A digital twin assessment method for measuring the wear condition of switch contacts

Abstract

This invention discloses a digital twin assessment method for measuring the wear state of switch contacts, comprising: acquiring current waveform data during the opening and closing process of the switch as an input source; identifying and quantifying key feature parameters from the preprocessed waveform data; based on the fatigue characteristics of the contact material, considering operating stress cycles, quantifying damage increments, and establishing a material fatigue cumulative damage model; inputting the key feature parameters extracted from the waveform data and the material fatigue cumulative damage model into a digital twin framework to create a virtual mirror image of the contact, simulating the wear process of the physical contact, and iteratively predicting future wear trends; based on the digital twin simulation results, using the cumulative damage threshold method for life prediction; verifying the accuracy of the model through historical data, and feeding back deviations to optimize model parameters. This invention can construct a digital twin model of contact wear, achieving accurate prediction of the remaining mechanical life of the contacts.

Description

Technical Field

[0001] This invention relates to the field of measurement switch contact evaluation technology, and more specifically to a digital twin evaluation method for the wear condition of measurement switch contacts. Background Technology

[0002] In power metering systems, measuring switches are key equipment in the power system and are generally responsible for the metering and distribution of electrical energy. They are core components that ensure the reliability of power supply. Their contacts, as key contact parts, directly bear the arc impact, mechanical stress and thermal effects during the opening and closing operations.

[0003] As the number of operations increases, the contact surface will gradually wear, oxidize or deform, leading to increased contact resistance and prolonged arcing time, which may eventually cause equipment failure or even explosion. Traditional contact wear assessment methods mainly include offline disassembly and inspection, resistance measurement and empirical life estimation. Although these methods can provide some reference, they have many limitations: (1) Offline disassembly and inspection requires power outage operation, which affects the continuity of the system; (2) Resistance measurement only reflects the static state and cannot capture the dynamic wear process; (3) Empirical estimation relies on the operator's subjective judgment and ignores the complex influence of environmental factors such as humidity, temperature and load changes.

[0004] Therefore, with the rapid development of smart grid technology, the demand for condition monitoring of measurement and switching equipment is becoming increasingly urgent, and a real-time, non-contact assessment technology is urgently needed.

[0005] Digital twin technology, as a core technology tool in the development of modern industry, has shown its potential in mechanical fault diagnosis by mapping physical entities through virtual models. However, existing digital twin applications are mostly limited to fatigue prediction of structural components, and there are few dedicated models for wear of electrical contacts. At the same time, the switching current waveform contains rich wear information, such as arc duration and current slope changes, but these have not been fully explored.

[0006] To address this issue, this application proposes a digital twin assessment method for measuring the wear state of switch contacts by integrating current waveform feature extraction, material fatigue modeling, and real-time operation data. This method provides a non-intrusive dynamic monitoring and prediction solution for contact wear problems in high-voltage or medium-voltage switchgear, enabling dynamic quantification and life prediction of contact wear, thereby improving the level of preventive maintenance of power equipment and solving the aforementioned technical problems. Summary of the Invention

[0007] The main objective of this invention is to provide a digital twin assessment method for measuring the wear state of switch contacts. By real-time acquisition of current waveform characteristics (such as arcing time and current change rate) during the switching process, combined with material fatigue accumulation model and operation frequency data, a digital twin model of contact wear is constructed to achieve accurate prediction of the remaining mechanical life of the contacts. This solves the technical problems mentioned in the background art, improves the level of intelligent equipment maintenance, and is applicable to fields such as smart grids and industrial automation. It can significantly reduce maintenance costs and improve system availability.

[0008] The present invention solves the above-mentioned technical problems by adopting the following technical solutions:

[0009] A digital twin assessment method for measuring the wear condition of switch contacts includes the following steps:

[0010] S1. Data Acquisition and Preprocessing: The current waveform signal of the opening and closing operation is captured in real time by the current sensor installed on the switchgear. The current waveform data of the opening and closing process of the switchgear is used as the input source, and the signal is preprocessed, including filtering and normalization, to eliminate noise interference and ensure data quality.

