain values, before processing, first set the range to 200 mm, which represents the spatial scale of the correlation between strain values. Set the nugget effect to 0.1, which affects the smoothness of the interpolation result. Calculate the semivariogram function according to the strain values; construct a spatial covariance matrix by using the semivariogram function; use the covariance matrix and the strain values to execute the interpolation algorithm to generate an interpolation result for each spatial position; integrate the interpolation results to generate a continuous strain distribution field on the outer surface of the GIS circuit breaker.

[0160] Step S15: Calculate the gradient of each spatial point in the strain distribution field to obtain the gradient value and the maximum gradient value of each spatial point.

[0161] Specifically, the gradient is a vector field, whose direction points to the direction in which the strain value increases fastest, and the magnitude represents the rate of change of the strain value. Each component of the gradient vector is the partial derivative of the strain value with respect to each coordinate axis: Gradient value:

[0162] Traverse each spatial point in the strain distribution field, calculate the gradient value, and find the maximum gradient value from all the calculated gradient values.

[0163] Step S17: Determine the spatial points corresponding to the gradient values greater than the preset gradient threshold as the stress concentration regions.

[0164] Specifically, all spatial points can be traversed to check whether the gradient value is greater than the preset gradient threshold. If it is greater than the preset gradient threshold, then mark this spatial point as part of the strain concentration region. The preset gradient threshold is preferably 0.01 με / mm.

[0165] Step S19: Determine the stress concentration coefficient and the material structure change index according to the maximum strain value, the maximum gradient value, and the area of the stress concentration region through a preset strain distribution - stress model.

[0166] Specifically, use a preset strain distribution - stress model to determine the stress concentration coefficient and the material structure change index. The Support Vector Regression (SVR) algorithm can be selected to establish the mapping relationship between the strain distribution pattern and the internal spring stress state. The Radial Basis Function (RBF) is selected as the kernel function in the SVR algorithm, and the five - fold cross - validation method is used to optimize the parameters C and γ of the SVR model. The parameter C controls the penalty of the error term, and γ determines the width of the radial basis kernel. Use historical data to train the SVR model, taking the maximum strain value, the maximum gradient value, and the area of the stress concentration region as inputs, and the stress concentration coefficient and the material structure change index as outputs. Use five - fold cross - validation to evaluate the performance of the model, and adjust the model parameters until a satisfactory accuracy is achieved. Analyze the stress concentration coefficient and the material structure change index output by the model, compare them with the actual measured values, verify the prediction ability of the model, and finally obtain the trained strain distribution - stress model. The strain distribution - stress model can be used to predict the stress concentration coefficient and the material structure change index.

[0167] The normal value of the stress concentration coefficient here is < 2.5, and the normal value of the material structure change index is < 0.2. It is also possible to judge whether it is abnormal through these two data. If it is abnormal, an alarm can be given to remind the staff to perform maintenance and other operations.

[0168] And Figure 1Compared with the embodiments shown, in this embodiment, the strain value of each measurement point is first calculated using the principle of Bragg wavelength drift. Subsequently, a spatial interpolation algorithm is used to process these strain values to generate a continuous strain distribution field, thereby effectively inferring the strain distribution on the outer surface of the entire GIS circuit breaker and providing accurate spatial data. Then, gradient calculations are performed on the strain distribution field to obtain the gradient values of each spatial point, which reflect the rate of strain change and help determine the stress concentration areas in the structure. By setting a gradient threshold, the stress concentration areas in the strain distribution field are identified and marked. These areas may be critical stress concentration parts in the structure and require special attention and analysis. Finally, a mapping relationship between the strain distribution pattern and the internal spring stress state is established using a preset strain distribution - stress model, and then the stress concentration coefficient and material structure change index are predicted, which helps to monitor and analyze the performance of the spring in the GIS circuit breaker in real time.

[0169] Figure 3 The flowchart of step S3 provided by the embodiment of the present application may include the following steps:

[0170] Step S31: Obtain an image of the spring and extract the microscopic damage characteristics of the image, where the microscopic damage characteristics characterize the damage and defects of the spring.

[0171] Specifically, a scanning electron microscope (SEM) or a transmission electron microscope (TEM) can be used to take a comprehensive picture of the spring with a magnification of 5000 times. Ensure that the image clarity is sufficient to reveal the microscopic damage characteristics. Preprocess the collected image, including adjusting brightness, contrast, denoising, etc., to improve the image quality. Use the Otsu threshold segmentation algorithm to process the image to separate the damage characteristics in the image from the background. Extract the microscopic damage characteristics from the processed image. The microscopic damage characteristics may include crack density (number of cracks per square millimeter), defect size distribution (statistical distribution of defect diameters), and dislocation density (length of dislocation lines per square micrometer).

[0172] Step S33: Classify the microscopic damage characteristics to determine different damage levels.

[0173] Specifically, a support vector machine is selected as the classification model to classify the microscopic damage characteristics, and a radial basis function (RBF) kernel is used. Set the penalty parameter C (10 -2 -10 2 ) and the kernel parameter γ (10 -4-1), the grid search method is used to traverse all possible combinations of C and γ to find the best parameter pair. 5-fold cross-validation is adopted to evaluate the model performance under different parameter combinations to ensure the generalization ability of the model. The SVM model is trained using historical data, with the microscopic damage features as the input and the damage level as the output. The trained SVM model is used to classify new microscopic damage feature data to determine its damage level, which is divided into three levels: low, medium, and high.

[0174] Step S35, determine the stress accumulation factor and fatigue index corresponding to the spring.

[0175] Specifically, first, the model consists of 3 convolutional layers and 2 fully connected layers. Each convolutional layer has 64 3×3 convolutional kernels, followed by a ReLU activation function. Each fully connected layer has 128 neurons and also uses the ReLU activation function. The model uses the Adam optimizer with an initial learning rate set to 0.001. The input data is the time series of the stress accumulation factor and fatigue index of the spring in the past 100 cycles, where the stress accumulation factor is normalized to the range of 0-1, and the fatigue index is normalized to the range of 0-100. The output of the model is the predicted stress accumulation factor and fatigue index for the next 10 cycles. The training process is 1000 epochs, and the entire dataset is traversed once in each epoch. After training, the model is applied to the actual operation of the spring. The stress accumulation factor and fatigue index data of the past 100 cycles are collected in real time through sensors and data acquisition systems and are used for prediction to predict the stress accumulation factor and fatigue index of the spring for the next 10 cycles.

[0176] Step S37, input the stress concentration coefficient, the material structure change index, the damage level, the stress accumulation factor, and the fatigue index into a preset random forest tree algorithm model to obtain the predicted remaining life of the spring.

