Intelligent power collection loop health management method and system based on digital twinning

Abstract

This invention discloses a digital twin-based intelligent slip ring health management method and system, relating to the field of electromechanical systems technology. The method includes: acquiring a twin model based on digital twins; acquiring twin simulation parameters and obtaining fault evolution links based on fault simulation results; using link analysis to obtain fault management methods; and performing health management on the intelligent slip ring based on the latest fault management methods corresponding to real-time management faults. This invention addresses the problem that existing intelligent slip ring health management methods cannot analyze fault chains, leading to an inability to effectively predict and manage potential hazards caused by faults. This results in only passive monitoring and post-event remediation of faults, without the ability to proactively predict faults, thus causing false alarms and missed alarms.

Description

Technical Field

[0001] This invention relates to the field of electromechanical systems technology, specifically to a method and system for intelligent collector ring health management based on digital twins. Background Technology

[0002] Intelligent slip rings are rotary electrical interfaces that integrate intelligent technologies such as sensing, signal processing, and IoT communication on the basis of traditional slip rings. The core is to achieve intelligent functions such as status monitoring, fault warning, and adaptive adjustment while stably transmitting power and signals between rotating and stationary components.

[0003] Existing methods for health management of smart slip rings typically focus on improving the monitoring of single faults within the smart slip ring. They aim to resolve detected faults and manage the health of the smart slip ring by analyzing monitoring results. While this approach offers more precise fault management, it only allows for independent assessment of single faults and cannot analyze the fault chain. This results in an inability to effectively predict and manage potential problems caused by faults, leading to passive monitoring and reactive remediation rather than proactive fault prediction. Consequently, this can cause false alarms and missed alarms, as illustrated in patent publication CN119987462A. The application disclosed a temperature control system for the collector ring of a synchronous condenser. This solution uses phase change materials to control the temperature of the collector ring and analyzes the power of the collector ring to predict future trends and control the temperature. Other methods for health management of intelligent collector rings usually improve the trigger threshold of management, but they still cannot analyze the fault chain caused by the fault. This makes it impossible to effectively predict and manage the hidden dangers caused by the fault, resulting in only passive monitoring and post-event remediation of faults, and the inability to actively predict faults. This leads to false alarms and missed alarms. In view of this, it is necessary to improve the existing intelligent collector ring health management methods. Summary of the Invention

[0004] This invention aims to at least partially solve one of the technical problems in the prior art by proposing a digital twin-based intelligent collector ring health management method and system. This addresses the shortcomings of existing intelligent collector ring health management methods, such as the inability to analyze fault chains caused by faults, which leads to the inability to effectively predict and manage potential hazards caused by faults. Consequently, these methods can only passively monitor and remedy faults after they occur, and cannot proactively predict faults, resulting in false alarms and missed alarms.

[0005] To achieve the above objectives, in a first aspect, this application provides a method for intelligent collector ring health management based on digital twins, comprising the following steps:

[0006] The digital twin model corresponding to the intelligent collector ring is obtained based on the digital twin and recorded as the twin model; the fault types of the intelligent collector ring and the twin simulation parameters corresponding to each fault type are obtained based on the operation log of the intelligent collector ring.

[0007] Based on the twin simulation parameters of all fault types, fault simulation is performed within the twin model, and the fault evolution link for each fault type is obtained based on the fault simulation results; the fault evolution link is analyzed using the link analysis method, and the fault management method for each fault type is obtained based on the analysis results.

[0008] Based on the operating cycle of the intelligent collector ring, the fault management methods for all fault types corresponding to the intelligent collector ring are updated; the fault types that are detected as abnormal within the intelligent collector ring are recorded as real-time management faults, and the intelligent collector ring is health managed based on the latest fault management methods corresponding to the real-time management faults.

[0009] Furthermore, a digital twin model corresponding to the intelligent slip ring is obtained based on the digital twin and denoted as the twin model; the fault types of the intelligent slip ring are obtained based on its operation logs, including:

[0010] Based on the geometric model, physical model, and digital thread of the smart slip ring, a digital twin model corresponding to the smart slip ring is constructed using digital twins and denoted as the twin model. The geometric model includes the dimensional data of all components used to build the smart slip ring, the physical model includes the equations for integrated electrical contact, heat conduction, mechanical vibration, wear, and fluid multi-field coupling, and the digital thread includes the access design BMO, manufacturing process parameters, and factory test data.