[0011] S2. Feature Extraction: Identify and quantify key feature parameters, including arcing time and current change rate, from the preprocessed waveform data;

[0012] S3. Model integration of material fatigue characteristics: Based on the fatigue characteristics of contact materials (such as stress-life curves of silver alloys or copper alloys), considering the operating stress cycle, each opening and closing of the circuit is regarded as a fatigue cycle, the damage increment is quantified, and a material fatigue cumulative damage model is established.

[0013] S4. Digital Twin Model Construction and Simulation: The key feature parameters extracted from the waveform data and the material fatigue cumulative damage model are input into the digital twin framework to create a virtual mirror of the contact. By combining the material fatigue cumulative model and the real-time operation frequency of the equipment, a digital twin model is constructed and dynamically updated to simulate the wear process of the physical contact, including microscopic changes on the surface and macroscopic life decay. Through iterative simulation, the wear trend under future operation is predicted to simulate the wear evolution process of the contact and facilitate the output of the remaining mechanical life prediction value.

[0014] S5. Remaining life prediction and output: Based on the results of digital twin simulation, calculate the remaining number of operations or time from the current state to failure of the contact, use the cumulative damage threshold method to predict the life, and issue an early warning when the damage index is close to the critical value.

[0015] S6. Verification and Feedback: Verify the accuracy of the model through historical data backtracking or experimental simulation, and feed back the deviations to optimize the model parameters to form a closed loop.

[0016] Preferably, in step S1, a current transformer with a sampling frequency of not less than 10kHz is installed on the switching circuit to collect current waveform data during the switching process in real time. After the current waveform data is collected, preprocessing operations including wavelet denoising and Z-fraction normalization are performed to remove environmental noise and amplitude differences. The calculation formula for wavelet denoising is as follows:

[0017]

[0018] in, For the corresponding time after denoising The current waveform signal, For the corresponding time The original acquired current signal, For the first One noise component, This represents the total number of noise components.

[0019] The formula for calculating the Z-score normalization is:

[0020]

[0021] in, For the normalized current waveform, The average value of the current signal. This represents the standard deviation of the current signal.

[0022] Preferably, the key feature parameters in step S2 are extracted in real time using an embedded processor, wherein:

[0023] The arcing time is obtained by detecting the start and end points of the non-zero current interval, and is as follows: ,in, For arc burning time, The moment the electric arc extinguishes. The moment when the electric arc is generated;

[0024] The rate of change of current is calculated using the finite difference method, and we have:

[0025]

[0026] in, Indicates the rate of change of current. It is a finite difference step size, usually the sampling interval.

[0027] Preferably, in step S3, the material fatigue cumulative damage model is established based on the Miner rule, where each operation is considered a stress cycle. The damage increment is dynamically adjusted by setting the operation frequency as a weight, resulting in the adjusted damage index:

[0028]

[0029] in, To adjust the damage index, This is the frequency influence coefficient. The cumulative damage index, with a value between 0 and 1. For the first Number of operations under stress-like conditions For the first Fatigue life under stress-like conditions For the number of stress categories, The weight is the frequency of operation, and its value ranges from 0 to 1. For real-time operation frequency, This is the maximum design frequency.

[0030] Preferably, in step S4, the virtual mirror of the contact point serves as a twin, used to map waveform features to the fatigue model, and iteratively calculates wear evolution through finite element simulation or a proxy model, wherein a waveform coupling expression exists:

[0031]

[0032] in, This represents a dynamically coupled waveform, which is a synthesized signal resulting from the superposition of environmental parameters. Indicates a standard abnormal waveform. Indicates the rate of change of current. The current change rate coupling coefficient, The arcing time coupling coefficient is... This refers to the arc ignition time.

[0033] Preferably, in step S4, the virtual mirror image of the contact is used as a twin, and its contact stress distribution expression is:

[0034]

[0035] in, For contact stress distribution, The elastic modulus of the material. For time-varying strain, Indicates time-based The creep kernel function, Represented as based on small time variables strain rate;

[0036] Based on the contact stress described above, the virtual mirror image of the contact predicts the simulated wear trend of the contact by calculating the single-cycle damage increment, resulting in a single-cycle damage increment. The calculation formula is:

[0037]

[0038] in, and These represent the maximum stress and the minimum stress, respectively. This represents the fatigue limit strength.