[0177] Specifically, during the prediction process of the remaining life of the spring, the random forest algorithm is adopted. First, the Gini index is used as the criterion for feature selection to help identify the features that contribute the most to the prediction. Through the Out-of-Bag (OOB) error, the importance of each feature is evaluated, and it is found that the damage level is the most important feature, with a contribution rate of 40%. Followed by the stress accumulation factor, accounting for 30%, and the contribution rate of the fatigue index is 20%. In addition, the stress concentration coefficient and the material structure change index also have an impact on the prediction, each accounting for 5%.

[0178] After determining the feature importance, set the key parameters of the random forest algorithm model: select 100 trees and set the maximum depth of each tree to 10. Such parameter settings are aimed at balancing the complexity and generalization ability of the model. To evaluate the prediction accuracy of the model, use the validation set and cross-validation method to ensure the reliability of its prediction results, and the prediction error is within ±500 working cycles. Finally, use the trained and validated random forest algorithm model to predict the remaining life of the spring.

[0179] Compared with Figure 1 the embodiment shown, in this embodiment, by acquiring the image of the spring, then performing image preprocessing and Otsu threshold segmentation to extract microscopic damage features, these features can accurately characterize the damage and defect conditions of the spring. Then classify the extracted microscopic damage features to determine different damage levels (low, medium, high). Then predict the stress accumulation factor and fatigue index of the spring in the past cycles to obtain the predicted stress accumulation factor and fatigue index. Finally, use the stress concentration coefficient, material structure change index, damage level, stress accumulation factor and fatigue index as inputs, and use the random forest algorithm model to predict the remaining life of the spring.

[0180] Figure 4 It is the flowchart of step S5 provided by the embodiment of the present application. This process may include the following steps:

[0181] Step S51, in the case where the predicted remaining life is lower than the preset remaining life threshold, construct an energy conversion model between the spring and the hydraulic cylinder in the spring energy storage system.

[0182] Specifically, the predicted remaining life being lower than the preset remaining life threshold means that the spring may face the risk of reduced aging performance or even breakage. To extend the service life of the spring and ensure the safety of the GIS circuit breaker, it is necessary to adjust the force action of the spring in the spring energy storage system in real time. Therefore, it is necessary to construct an energy conversion model to determine the energy conversion data.

[0183] First, a parallel structure of a mechanical spring and a hydraulic cylinder is adopted to ensure that both can work simultaneously and jointly provide the required energy. The maximum compression of the mechanical spring is 50 mm, and the maximum stroke of the hydraulic cylinder is 100 mm. The working pressure of the hydraulic cylinder is precisely adjusted from 0 to 20 MPa through an electro-hydraulic proportional valve to change the output force of the hydraulic cylinder, thereby adjusting the energy storage ratio of the mechanical spring and the hydraulic cylinder. In addition, high-precision displacement sensors and pressure sensors are installed on the mechanical spring and the hydraulic cylinder of the spring energy storage system, and a Data Acquisition Module (DAQ) is used to collect sensor data in real time. The sampling frequency is set to 1 kHz (i.e., 1000 samples per second), and the resolution of the displacement sensor is 0.01 mm, ensuring accurate measurement of the spring compression displacement. The accuracy of the pressure sensor is 0.1% FS (Full Scale), ensuring the accuracy of the hydraulic cylinder pressure measurement. A 32-point sliding window average filtering algorithm is used to denoise the original data to reduce noise and fluctuations in the data. The window overlap rate is set to 50%, and each window overlaps 16 data points (half of 32 points) with the previous window, which helps to maintain the continuity and stability of the filtered data.

[0184] Then, based on the characteristics of the mechanical spring and the mechanical properties of the hydraulic cylinder, a force-displacement relationship function of the spring and a pressure-volume relationship function of the hydraulic cylinder are established respectively. After considering leakage losses and friction losses, these two relationship functions are combined to obtain a total energy conversion model, which can be obtained by adding or weighted summing the force-displacement relationship function of the spring and the pressure-volume relationship function of the hydraulic cylinder. This energy conversion model helps to calculate energy conversion data subsequently and helps to precisely control the working states of the spring and the hydraulic cylinder to ensure that the spring energy storage system operates within the optimal performance range.

[0185] Step S53, calculate the energy conversion data during the energy storage process according to the energy conversion model.

[0186] Specifically, the spring compression displacement and the pressure of the hydraulic cylinder are obtained in real time through the displacement sensor and the pressure sensor, and then the real-time energy conversion data is calculated using the energy conversion model. The integration method, such as the Simpson integration method, can be used to perform numerical integration on the energy conversion model to obtain the energy conversion data. Among them, the integration step size in the Simpson integration method is 0.1 mm (displacement) and 0.1 MPa (pressure).

[0187] Step S55, determine the real-time energy storage density according to the energy conversion data and the total volume of the hydraulic cylinder.

[0188] Specifically, the real-time energy storage density can be calculated according to the following formula:

[0189]

[0190] Among them, ρ is the real-time energy storage density, E total,adjusted is the energy conversion data, and V is the total volume of the hydraulic cylinder.

[0191] In addition, the Kalman filter algorithm is also used to smooth the real-time energy storage density data. Among them, the process noise covariance is set to 0.01, and the measurement noise covariance is set to 0.1. Through the Kalman filter, the random fluctuations in the data can be reduced, and smoother real-time energy storage density data can be obtained.

[0192] Compared with Figure 1 the embodiment shown, when the predicted remaining life in this embodiment is lower than the preset remaining life threshold, by adjusting the energy storage ratio, the dependence on the spring is reduced, thereby reducing the stress on the spring and extending its service life. By constructing an energy conversion model between the spring and the hydraulic cylinder, the real-time energy storage density can be obtained according to the real-time energy conversion data, so as to adjust the energy storage ratio in combination with the real-time energy storage density subsequently to ensure that the spring energy storage system operates within the optimal performance range and optimize the overall performance of the spring energy storage system.

[0193] Figure 5 The flowchart of step S51 provided by the embodiment of the present application is as follows, and this process may include the following steps:

[0194] Step S511, obtain the spring compression displacement, the force applied to the spring, the hydraulic cylinder pressure, and the total volume of the hydraulic cylinder.

[0195] Specifically, the spring compression displacement, the force applied to the spring, and the hydraulic cylinder pressure are obtained through sensors, and the total volume of the hydraulic cylinder is obtained through the product manual provided by the supplier for subsequent calculation of the energy storage density.

[0196] Step S513, establish the force-displacement relationship function of the spring according to the force and the spring compression displacement.