[0011] Based on the operation log of the intelligent collector ring, all fault types detected within the intelligent collector ring are obtained and denoted as fault type GL1 to fault type GL. t Where t is the total number of faults that the device monitoring the smart collector ring can detect within the smart collector ring, and the device that monitors the fault type is denoted as the fault monitoring device of the fault type;

[0012] For any fault type GL r : Obtain the fault type GL from the operation log of the smart slip ring. r The fault monitoring equipment for fault type GL r All monitoring records are recorded as parameter monitoring records, where r is a positive integer greater than or equal to 1 and less than or equal to t; the fault monitoring equipment is used to monitor fault type GL. r The criteria for determining the presence of an anomaly are recorded as the fault determination criteria, and the fault type GL is assigned. r The corresponding parameter unit is denoted as the fault unit;

[0013] Establish a Cartesian coordinate system, denoted as the parameter analysis coordinate system, where the units of the X-axis and Y-axis are time and fault units, respectively. Plot the parameter monitoring records within the parameter analysis coordinate system, where the fault type GL... r The corresponding parameter-time relationship curve is recorded and denoted as the parameter analysis curve.

[0014] Furthermore, based on the operation logs of the intelligent slip ring, the fault types of the intelligent slip ring and the corresponding twin simulation parameters for each fault type are obtained, including:

[0015] Based on the fault determination criteria, the fault type GL in the parameter monitoring records will be... r The curve corresponding to abnormal data in the parameter analysis curve is denoted as the parameter abnormality curve. The fault type GL in the parameter monitoring record is recorded. r The curve corresponding to data without anomalies in the parameter analysis curve is called the normal parameter curve. Multiple abnormal parameter curves and normal parameter curves are allowed to exist.

[0016] Fault type GL r All corresponding abnormal parameter curves and normal parameter curves are denoted as fault type GL. r Twin simulation parameters;

[0017] Obtain the twin simulation parameters corresponding to all fault types.

[0018] Furthermore, fault simulation includes single-fault simulation and multi-fault simulation. Single-fault simulation includes:

[0019] For any fault type GL r The corresponding abnormal curve α for any parameter: This represents the fault type GL. r The left-hand curves of all corresponding parameter normal curves are denoted as corrected operating curves XY1 to XY1, respectively. e Where e is the fault type GL r The corresponding number of normal curves for parameters; the left domain curves are the curves corresponding to the left half of the normal curves for parameters.

[0020] For any corrected running curve XY p Simulate a smart slip ring in standard operating condition within a twin model, and use fault monitoring equipment to detect fault type GL. r The monitoring records correspond to the curves in the parameter analysis coordinate system, denoted as the real-time simulation curves. The standard operating state is when none of the fault types are in an abnormal state, and p is a positive integer less than or equal to e and greater than or equal to 1.

[0021] Furthermore, single-fault simulation also includes:

[0022] Adjust the operating state of the intelligent collector ring in the twin model. When the rightmost region of the real-time simulation curve is exactly the same as the parameter anomaly curve α, mark the abscissa of the rightmost point of the real-time simulation curve as A1.

[0023] Continue adjusting the operating state of the intelligent collector ring within the twin model until the rightmost region of the real-time simulation curve matches the corrected operating curve XY. p When they are completely identical, the abscissa of the rightmost point of the real-time simulation curve is marked as A2, the difference between A2 and A1 is recorded as the abnormal correction time, and the adjustment process of all components in the smart collector ring between A1 and A2 is recorded as the abnormal correction process.

[0024] Obtain the abnormal correction time of all corrected running curves; denote the abnormal correction process corresponding to the corrected running curve with the smallest abnormal correction time as the first correction process of parameter abnormal curve α.

[0025] The first correction process obtains all abnormal parameter curves for all fault types.