[0039] Preferably, in step S5, during the lifetime prediction process, the remaining number of operations is calculated using cumulative damage, wherein the remaining number of operations... As the remaining mechanical life, based on the total fatigue life The calculation has the following formula: ;

[0040] When the cumulative damage index D approaches 1, Monte Carlo simulation is used to consider uncertainties and provide early warning, indicating a probability of contact failure. The calculation formula is: ,in This is the failure rate parameter;

[0041] Based on total fatigue life Calculate the lifespan decay rate and correlate it with the contact failure probability. Total fatigue life The output is the lifetime prediction result, where the lifetime decay rate is... The calculation formula is: .

[0042] Preferably, for the digital twin simulation results in step S5, for the damage index, there is a warning condition for issuing an alert when it approaches a critical value:

[0043]

[0044] in, To predict the time window, The damage threshold (typically 0.9) is used to combine the cumulative damage model to quantify the safety margin, avoid over-reliance on a single damage index, and improve the reliability of the warning.

[0045] Preferably, the virtual mirror image of the contact point generated in step S4 is used as a twin model, and its model accuracy is evaluated by the root mean square error, which is as follows:

[0046]

[0047] in, The root mean square error, For the first Predicted lifespan. For actual lifespan, To verify the sample size.

[0048] Preferably, in step S6, the model parameter optimization process uses gradient descent based on RMSE to iteratively optimize key model parameters, including the frequency influence coefficient, to reduce prediction errors. The optimization formula is as follows:

[0049]

[0050] in, These are the updated coefficients used for parameter iterative optimization. This represents the learning rate, which is usually a preset value.

[0051] Preferably, the virtual mirror image of the contact point generated in step S4 is used as a twin model, and the model uses a material creep kernel function to evaluate its material deformation characteristics under long-term stress, as follows:

[0052]

[0053] in, This is an approximation of the creep kernel function. and All of these are fitting parameters (calibrated using experimental data). The number of terms (used to control the approximate precision).

[0054] Preferably, based on the virtual mirror image of the contact point generated in step S4 as a twin model, the dynamic change law of the contact point material strain over time is as follows, which is used to obtain the optimal parameter optimization result:

[0055]

[0056] in, Represents time-varying strain. Indicates the initial dependent variable. Indicates the harmonic amplitude (used to reflect the intensity of strain fluctuations). This indicates the frequency of characteristics such as the corresponding operation cycle. This represents the harmonic order (used to control the fitting accuracy).

[0057] In another aspect, the present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method described above.

[0058] In another aspect, the present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method described above.

[0059] As can be seen from the above technical solution, the present invention provides a digital twin evaluation method for measuring the wear state of switch contacts. Compared with the prior art, the present invention has the following advantages:

[0060] 1. This invention collects the current waveform characteristics during the opening and closing of switches in real time, combines them with material fatigue accumulation models and operation frequency data, and constructs a digital twin model of contact wear. This enables accurate prediction of the remaining mechanical life of the contacts, solves the problems of passivity and subjectivity in traditional assessment, improves the level of intelligent equipment maintenance, and is applicable to fields such as smart grids and industrial automation. It can significantly reduce maintenance costs and improve system availability.

[0061] 2. This invention introduces real-time operation frequency as a dynamic weighting factor by integrating a material fatigue model. Through real-time statistics of operation frequency, the model can adaptively adjust damage calculation, avoiding the bias of static models, thereby improving the adaptability of prediction, and is especially suitable for scenarios with large load fluctuations.

[0062] 3. In the process of constructing and simulating digital twin models, this invention uses the dynamic characteristics of current as a bridging variable to achieve multi-physics coupling simulation of electromechanical and thermal fields, which makes the model more consistent with the actual working conditions of electrical contacts, thereby improving the simulation accuracy.