[0197] Specifically, mechanical energy storage can be expressed as the change in spring potential energy. Introduce the mechanical friction loss rate α (5-10%), that is, the force-displacement relationship function:

[0198] F(x) = k·x

[0199]

[0200] E m,adjusted = E m ×(1 - α)

[0201] Among them, F(x) is the applied force; x is the spring compression displacement; k is the spring stiffness, k = 100N / mm; E m,adjusted is the mechanical energy storage considering friction loss.

[0202] Step S515: Establish a pressure - volume relationship function for the hydraulic cylinder based on the hydraulic cylinder pressure and the total volume.

[0203] Specifically, hydraulic energy storage can be expressed as the change in the pressure work of the hydraulic system. Introduce a leakage loss rate β (3 - 8%), that is, the pressure - volume relationship function:

[0204] P = F’ / A

[0205]

[0206] E h,adjusted = E h ×(1 - β)

[0207] where P is the hydraulic cylinder pressure; F’ is the force acting on the piston; A is the area of the piston, A = 50 cm 2 ; V is the total volume; E h,adjusted is the hydraulic energy storage considering leakage losses.

[0208] Step S517: Integrate the force - displacement relationship function and the pressure - volume relationship function to obtain the energy conversion model.

[0209] Specifically, the energy conversion model: E total,adjusted = E m,adjusted + E h,adjusted where E total,adjusted is the energy conversion data.

[0210] Compared with the embodiment shown in Figure 4 , in this embodiment, through the data obtained by the sensor and combined with the mechanical characteristics, a force - displacement relationship function and a pressure - volume relationship function are constructed, which can accurately calculate the mechanical energy storage and hydraulic energy storage. Through the comprehensively adjusted mechanical energy storage and hydraulic energy storage, a comprehensive energy conversion model is obtained, which can better predict and optimize the energy efficiency and performance of the entire spring energy storage system.

[0211] Figure 6 The flowchart of step S7 provided by the embodiment of the present application is as follows, and this process may include the following steps:

[0212] Step S71: According to the real - time energy storage density, use a preset long - short - term memory network to predict the energy storage demand in the future time period, and obtain the predicted energy storage demand corresponding to each time point in the future time period.

[0213] Specifically, a three-layer LSTM network with 64 neurons in each layer is designed to process time series data. The input features include energy storage density, spring compression displacement, hydraulic cylinder pressure, and ambient temperature, and the output is the predicted energy storage demand for the next 24 hours. First, historical energy storage density and current state information such as spring compression displacement, hydraulic cylinder pressure, and ambient temperature are collected. The data is denoised by applying 5-layer db4 wavelet decomposition to reduce the influence of environmental noise and measurement errors. Then, Z-score normalization is used to make the data have zero mean and unit standard deviation, improving the model training effect. The historical data is divided into 24-hour windows with a sliding step of 1 hour for time series analysis. The LSTM network is used for in-depth analysis to capture the dynamic features and long-term dependencies in the data. The Adam optimizer (learning rate 0.001) is used for 1000 rounds of training until the validation set loss drops below 0.05. After training, the LSTM network can be used to predict the energy storage demand in the future time period.

[0214] Step S73: Calculate the energy storage change from the current state to the next state for the predicted energy storage demand corresponding to any time point.

[0215] Step S75: Dynamically program all the energy storage changes with the goal of maximizing the energy storage efficiency to determine the optimal path.

[0216] Step S77: Determine the optimal mechanical-hydraulic energy storage ratio for each time point according to the optimal path.

[0217] Step S79: Dynamically adjust the mechanical-hydraulic energy storage ratio of the spring energy storage system according to the optimal mechanical-hydraulic energy storage ratio.

[0218] In steps S73 to S79, through the dynamic programming algorithm, for the predicted energy storage demand at each time point, calculate and optimize the energy storage change from the current state to the next state. With the goal of maximizing the energy storage efficiency, use the dynamic programming algorithm to program all the energy storage changes to determine the optimal energy allocation path. In this process, constraint conditions such as the total energy storage, remaining spring life, and hydraulic system pressure limit need to be considered to ensure that the system operates within the safe and design range. Define the objective function η=(E m +E h ) / (E m,max +E h,max ) to maximize the energy storage efficiency, where E m and E h are mechanical energy storage and hydraulic energy storage respectively, and E m,max and E h,max are their maximum values. Set constraint conditions including the total energy storage (50 - 100 kJ), remaining spring life (greater than 10 ^6The number of cycles (less than 2000 cycles), the pressure limit of the hydraulic system (less than 25 MPa).

[0219] Specifically, divide the 24-hour time into 1-hour intervals, and establish the states of dynamic programming for each time point. For each time point, establish the state transition equation of dynamic programming, considering the energy storage change from the current state to the next state. Define the initial allocation of mechanical energy storage and hydraulic energy storage for the initial state of the dynamic programming algorithm (usually the start of the time series). For each time point, calculate all possible paths from the current state to the next state, and evaluate the efficiency of each path. Apply constraint conditions during state transition to exclude those transitions that violate the total energy storage, remaining spring life, or hydraulic pressure limit. Use the iterative method of dynamic programming to gradually determine the optimal path from the initial state to the final state, which maximizes the objective function. According to the optimal path, calculate the optimal allocation ratio of mechanical energy storage and hydraulic energy storage at each time point.

[0220] In addition, adopt the model predictive control (MPC) method to achieve the rolling optimization of the energy storage system through the strategy of predicting 6 hours and controlling 1 hour in real time. The initial PID controller parameters are determined by the Ziegler-Nichols method and adjusted online in combination with the fuzzy adaptive algorithm, and the adjustment range is ±20% to meet the requirements of system dynamic changes. Through wavelet denoising and Z-score normalization processing, the data quality is improved, providing accurate inputs for the LSTM network and the MPC algorithm. The establishment of the state space model further describes the energy storage dynamics, making the control strategy more accurate. The entire system can respond to external environmental changes and internal state fluctuations in a timely manner through real-time monitoring and adaptive adjustment, achieving the maximization of energy storage efficiency, extending the spring life, and ensuring that the hydraulic system pressure is within the safe range.

[0221] Compared with Figure 1 the embodiment shown, this embodiment can predict the energy storage demand at each time point in the future time period by using the long short-term memory network, ensuring the accuracy of the prediction results. Then, determine the optimal path through dynamic programming, thereby optimizing the energy storage change process and achieving the maximization of energy storage efficiency. Then, according to the determined optimal path, determine the mechanical-hydraulic energy storage ratio at each time point, so as to achieve the best energy storage configuration at different time points and ensure that the performance of the energy storage system reaches the optimal at each time point. By dynamically adjusting the mechanical-hydraulic energy storage ratio, it can respond to the changes in energy storage demand in real time, making the system always maintain high efficiency and stability during actual operation.