[0063] It should be understood that the descriptions in this section are not intended to identify key or essential features of embodiments of the invention, nor are they intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Of course, implementing any product of the invention does not necessarily require achieving all of the advantages described above simultaneously. Attached Figure Description

[0064] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0065] Figure 1 This is an overall perspective view of the present invention;

[0066] Figure 2 This is a schematic diagram of the data processing flow of the present invention;

[0067] Figure 3 This is a line graph showing the comparison between the number of operations and the prediction error during the simulation test of this invention. Detailed Implementation

[0068] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0069] For details in the embodiments, please refer to Figures 1 to 3 .

[0070] like Figure 1 and Figure 2 As shown in the figure, the digital twin evaluation method for measuring the wear state of switch contacts proposed in this embodiment of the invention includes the following steps:

[0071] S1. Data Acquisition and Preprocessing: By setting a current transformer with a sampling frequency of not less than 10kHz on the switch circuit, the current waveform signal of the opening and closing operation is captured in real time. The current waveform data of the switch opening and closing process is used as the input source, and the signal is preprocessed, including wavelet denoising and Z-fraction normalization, to eliminate environmental noise interference and amplitude differences, and ensure data quality.

[0072] This step is used to ensure the purity of the input data, avoid bias in subsequent analysis, and improve the robustness of the overall evaluation.

[0073] The formula for wavelet denoising is as follows:

[0074]

[0075] in, For the corresponding time after denoising The current waveform signal, For the corresponding time The original acquired current signal, For the first One noise component, This represents the total number of noise components.

[0076] The formula for calculating the Z-score normalization is:

[0077]

[0078] in, For the normalized current waveform, The average value of the current signal. This represents the standard deviation of the current signal.

[0079] S2. Feature Extraction: Using an embedded processor, key feature parameters, including arc duration (the duration of the arc from its inception to its extinction) and current change rate (di / dt, representing the instantaneous current slope, reflecting the transient state of contact), are identified and extracted in real-time from the preprocessed waveform data. These features are directly related to the contact surface roughness and material loss.

[0080] The arcing time is obtained by detecting the start and end points of the non-zero current interval, and is as follows: ,in, For arc burning time, The moment the electric arc extinguishes. The moment when the electric arc is generated;

[0081] The rate of change of current is calculated using the finite difference method, and we have:

[0082]

[0083] in, Indicates the rate of change of current. It is a finite difference step size, usually the sampling interval.

[0084] The aforementioned key characteristic parameters can capture the transient response of the contact and reflect the degree of wear.

[0085] S3. Model integration of material fatigue characteristics: Based on the fatigue characteristics of contact materials (such as stress-life curves of silver alloys or copper alloys), considering the operation stress cycle, each opening and closing is regarded as a fatigue cycle, the damage increment is quantified, and a material fatigue cumulative damage model is established based on the Miner rule, where each operation is regarded as a stress cycle.

[0086] Specifically, the damage increment is dynamically adjusted by setting the operation frequency as a weight, and the adjusted damage index is:

[0087]

[0088] in, To adjust the damage index, This is the frequency influence coefficient. The cumulative damage index, with a value between 0 and 1. For the first Number of operations under stress-like conditions For the first Fatigue life under stress-like conditions For the number of stress categories, The weight is the frequency of operation, and its value ranges from 0 to 1. For real-time operation frequency, This is the maximum design frequency.

[0089] In this process of introducing real-time operation frequency as a dynamic weighting factor, high-frequency operations will accelerate fatigue accumulation, while low-frequency operations will be relatively mild. Therefore, by using real-time statistics of operation frequency (such as the number of daily circuit breakers), the model can adaptively adjust the damage calculation, avoid the bias of the static model, and thus improve the adaptability of the prediction, which is especially suitable for scenarios with large load fluctuations.

[0090] It can be further explained that the above model parameters can be calibrated through material testing.

[0091] S4. Digital Twin Model Construction and Simulation: Key feature parameters extracted from waveform data and material fatigue cumulative damage model are input into the digital twin framework to create a virtual mirror of the contact. By combining the material fatigue cumulative model and the real-time operation frequency of the equipment, a digital twin model is constructed and dynamically updated to simulate the wear process of the physical contact, including microscopic changes on the surface and macroscopic life decay. Through iterative simulation, the wear trend under future operation is predicted to simulate the wear evolution process of the contact and facilitate the output of the remaining mechanical life prediction value.

[0092] This step is used to implement virtual-physical mapping, which can run on a cloud platform and supports remote monitoring. It is used to improve simulation accuracy through the coupling of current characteristics.

[0093] Furthermore, the virtual mirror image of the contact point here serves as a twin, used to map waveform features to the fatigue model, and iteratively calculates wear evolution through finite element simulation or a proxy model, where a waveform coupling expression exists:

[0094]

[0095] in, This represents a dynamically coupled waveform, which is a synthesized signal resulting from the superposition of environmental parameters. It represents a standard abnormal waveform (such as a reference waveform for voltage sag, harmonic distortion, etc.). Indicates the rate of change of current. The current change rate coupling coefficient, The arcing time coupling coefficient is... This refers to the arc burning time;

[0096] The expression for the stress distribution at the contact point here is:

[0097]

[0098] in, For contact stress distribution, The elastic modulus of the material. For time-varying strain, Indicates time-based The creep kernel function, Represented as based on small time variables strain rate;

[0099] Based on the contact stress described above, the virtual mirror image of the contact predicts the simulated wear trend of the contact by calculating the single-cycle damage increment, resulting in a single-cycle damage increment. The calculation formula is:

[0100]

[0101] in, and These represent the maximum stress and the minimum stress, respectively. This represents the fatigue limit strength.

[0102] It should be noted that, since traditional digital twins rely heavily on geometric parameters, the method in this application uses the dynamic characteristics of the current as a bridging variable to achieve multi-physics coupling simulation of electromechanical and thermal fields. This makes the model more closely resemble the actual working conditions of electrical contacts and improves the simulation accuracy.

[0103] S5. Remaining Life Prediction and Output: Based on the results of digital twin simulation, calculate the remaining number of operations or time from the current state to failure of the contact. Use the cumulative damage threshold method to predict the lifespan. When the damage index is close to the critical value, issue an early warning.

[0104] This step is used to provide decision support, dynamically update to ensure real-time performance, reduce false alarms, and the specific output can be integrated into the SCADA system.

[0105] It should be further noted that the remaining number of operations is calculated using cumulative damage during the lifetime prediction process. As the remaining mechanical life, based on the total fatigue life The calculation has the following formula: ;

[0106] When the cumulative damage index D approaches 1, Monte Carlo simulation is used to consider uncertainties and provide early warning, indicating a probability of contact failure. The calculation formula is: ,in This is the failure rate parameter;

[0107] Based on total fatigue life Calculate the lifespan decay rate and correlate it with the contact failure probability. Total fatigue life The output is the lifetime prediction result, where the lifetime decay rate is... The calculation formula is: .

[0108] It can be further explained at this point that, based on the digital twin simulation results in step S5, for the damage index, there are conditions for issuing an early warning when it approaches a critical value:

[0109]

[0110] in, To predict the time window, The damage threshold (typically 0.9) is used to combine the cumulative damage model to quantify the safety margin, avoid over-reliance on a single damage index, and improve the reliability of the warning.

[0111] Furthermore, since the model immediately reconstructs the twin whenever new operational data is input, it can ensure the real-time nature of the prediction. In one specific embodiment, this method can reduce the error rate by more than 20% compared to static prediction.

[0112] S6. Verification and Feedback: Verify the accuracy of the model by backtracking through historical data or experimental simulation, and feed the deviation back to optimize the model parameters to form a closed loop. At this time, the closed loop mechanism improves the model's adaptability and ensures long-term reliability. The verification data comes from historical logs or accelerated experiments.

[0113] It should be further explained at this point that, since the virtual mirror image of the contact point generated in step S4 is used as a twin model, its model accuracy can be evaluated by the root mean square error, as follows:

[0114]

[0115] in, The root mean square error, For the first Predicted lifespan. For actual lifespan, To verify the sample size.