[0222] Figure 7 The flowchart for determining the existence of microscopic damage to the spring provided by the embodiment of the present application, this process may include the following steps:

[0223] Step S101, obtain the reflected wave signal for the spring in the GIS circuit breaker.

[0224] Specifically, use a high-frequency ultrasonic detection device to scan the spring in the GIS circuit breaker from multiple angles, and collect the reflected wave signals at different angles. Select a high-frequency ultrasonic detection device with a frequency of 50 MHz to perform a 360-degree full-range scan on the spring in the GIS circuit breaker, and collect a set of reflected wave signals every 15 degrees to ensure that all key angles are covered. A total of 24 sets of reflected wave signals are obtained, and each set corresponds to the measurement result of a specific angle. The 24 sets of reflected wave signals are superimposed in phase to enhance the useful signals and reduce the random noise. Through signal superposition, the signal-to-noise ratio is improved, increasing by approximately 6 dB.

[0225] Step S103, perform waveform analysis on the reflected wave signal and extract the waveform feature vector.

[0226] Specifically, perform waveform analysis on the collected reflected wave signal, extract key waveform features such as amplitude (0.1 - 1 V), frequency (center frequency of 50 MHz), and phase (0 - 360 degrees) to form a waveform feature vector. According to the known propagation speed of ultrasonic waves in the spring material (about 5900 m / s), analyze the arrival time of the reflected wave to infer the internal structure information.

[0227] Step S105, calculate the Euclidean distance between the waveform feature vector and the standard waveform feature vector in the preset standard feature library.

[0228] Specifically, create a standard feature library for healthy springs, which contains the reflected wave characteristics under 10 different working conditions, and each condition has 100 sets of sample data. Calculate the Euclidean distance between the waveform feature vector to be measured and each standard vector in the standard feature library.

[0229] Step S107, when the Euclidean distance is greater than the preset distance threshold, determine that there is microscopic damage to the spring.

[0230] Specifically, if the Euclidean distance between the waveform feature vector to be measured and any standard vector in the standard feature library is greater than the preset distance threshold, it is determined that there may be microscopic damage inside the spring. The microscopic damage is the expansion of microcracks or microscopic plastic deformation.

[0231] And Figure 1Compared with the shown embodiments, in this embodiment, by acquiring the reflected wave signal and performing waveform analysis and feature vector extraction on it, an accurate description of the internal structure and state of the spring is achieved. This method helps to identify the working state and potential problems of the spring. Further, by comparing the waveform feature vector with the data in the standard feature library and calculating the Euclidean distance, it is possible to accurately determine whether there is microscopic damage to the spring, thereby quickly locating the anomaly and effectively evaluating the health state of the spring.

[0232] Figure 8 The flow chart for determining the total health score corresponding to the spring provided by the embodiment of the present application, wherein the waveform feature vector includes amplitude, frequency, and phase. The process may include the following steps:

[0233] Step S201, determine the amplitude decay rate according to the ratio of the amplitude to the initial amplitude.

[0234] Specifically, the amplitude decay rate ADR generally refers to the ratio of the amplitude of the reflected wave signal to its initial amplitude:

[0235]

[0236] wherein, V initial is the initial amplitude, and V current is the currently measured amplitude.

[0237] Step S203, determine the frequency offset according to the difference between the frequency and the initial frequency.

[0238] Specifically, the frequency offset is the difference between the frequency of the reflected wave signal and its initial frequency: Δf = |f current - f initial |, wherein, f current is the currently measured frequency, and f initial is the initial frequency.

[0239] Step S205, determine the phase difference according to the difference between the phase and the initial phase.

[0240] Specifically, the phase difference refers to the difference between the phase of the reflected wave signal and the initial phase: ΔΦ = |Φ current - Φ initial |, wherein, Φ current is the currently measured phase, and Φ initial is the initial phase.

[0241] Step S207, determine the total health score corresponding to the spring based on the amplitude decay rate, the frequency offset, and the phase difference.

[0242] Specifically, set the threshold range of the reflected wave signal characteristic parameters, including the amplitude attenuation rate, frequency offset, and phase difference. If the amplitude attenuation rate is greater than 20%, the frequency offset exceeds 2 MHz, and the phase difference is greater than 30 degrees, then according to the degree to which these parameters exceed the threshold, set a maximum score for each parameter. For example, the maximum score for each parameter is 100 points. If the parameter does not exceed the threshold, the maximum score is obtained; if it exceeds the threshold, the corresponding score is deducted according to the exceeding degree. Combine the scores of the three parameters to obtain the total health status score of the spring energy storage system. For example, if the scores of the amplitude attenuation rate, frequency offset, and phase difference are 85, 90, and 95 respectively, the comprehensive score may be (85 + 90 + 95) / 3 = 90 points.

[0243] According to the comprehensive score, divide the health status of the spring energy storage system into three levels:

[0244] 0 - 60 points: Seriously abnormal, and immediate measures may be required.

[0245] 61 - 80 points: Moderately abnormal, and further monitoring and evaluation are required.

[0246] 81 - 100 points: Slightly abnormal or normal, and the system is operating well.

[0247] Compared with Figure 7 the embodiment shown, this embodiment can accurately evaluate the health status of the spring by measuring and calculating the amplitude attenuation rate, frequency offset, and phase difference of the reflected wave signal. Divide the spring health status into different levels (seriously abnormal, moderately abnormal, slightly abnormal or normal) by combining the scores of each parameter. It can provide a clear health status and help decision-makers take corresponding measures according to the actual health status.

[0248] In some embodiments, the reflected wave signal can also be analyzed using continuous wavelet transform, and Morlet wavelet is selected as the mother wavelet, and the center frequency is set to 50MHz, which matches the frequency of the ultrasonic wave in the spring material. The wavelet coefficients of the reflected wave signal are calculated, the energy distribution of the wavelet coefficients is analyzed, and the microstructural changes inside the spring material, such as microcrack extension or microplastic deformation, are identified. If the energy concentration in a certain area exceeds 10% of the total energy, it is determined that there is damage in the area. The specific location of the abnormality inside the spring is located using a method with an accuracy of 0.1mm through the center position of the energy distribution. Specifically, the wavelet coefficient energy density map at each scale can be drawn, and these images can intuitively show the characteristics of the energy distribution in the signal. Observe the areas with significant increase or decrease in energy in the energy density map, which may indicate defects or abnormalities inside the material. Look for peak points in the energy density map, which usually correspond to damaged or defective areas inside the material. Defects of different sizes may show the greatest energy changes at different scales. By comparing the energy distribution maps of different scales, the size of the defect can be estimated. Combined with the location of the peak and the characteristics of the energy distribution, the specific location of the defect in the material is determined. The wavelet coefficient calculation and energy distribution analysis of the reflected wave signal are realized, providing a scientific basis for the non-destructive testing and health assessment of the spring energy storage system of the GIS circuit breaker.

[0249] Figure 9 A flowchart for determining whether a spring is abnormal is provided in an embodiment of the present application, and the flowchart may include the following steps:

[0250] Step S301, obtaining the original strain signal of the spring in the GIS circuit breaker.

[0251] Specifically, a micro piezoelectric strain sensor can be made of polyvinylidene fluoride (PVDF) piezoelectric film material. A micro piezoelectric strain sensor array with a thickness of 28μm is designed to ensure that it can accurately measure tiny strain changes. The stress distribution of the GIS circuit breaker housing is simulated using finite element analysis (FEA) software to determine the stress concentration area. The micro piezoelectric strain sensor array is pasted at the five locations with the largest stress determined by the finite element analysis to achieve high-precision measurement of key areas. The sensor has a sensitivity of 15mV / με and can convert tiny strain changes into electrical signals. Using the data acquisition module, a sampling frequency of 1000Hz and an acquisition time window of 10 seconds are set to obtain the original strain signal for the spring in the GIS circuit breaker. The tiny deformation signal of the GIS circuit breaker housing is collected by the data acquisition module to obtain the original strain signal of 50,000 data points.

[0252] Step S303: performing time-frequency analysis on the original strain signal to extract time domain features and frequency domain features.

[0253] Specifically, before performing time-frequency analysis on the original strain signal, it is also necessary to use a sixth-order Butterworth low-pass filter to denoise the original strain signal, with the cut-off frequency set at 200 Hz to filter out high-frequency interference and noise. Then, time-frequency analysis is performed on the denoised original strain signal to extract the time-domain characteristics of the original strain signal: the root mean square (RMS) value ranges from 0.5 to 2 με; the peak-to-peak value ranges from 2 to 8 με; the peak factor ranges from 3 to 5. The signal is analyzed in the frequency domain through a 2048-point fast Fourier transform (FFT) algorithm, in conjunction with a Hanning window function to reduce spectral leakage. The frequency-domain characteristics of the signal are extracted from the frequency-domain analysis, including the first five main frequency components and their corresponding amplitudes. These frequency components are typically in the range of 10 - 100 Hz.

[0254] Step S305: Input the time-domain characteristics and the frequency-domain characteristics into a preset strain signal feature - elastic modulus model to obtain an estimated value of the elastic modulus.

[0255] Specifically, a support vector regression algorithm is used to establish a strain signal feature - elastic modulus model, which maps the input time-domain and frequency-domain characteristic parameters to the estimated value of the elastic modulus of the spring. The radial basis function (RBF) kernel function is selected as the kernel function in the SVR algorithm, and the parameters C (2 -5 to 2 15 ) and γ (2 -15 to 2 3 ) are optimized within a preset range through a grid search method. The parameter C controls the regularization strength of the model, while γ defines the influence range of a single training sample. The SVR model is trained using the extracted characteristic parameters, with 13 characteristic parameters as the input (including the root mean square value, peak-to-peak value, peak factor, 5 main frequency components and their corresponding amplitudes), and the estimated value of the elastic modulus of the spring as the output.

[0256] Step S307: Determine the degree of reduction in the elastic modulus corresponding to the estimated value of the elastic modulus.

[0257] Specifically, set an initial value for the elastic modulus of the spring, such as 210 GPa. Degree of reduction in elastic modulus = Estimated value of elastic modulus / Initial value of the elastic modulus of the spring.

[0258] Step S309: If the degree of reduction in the elastic modulus exceeds a preset reduction threshold, determine that the spring is abnormal.

[0259] Specifically, if the degree of reduction in the elastic modulus is less than 90% (189 GPa) of the initial value, it is determined that the spring is abnormal, and a spring anomaly alarm is triggered to indicate a significant reduction in the elastic modulus of the spring.

[0260] And Figure 1Compared with the embodiments shown, in this embodiment, by extracting time-domain features and frequency-domain features from the acquired original strain signal, the changes and features of the signal can be effectively characterized. These features provide important information for subsequent elastic modulus estimation, which helps to accurately identify the state of the spring. By mapping the time-domain and frequency-domain features to the elastic modulus estimation value of the spring through the strain signal feature-elastic modulus model, the elastic modulus estimation value can be accurately obtained. Calculating the degree of elastic modulus reduction can quantify the performance change of the spring, providing a clear standard to evaluate whether there is an abnormality in the spring. By setting a threshold to determine whether there is an abnormality in the spring, automatic monitoring and alarming can be achieved. If the degree of elastic modulus reduction exceeds the preset threshold, an alarm will be triggered. This mechanism can timely detect the performance degradation of the spring, thereby preventing potential failures or equipment damage and ensuring the reliability and safety of the equipment.

[0261] Figure 10 The flowchart for determining the total cumulative damage amount provided by the embodiments of the present application may include the following steps:

[0262] Step S401: Obtain the magnetic field data generated at different positions of the spring during the charging and discharging process; wherein, the magnetic field data includes the magnetic field strength and the magnetic field direction.

[0263] Specifically, a Hall element and a fluxgate sensor are combined and used. A group of sensors is arranged every 10 cm along the circumferential direction of the GIS circuit breaker housing, and 3 layers are arranged axially to form a 3D sensor array with 72 detection points. The measurement range of each detection unit is ±2000 gauss, and the sensitivity is 0.1 gauss. The sampling frequency is 1 kHz, and 72000 magnetic field data are obtained per second.

[0264] Step S403: Interpolate the magnetic field data using cubic spline interpolation to generate an electromagnetic field distribution map.

[0265] Specifically, the cubic spline interpolation algorithm is used to process the discrete data to generate a continuous electromagnetic field distribution map with a resolution of 1 mm. The number of interpolation nodes: 100×100×30, ensuring sufficient resolution and data accuracy.

[0266] Step S405: Input the electromagnetic field distribution map as a boundary condition into a preset corresponding relationship model, and use the conjugate gradient method to solve the deformation data of the spring under the action of the electromagnetic field; wherein, the corresponding relationship model is established by finite element software.

[0267] Specifically, a corresponding relationship model between the spring deformation and the electromagnetic field distribution is established by finite element software. Among them, the corresponding relationship model uses tetrahedral meshes to divide the spring structure; the element size is 0.5 mm, and the total number of meshes is about 2 million. The conjugate gradient method is used for reverse solution, with a maximum number of iterations of 1000 and a convergence error of 0.1%.