[0116] Since the virtual mirror image of the contact point generated in step S4 is used as a twin model, its model uses a material creep kernel function to evaluate its material deformation characteristics under long-term stress, as follows:

[0117]

[0118] in, This is an approximation of the creep kernel function. and All of these are fitting parameters (calibrated using experimental data). The number of terms (used to control the approximation precision);

[0119] The following dynamic variation law of the contact material strain over time is also presented, which is used to obtain the optimal parameter optimization results:

[0120]

[0121] in, Represents time-varying strain. Indicates the initial dependent variable. Indicates the harmonic amplitude (used to reflect the intensity of strain fluctuations). This indicates the frequency of characteristics such as the corresponding operation cycle. This represents the harmonic order (used to control the fitting accuracy).

[0122] Therefore, based on the above model data, the model parameter optimization process also utilizes the gradient descent method based on RMSE to iteratively optimize key model parameters, including the frequency influence coefficient, to reduce prediction errors. The optimization formula is as follows:

[0123]

[0124] in, These are the updated coefficients used for parameter iterative optimization. This represents the learning rate, which is usually a preset value.

[0125] In summary, this process emphasizes the integration of data-driven approaches and physical models to ensure the accuracy and real-time nature of the assessment, avoiding the static limitations of traditional methods. Furthermore, the process as a whole realizes an intelligent chain from data to decision-making, applicable to various switch types such as circuit breakers or contactors. Therefore, through the above steps, this application can cleverly integrate current waveform analysis and fatigue modeling to solve the problem of contact wear assessment in the prior art, providing a new path for intelligent maintenance of power equipment.

[0126] Therefore, this method, by collecting the current waveform characteristics during the opening and closing of the switch in real time, and combining the material fatigue accumulation model and operation frequency data, constructs a digital twin model of contact wear, which can accurately predict the remaining mechanical life of the contacts. It solves the problems of passivity and subjectivity in traditional assessment, improves the level of intelligent equipment maintenance, and is applicable to fields such as smart grids and industrial automation. It can significantly reduce maintenance costs and improve system availability.

[0127] Furthermore, to further verify the technical effectiveness of the digital twin evaluation method of the present invention, a detailed comparative test was conducted with the traditional empirical lifetime estimation method. In further comparative experimental examples, the following were included:

[0128] Implementation conditions: On a high-voltage circuit breaker platform simulated in a professional laboratory, a silver alloy contact switch with a rated voltage of 10kV was selected. The initial state was that the equipment was brand new. The test was divided into two groups: The experimental group used the method of this invention to collect the opening and closing current waveforms in real time (sampling rate 20kHz), extract the arcing time (t_arc) and current change rate (di / dt), and combine the Miner fatigue model and real-time operation frequency (simulating 50 operations per day) to dynamically construct a digital twin model and predict the remaining life; The control group used the traditional method, relying only on the fixed empirical life curve (total number of operations 10,000) and cumulative operation count provided by the manufacturer for static estimation.

[0129] Test Procedure: An accelerated fatigue testing platform was used, applying a standard load (peak current 5kA). After every 100 cycles, changes in contact resistance, surface image scans, and actual wear parameters were recorded as ground truth. The test continued until the contact resistance increased by more than 20%, considered a failure point (actual failure occurred after approximately 10,000 cycles). Key data collected included predicted remaining life and percentage of prediction error. The test includes parameters such as the number of early warning operations and response time calculations. Simultaneously, random noise is introduced to simulate real-world environmental disturbances (e.g., load fluctuations of ±10%) to ensure robustness. The experiment is repeated three times, and the average value is taken to reduce the impact of randomness.

[0130] Expected Results: The method of this invention has an average prediction error of <5% over its entire lifecycle, provides early warnings at least 500 operations in advance, and has a computation time of <1 second per prediction. Traditional methods have an average error of >15%, lack dynamic early warning functionality, and rely solely on static thresholds, resulting in delayed responses. In the high-frequency operation phase (>7000 operations), this method improves accuracy by approximately 25%-30%, significantly reducing false alarm rates and the risk of sudden failures, demonstrating its superiority and practical application value in complex power systems.

[0131] Comparison of Actual Data Results: The table below, based on simulated test data (sampled every 100 operations), compares the actual remaining lifespan, the predicted value of this invention, the predicted value of the traditional method, and their respective percentage errors. The data reflects the higher accuracy of this method in the later stages of wear (>5000 operations).

[0132]

[0133] Based on the data in the table above, combined with Figure 3 Therefore, we can conclude that:

[0134] (1) The prediction accuracy of this invention is significantly better than that of traditional methods, with an average error reduction of 93.7%.

[0135] (2) Traditional methods suffer from a sharp deterioration in error with a high number of operations (up to 324.87%), while the present invention maintains an error of less than 20%.

[0136] (3) The present invention exhibits stable predictive performance throughout the entire life cycle of the equipment, verifying the robustness of the method.

[0137] Based on the above actual test comparison results, the technical advantages of the present invention in life prediction can be fully demonstrated, especially the significant improvement in reliability under high usage intensity scenarios.

[0138] In another aspect, the present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method described above.

[0139] In another aspect, the present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method described above.

[0140] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute any of the digital twin evaluation methods for measuring the wear state of switch contacts in the above embodiments.

[0141] It is understood that the system provided in the embodiments of the present invention corresponds to the method provided in the embodiments of the present invention, and the explanation, examples and beneficial effects of the relevant content can be referred to the corresponding parts of the above methods.

[0142] This application also provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, communication interface, and memory communicate with each other via the communication bus.

[0143] Memory, used to store computer programs;

[0144] When the processor executes the program stored in the memory, it implements the above-mentioned digital twin evaluation method for measuring the wear state of switch contacts.

[0145] The communication bus mentioned in the above-mentioned electronic devices can be a standard bus for interconnecting peripheral components or an extended industrial standard structure bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc.

[0146] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0147] The memory may include random access memory or non-volatile memory, such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0148] The processors mentioned above can be general-purpose processors, including central processing units, network processors, etc.; they can also be digital signal processors, application-specific integrated circuits, field-programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0149] It should also be noted that electronic devices include terminal devices, which can also be called terminals, user equipment, mobile stations, mobile terminals, etc. Terminal devices can be mobile phones, smart TVs, wearable devices, tablets, computers with wireless transceiver capabilities, virtual reality terminal devices, augmented reality terminal devices, wireless terminals in industrial control, wireless terminals in autonomous driving, wireless terminals in remote surgery, wireless terminals in smart grids, wireless terminals in transportation safety, wireless terminals in smart cities, wireless terminals in smart homes, and so on. The embodiments of this application do not limit the specific technologies or device forms used in the terminal devices.

[0150] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium, an optical medium, or a semiconductor medium, etc.

[0151] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0152] Furthermore, it should be noted that if any directional indication (such as up, down, left, right, front, back, etc.) is involved in the embodiments of the present invention, the directional indication is only used to explain the relative positional relationship and movement of each component in a specific posture. If the specific posture changes, the directional indication will also change accordingly.

[0153] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the meaning of "and / or" throughout the text includes three parallel solutions; for example, "A and / or B" includes solution A, solution B, or a solution where both A and B are satisfied simultaneously. Furthermore, in the embodiments of this invention, "multiple" refers to two or more. Moreover, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

Claims

1. A digital twin assessment method for measuring the wear condition of switch contacts, characterized in that, include: S1. Collect current waveform data during the opening and closing of the switch as the input source; S2. Identify and quantify key feature parameters, including arcing time and current change rate, from the preprocessed waveform data; S3. Based on the fatigue characteristics of the contact material, considering the operating stress cycle, each opening and closing of the circuit is regarded as a fatigue cycle, the damage increment is quantified, and a material fatigue cumulative damage model is established. S4. Input the key feature parameters extracted from the waveform data and the material fatigue cumulative damage model into the digital twin framework to create a virtual mirror of the contact point, which is used to simulate the wear process of the physical contact point, and predict the wear trend under future operation through iterative simulation. S5. Based on the results of digital twin simulation, the cumulative damage threshold method is used for lifetime prediction; S6. Verify the accuracy of the model through historical data backtracking or experimental simulation, and feed the deviation back to optimize the model parameters; In step S3, the material fatigue cumulative damage model is established based on the Miner rule, where each operation is considered a stress cycle. The damage increment is dynamically adjusted by setting the operation frequency as a weight, resulting in the adjusted damage index: in, To adjust the damage index, This is the frequency influence coefficient. The cumulative damage index, with a value between 0 and 1. For the first Number of operations under stress-like conditions For the first Fatigue life under stress-like conditions For the number of stress categories, The weight is the frequency of operation, and its value ranges from 0 to 1. For real-time operation frequency, This is the maximum design frequency.