[0268] Step S407: Determine the expected fatigue life corresponding to the deformation data and generate the total cumulative damage amount corresponding to the expected fatigue life.

[0269] Specifically, perform fatigue analysis on the spring based on the linear cumulative damage theory of the Miner criterion. Divide the number of stress cycles by the fatigue life at the corresponding stress level to obtain the damage amount of a single cycle, accumulate the damage amounts of multiple cycles, and calculate the cumulative degree of fatigue stress.

[0270] Step S409: When the total cumulative damage amount exceeds the preset damage threshold, determine that there is a risk of failure for the spring.

[0271] Specifically, if the cumulative damage value exceeds 0.8, it is determined that there is a potential risk of failure for the spring.

[0272] Compared with Figure 1 the embodiment shown, in this embodiment, by collecting high-precision magnetic field data of the spring during the charging and discharging process, and then using cubic spline interpolation to generate the electromagnetic field distribution map, the continuity and accuracy of the data are ensured, so as to accurately reflect the magnetic field change of the spring. Input the electromagnetic field distribution map as the boundary condition into the finite element model, use tetrahedral meshing and the conjugate gradient method to perform high-precision deformation solution, apply the Miner criterion to calculate the fatigue damage of the spring, and considering the stress accumulation of multiple cycles, accurately predict the expected fatigue life of the spring. By calculating the total cumulative damage amount, if it exceeds the preset damage threshold, the potential risk of failure of the spring can be effectively judged, providing a scientific basis for maintenance and replacement.

[0273] Figure 11 This is the flowchart of step S407 provided by the embodiment of the present application. This process may include the following steps:

[0274] Step S4071: Determine the strain corresponding to the deformation data and generate the corresponding stress according to the strain.

[0275] Specifically, strain is the relative change of displacement with respect to the original size and can be calculated by the following formula:

[0276]

[0277] where du is the deformation data, i.e., the displacement change amount, and x is the original size;

[0278] The relationship between stress (σ) and strain (ε) can be expressed as: σ = E·ε, where E is the elastic modulus.

[0279] Step S4073: Map the stress to a preset S-N curve of the spring material to determine the expected fatigue life of each stress; wherein, the preset S-N curve is a curve describing the number of cycles that the material can withstand under different stresses.

[0280] Specifically, set the S-N curve of the spring material with a fatigue limit of 400 MPa corresponding to 10^7 cycles. Perform fatigue analysis on the spring based on the linear cumulative damage theory of the Miner criterion.

[0281] Step S4075: For each stress, calculate the damage amount of a single cycle using the Miner criterion.

[0282] Specifically,

[0283]

[0284] where D is the damage amount, N is the actual number of cycles the spring has experienced under the current stress, and N f is the expected fatigue life under the current stress;

[0285] Step S4077: Accumulate the damage amounts of all the stresses to obtain the total cumulative damage amount.

[0286] Specifically,

[0287] D total = ∑D i

[0288] where D total is the total cumulative damage amount, and D i is the damage amount under the i-th stress.

[0289] Compared with the embodiment shown in Figure 10 , in this embodiment, by calculating the ratio of the displacement change amount to the original size, the strain of the spring is obtained. Further, the stress is calculated by multiplying the strain and the elastic modulus. Mapping the calculated stress value to the preset S-N curve can determine the corresponding fatigue life for each stress value. This mapping process provides the fatigue life expectation for each stress level, ensuring the accuracy of the fatigue analysis. By comparing the fatigue limit of the spring material and the actual stress, the durability under different stress levels can be accurately predicted. The Miner criterion is used to calculate the damage amount of a single cycle, which is the ratio of the actual number of cycles of the spring under the current stress to the expected fatigue life. By accumulating the damage amounts under all stresses, the total cumulative damage amount is obtained. This total cumulative damage amount can effectively predict the overall fatigue state of the spring and help predict possible failure risks.

[0290] Correspondingly, please refer to Figure 12, a block diagram of a spring energy storage detection system for a GIS circuit breaker provided by an embodiment of the present application. The system includes:

[0291] A concentration coefficient and change index determination module 111, configured to determine a stress concentration coefficient and a material structure change index corresponding to a spring inside the GIS circuit breaker based on grating reflection wavelengths obtained at multiple measurement points on the outer surface of the GIS circuit breaker, where the material structure change index characterizes the grain size change rate;

[0292] A predicted remaining life module 222, configured to predict the remaining life of the spring based on the stress concentration coefficient and the material structure change index, and determine the predicted remaining life of the spring;

[0293] An energy storage density determination module 333, configured to determine the real-time energy storage density of the spring energy storage system in the GIS circuit breaker when the predicted remaining life is lower than a preset remaining life threshold;

[0294] A dynamic adjustment module 444, configured to determine a predicted energy storage demand corresponding to the real-time energy storage density, and dynamically adjust the mechanical-hydraulic energy storage ratio of the spring energy storage system according to the predicted energy storage demand.

[0295] Optionally, the concentration coefficient and change index determination module 111 includes:

[0296] According to the grating reflection wavelength, calculate the strain value and the maximum strain value of each of the measurement points on the outer surface of the GIS circuit breaker by using the Bragg wavelength drift principle;

[0297] Use a spatial interpolation algorithm to process the strain values to generate a continuous strain distribution field;

[0298] Perform gradient calculation on each spatial point in the strain distribution field to obtain the gradient value and the maximum gradient value of each spatial point;

[0299] Determine the stress concentration region as the spatial points corresponding to the gradient values greater than a preset gradient threshold;

[0300] According to the maximum strain value, the maximum gradient value, and the area of the stress concentration region, determine the stress concentration coefficient and the material structure change index through a preset strain distribution-stress model.

[0301] Optionally, the predicted remaining life module 222 includes:

[0302] Obtain an image of the spring, and extract the microscopic damage characteristics of the image, where the microscopic damage characteristics characterize the damage and defects of the spring;

[0303] Classify the micro-damage characteristics to determine different damage levels;

[0304] Determine the stress accumulation factor and fatigue index corresponding to the spring;

[0305] Input the stress concentration factor, the material structure change index, the damage level, the stress accumulation factor, and the fatigue index into a preset random forest tree algorithm model to obtain the predicted remaining life of the spring.

[0306] Optionally, the energy storage density determination module 333 includes:

[0307] Construct an energy conversion model between the spring and the hydraulic cylinder in the spring energy storage system when the predicted remaining life is lower than a preset remaining life threshold;

[0308] Calculate the energy conversion data during the energy storage process according to the energy conversion model;

[0309] Determine the real-time energy storage density according to the energy conversion data and the total volume of the hydraulic cylinder.

[0310] Optionally, constructing the energy conversion model between the spring and the hydraulic cylinder in the spring energy storage system includes:

[0311] Obtain the spring compression displacement, the force exerted on the spring, the hydraulic cylinder pressure, and the total volume of the hydraulic cylinder;

[0312] Establish a force-displacement relationship function of the spring according to the force and the spring compression displacement;

[0313] Establish a pressure-volume relationship function of the hydraulic cylinder according to the hydraulic cylinder pressure and the total volume;

[0314] Integrate the force-displacement relationship function and the pressure-volume relationship function to obtain the energy conversion model.

[0315] Optionally, the dynamic adjustment module 444 includes:

[0316] According to the real-time energy storage density, use a preset long short-term memory network to predict the energy storage demand in a future time period, and obtain the predicted energy storage demand corresponding to each time point in the future time period;

[0317] For the predicted energy storage demand corresponding to any time point, calculate the energy storage change from the current state to the next state;

[0318] Taking the maximization of energy storage efficiency as the goal, dynamically plan all the energy storage changes to determine the optimal path;

[0319] Determine the optimal mechanical - hydraulic energy storage ratio at each time point according to the optimal path;

[0320] Dynamically adjust the mechanical - hydraulic energy storage ratio of the spring energy storage system according to the optimal mechanical - hydraulic energy storage ratio.

[0321] Optionally, the system further includes:

[0322] Obtain the reflected wave signal for the spring in the GIS circuit breaker;

[0323] Conduct waveform analysis on the reflected wave signal and extract waveform feature vectors;

[0324] Calculate the Euclidean distance between the waveform feature vector and the standard waveform feature vector in the preset standard feature library;

[0325] When the Euclidean distance is greater than the preset distance threshold, determine that there is micro - damage in the spring.

[0326] Optionally, the waveform feature vector includes amplitude, frequency, and phase; the system further includes:

[0327] Determine the amplitude attenuation rate according to the ratio of the amplitude to the initial amplitude;

[0328] Determine the frequency offset according to the difference between the frequency and the initial frequency;

[0329] Determine the phase difference according to the difference between the phase and the initial phase;

[0330] Based on the amplitude attenuation rate, the frequency offset, and the phase difference, determine the total health score of the spring.

[0331] Optionally, the system further includes:

[0332] Obtain the original strain signal for the spring in the GIS circuit breaker;

[0333] Conduct time - frequency analysis on the original strain signal and extract time - domain features and frequency - domain features;

[0334] Input the time - domain features and the frequency - domain features into a preset strain signal feature - elastic modulus model to obtain an estimated value of the elastic modulus;

[0335] Determine the degree of reduction in the elastic modulus corresponding to the estimated value of the elastic modulus;

[0336] When the degree of reduction in the elastic modulus exceeds the preset reduction degree threshold, determine that there is an abnormality in the spring.

[0337] The further function descriptions of the above - mentioned various modules and units are the same as those in the corresponding above - mentioned embodiments, and will not be elaborated here.

[0338] The spring energy storage detection system of a GIS circuit breaker in this embodiment is presented in the form of a functional unit. Here, the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and a memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0339] Please refer to Figure 13 , Figure 13 which is a schematic structural diagram of a computer device provided by an embodiment of the present application. As Figure 13 shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including a high-speed interface and a low-speed interface. Each component communicates with each other using different buses and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed within the computer device, including instructions stored in the memory or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some alternative embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (such as an array of servers, a set of blade servers, or a multi-processor system). Figure 13 In

[0340] which, a processor 10 is taken as an example.

[0341] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0342] The memory 20 may include a program storage area and a data storage area. The program storage area may store an operating system and application programs required for at least one function. The data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some alternative embodiments, the memory 20 may optionally include a memory remotely disposed relative to the processor 10, and these remote memories may be connected to the computer device through a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0343] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, a hard disk, or a solid-state drive; the memory 20 may further include a combination of the above types of memory.

[0344] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or communication networks.

[0345] The embodiments of the present application also provide a computer-readable storage medium. The methods according to the embodiments of the present application may be implemented in hardware, firmware, or may be implemented as computer code recorded on a storage medium, or may be implemented as computer code originally stored in a remote storage medium or a non-transitory machine-readable storage medium and downloaded through a network and to be stored in a local storage medium, so that the methods described herein may be stored in such software processes on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium may be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid-state drive, etc.; further, the storage medium may also include a combination of the above types of memory. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component capable of storing or receiving software or computer code, and when the software or computer code is accessed and executed by the computer, the processor, or the hardware, the methods shown in the above embodiments are implemented.

[0346] The systems, modules, or units illustrated in the above embodiments may be specifically implemented by a computer chip or an entity, or by a product having a certain function. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0347] For the convenience of description, when describing the above device, various units are described separately according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in one or more software and / or hardware.

[0348] Those skilled in the art should understand that the embodiments of the present application can be provided as methods and systems. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0349] The present application is described with reference to the flowcharts and / or block diagrams of methods and devices (systems) according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or the combination of blocks.

[0350] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device that implements the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or the combination of blocks.

[0351] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are performed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or the combination of blocks.

[0352] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, commodity or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent in such process, method, commodity or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, commodity or device comprising said element.

[0353] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the differences between each embodiment and other embodiments are emphasized. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can refer to the description of the method embodiment.

[0354] The above are only the embodiments of the present application and are not used to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

[0355] Although the embodiments of the present application are described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present application, and such modifications and variations fall within the scope defined by the appended claims.

Claims

1. A method for detecting the spring energy storage of a GIS circuit breaker, characterized in that, The method includes: Based on the grating reflection wavelengths obtained from multiple measurement points on the outer surface of the GIS circuit breaker, determining the stress concentration coefficient and the material structure change index corresponding to the internal spring of the GIS circuit breaker, where the material structure change index characterizes the grain size change rate; Predicting the remaining life of the spring based on the stress concentration coefficient and the material structure change index, and determining the predicted remaining life of the spring; When the predicted remaining life is lower than a preset remaining life threshold, determining the real-time energy storage density of the spring energy storage system in the GIS circuit breaker; Determining the predicted energy storage demand corresponding to the real-time energy storage density, and dynamically adjusting the mechanical-hydraulic energy storage ratio of the spring energy storage system according to the predicted energy storage demand; Wherein, the determining the predicted energy storage demand corresponding to the real-time energy storage density and dynamically adjusting the mechanical-hydraulic energy storage ratio of the spring energy storage system according to the predicted energy storage demand includes: According to the real-time energy storage density, using a preset long short-term memory network to predict the energy storage demand in a future time period, and obtaining the predicted energy storage demand corresponding to each time point in the future time period; For the predicted energy storage demand corresponding to any time point, calculating the energy storage change from the current state to the next state; Taking the maximization of energy storage efficiency as the goal, dynamically programming all the energy storage changes to determine the optimal path; According to the optimal path, determining the optimal mechanical-hydraulic energy storage ratio at each time point; Dynamically adjusting the mechanical-hydraulic energy storage ratio of the spring energy storage system according to the optimal mechanical-hydraulic energy storage ratio.