2. The digital twin evaluation method for measuring the wear state of switch contacts as described in claim 1, characterized in that, In step S1, a current transformer is installed on the switching circuit to collect current waveform data during the switching process in real time. After the current waveform data is collected, preprocessing operations, including wavelet denoising, are performed. The calculation formula for wavelet denoising is as follows: in, For the corresponding time after denoising The current waveform signal, For the corresponding time The original acquired current signal, For the first One noise component, This represents the total number of noise components.

3. The digital twin evaluation method for measuring the wear state of switch contacts as described in claim 2, characterized in that, The key feature parameters of step S2 are extracted in real time using an embedded processor. Specifically: the arcing time is obtained by detecting the start and end points of the non-zero current interval; the current change rate is calculated using the finite difference method. ,in, Indicates the rate of change of current. The step size is finite difference.

4. The digital twin evaluation method for measuring the wear state of switch contacts as described in claim 1, characterized in that, In step S4, the virtual mirror of the contact point serves as a twin, used to map waveform features to the fatigue model. Wear evolution is then iteratively calculated using finite element simulation or a proxy model, where a waveform coupling expression exists: in, This represents a dynamically coupled waveform, which is a synthesized signal resulting from the superposition of environmental parameters. Indicates a standard abnormal waveform. Indicates the rate of change of current. The current change rate coupling coefficient, The arcing time coupling coefficient is... This refers to the arc ignition time.

5. The digital twin evaluation method for measuring the wear state of switch contacts as described in claim 4, characterized in that, In step S4, the virtual mirror image of the contact point is used as a twin, and its contact stress distribution expression is as follows: in, For contact stress distribution, The elastic modulus of the material. For time-varying strain, Indicates time-based The creep kernel function, Represented as based on small time variables strain rate; Based on the contact stress described above, the virtual mirror image of the contact predicts the simulated wear trend of the contact by calculating the single-cycle damage increment, resulting in a single-cycle damage increment. The calculation formula is: in, and These represent the maximum stress and the minimum stress, respectively. This represents the fatigue limit strength.

6. The digital twin evaluation method for measuring the wear state of switch contacts as described in claim 4, characterized in that, In step S5, during the lifetime prediction process, the remaining number of operations is calculated using cumulative damage, where the remaining number of operations... As the remaining mechanical life, based on the total fatigue life The calculation has the following formula: ; When the cumulative damage index D approaches 1, Monte Carlo simulation is used to consider uncertainties and provide early warning, indicating a probability of contact failure. The calculation formula is: ,in This is the failure rate parameter; Based on total fatigue life Calculate the lifespan decay rate and correlate it with the contact failure probability. Total fatigue life The output is the lifetime prediction result, where the lifetime decay rate is... The calculation formula is: .

7. The digital twin evaluation method for measuring the wear state of switch contacts as described in claim 5, characterized in that, The virtual mirror image of the contact point generated in step S4 is used as a twin model. The accuracy of the model is evaluated by the root mean square error, and we have: in, The root mean square error, For the first Predicted lifespan. For actual lifespan, To verify the sample size.

8. The digital twin evaluation method for measuring the wear state of switch contacts as described in claim 7, characterized in that, In step S6, the model parameter optimization process uses gradient descent based on RMSE to iteratively optimize key model parameters, including the frequency influence coefficient, to reduce prediction errors. The optimization formula is as follows: in, These are the updated coefficients used for parameter iterative optimization. This represents the learning rate.

9. The digital twin evaluation method for measuring the wear state of switch contacts as described in claim 7, characterized in that, Based on the virtual mirror image of the contact point generated in step S4 as a twin model, the dynamic change law of the contact point material strain over time is as follows, which is used to obtain the optimal parameter optimization result: in, Represents time-varying strain. Indicates the initial dependent variable. Indicates harmonic amplitude. Indicates frequency, It represents the harmonic order.

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