2. The method according to claim 1, wherein The determining the stress concentration coefficient and the material structure change index corresponding to the internal spring of the GIS circuit breaker based on the grating reflection wavelengths obtained from multiple measurement points on the outer surface of the GIS circuit breaker includes: According to the grating reflection wavelengths, using the Bragg wavelength drift principle to calculate the strain value and the maximum strain value of each measurement point on the outer surface of the GIS circuit breaker; Processing the strain values using a spatial interpolation algorithm to generate a continuous strain distribution field; Calculating the gradient of each spatial point in the strain distribution field to obtain the gradient value and the maximum gradient value of each spatial point; Determining the stress concentration area as the spatial points corresponding to the gradient values greater than a preset gradient threshold; According to the maximum strain value, the maximum gradient value and the area of the stress concentration area, determining the stress concentration coefficient and the material structure change index through a preset strain distribution-stress model.

3. The method according to claim 1, characterized in that, The predicting the remaining life of the spring based on the stress concentration coefficient and the material structure change index and determining the predicted remaining life of the spring includes: Obtaining an image of the spring and extracting the microscopic damage features of the image, where the microscopic damage features characterize the damage and defects of the spring; Classifying the microscopic damage features to determine different damage levels; Determining the stress accumulation factor and the fatigue index corresponding to the spring; Input the stress concentration factor, the material structure change index, the damage level, the stress accumulation factor, and the fatigue index into a preset random forest tree algorithm model to obtain the predicted remaining life of the spring.

4. The method according to claim 1, wherein When the predicted remaining life is lower than a preset remaining life threshold, determining the real-time energy storage density of the spring energy storage system in the GIS circuit breaker includes: When the predicted remaining life is lower than a preset remaining life threshold, constructing an energy conversion model between the spring and the hydraulic cylinder in the spring energy storage system; Calculating energy conversion data during the energy storage process according to the energy conversion model; Determining the real-time energy storage density according to the energy conversion data and the total volume of the hydraulic cylinder.

5. The method according to claim 4, characterized in that Constructing the energy conversion model between the spring and the hydraulic cylinder in the spring energy storage system includes: Obtaining the spring compression displacement, the force exerted on the spring, the hydraulic cylinder pressure, and the total volume of the hydraulic cylinder; Establishing a force-displacement relationship function of the spring according to the force and the spring compression displacement; Establishing a pressure-volume relationship function of the hydraulic cylinder according to the hydraulic cylinder pressure and the total volume; Combining the force-displacement relationship function and the pressure-volume relationship function to obtain the energy conversion model.

6. The method according to claim 1, characterized in that The method further includes: Obtaining a reflected wave signal for the spring in the GIS circuit breaker; Performing waveform analysis on the reflected wave signal and extracting waveform feature vectors; Calculating the Euclidean distance between the waveform feature vector and the standard waveform feature vector in a preset standard feature library; When the Euclidean distance is greater than a preset distance threshold, determining that there is microscopic damage to the spring.

7. The method according to claim 6, characterized in that, The waveform feature vector includes amplitude, frequency, and phase; the method further includes: Determining the amplitude attenuation rate according to the ratio of the amplitude to the initial amplitude; Determining the frequency offset according to the difference between the frequency and the initial frequency; Determining the phase difference according to the difference between the phase and the initial phase; Based on the amplitude attenuation rate, the frequency offset, and the phase difference, determining the total health score corresponding to the spring.

8. The method according to claim 1, wherein The method further includes: Obtaining the original strain signal for the spring in the GIS circuit breaker; Performing time-frequency analysis on the original strain signal and extracting time-domain features and frequency-domain features; Inputting the time-domain features and the frequency-domain features into a preset strain signal feature - elastic modulus model to obtain an estimated value of the elastic modulus; Determining the degree of reduction in the elastic modulus corresponding to the estimated value of the elastic modulus; When the degree of reduction in the elastic modulus exceeds a preset reduction degree threshold, determining that there is an abnormality in the spring.

9. A spring energy storage detection system for a GIS circuit breaker, characterized in that, The system includes: A concentration coefficient and change index determination module for determining the stress concentration factor and the material structure change index corresponding to the spring inside the GIS circuit breaker based on the grating reflection wavelengths obtained from multiple measurement points on the outer surface of the GIS circuit breaker, where the material structure change index characterizes the grain size change rate; A predicted remaining life module for predicting the remaining life of the spring based on the stress concentration factor and the material structure change index and determining the predicted remaining life of the spring; A determination of energy storage density module, which is used to determine the real-time energy storage density of the spring energy storage system in the GIS circuit breaker when the predicted remaining life is lower than a preset remaining life threshold; A dynamic adjustment module, which is used to determine the predicted energy storage demand corresponding to the real-time energy storage density and dynamically adjust the mechanical-hydraulic energy storage ratio of the spring energy storage system according to the predicted energy storage demand; Wherein, the determination of the predicted energy storage demand corresponding to the real-time energy storage density and the dynamic adjustment of the mechanical-hydraulic energy storage ratio of the spring energy storage system according to the predicted energy storage demand includes: According to the real-time energy storage density, using a preset long short-term memory network to predict the energy storage demand in a future time period, and obtaining the predicted energy storage demand corresponding to each time point in the future time period; For the predicted energy storage demand corresponding to any time point, calculate the energy storage change from the current state to the next state; With the goal of maximizing energy storage efficiency, dynamically plan all the energy storage changes to determine the optimal path; According to the optimal path, determine the optimal mechanical-hydraulic energy storage ratio for each time point; Dynamically adjust the mechanical-hydraulic energy storage ratio of the spring energy storage system according to the optimal mechanical-hydraulic energy storage ratio.

10. A computer device, characterized in that, Including: A memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to execute the spring energy storage detection method of the GIS circuit breaker according to any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that, Computer instructions are stored on the computer-readable storage medium, and the computer instructions are used to cause a computer to execute the spring energy storage detection method of the GIS circuit breaker according to any one of claims 1 to 8.

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