[0026] Furthermore, multi-fault simulation includes:

[0027] For any fault type GL r For any one of the abnormal parameter curves α: Simulate the smart collector ring in standard operating condition within the twin model and obtain the corresponding real-time simulation curve;

[0028] At time B1, the operating state of the smart slip ring within the twin model is adjusted. When the rightmost region of the real-time simulation curve is exactly the same as the parameter anomaly curve α, the first correction process of the anomaly parameter curve α is used to adjust all components within the smart slip ring until the fault type GL is reached. r When not in an abnormal state, the x-coordinate of the rightmost point of the real-time simulation curve is marked as B2.

[0029] Furthermore, multi-fault simulation also includes:

[0030] Between time B1 and time B2, troubleshooting type GL within the intelligent collector ring. r Among the fault types other than those mentioned above, the fault types that are in an abnormal state are denoted as linkage abnormal types, and the time they are in an abnormal state is denoted from first to last as linkage abnormal type LY1 to linkage abnormal type LY. u ;

[0031] For any linkage anomaly type, the curve corresponding to the data when the linkage anomaly type is in an abnormal state in the parameter analysis coordinate system is denoted as the linkage anomaly curve; among all the abnormal parameter curves of the linkage anomaly type, the curve with the highest similarity to the linkage anomaly curve is denoted as the linkage evolution curve of the linkage anomaly type.

[0032] Linking exception type LY1 to linkage exception type LY u This is denoted as fault type GL. r The fault evolution link corresponding to the abnormal parameter curve α;

[0033] Obtain the anomaly evolution link corresponding to all fault types and each anomaly parameter curve.

[0034] Furthermore, link analysis includes:

[0035] For any fault type GL r The fault evolution link corresponding to the abnormal parameter curve α: linking the fault evolution link from the linked abnormal type LY1 to the linked abnormal type LY. u The linked evolution curves are denoted sequentially as linked evolution curve LQ1 to linked evolution curve LQ. u ;

[0036] Fault type GL r The fault management method corresponding to the abnormal parameter curve α is set as follows: execute the first correction process of the abnormal parameter curve α, and then sequentially execute the linkage evolution curves LQ1 to LQ. u The first revision process;

[0037] Get Fault Type GL r With fault type GL r The fault management methods corresponding to all abnormal parameter curves.

[0038] Furthermore, based on the operating cycle of the intelligent slip ring, the fault management methods for all fault types corresponding to the intelligent slip ring are updated; fault types that detect anomalies within the intelligent slip ring are recorded as real-time management faults, and based on the latest fault management methods corresponding to real-time management faults, health management of the intelligent slip ring is performed, including:

[0039] When the smart slip ring enters a new operating cycle, the fault management methods for all fault types corresponding to the smart slip ring are updated;

[0040] When a real-time management fault is detected, based on the monitoring records of the real-time management fault by the fault monitoring equipment, the curve corresponding to the abnormal real-time management fault in the parameter analysis coordinate system is obtained and recorded as the real-time fault curve.

[0041] Among all the abnormal parameter curves of real-time management faults, the curve with the highest similarity to the real-time fault curve is recorded as the first correction curve; the fault management method corresponding to the real-time management fault and the first correction curve is executed.

[0042] Secondly, this application also provides a digital twin-based intelligent collector ring health management system, including a fault type analysis module, a fault management analysis module, and a real-time fault management module;

[0043] The fault type analysis module is used to obtain the digital twin model corresponding to the smart collector ring based on the digital twin, and denot it as the twin model; and to obtain the fault type of the smart collector ring and the twin simulation parameters corresponding to each fault type based on the operation log of the smart collector ring.

[0044] The fault management analysis module is used to perform fault simulation within the twin model based on the twin simulation parameters of all fault types, and obtain the fault evolution link for each fault type based on the fault simulation results; it uses link analysis to analyze the fault evolution link, and obtains the fault management method for each fault type based on the analysis results;

[0045] The real-time fault management module is used to update the fault management methods for all fault types corresponding to the smart collector ring based on the operating cycle of the smart collector ring; it records the fault types that are detected as abnormal within the smart collector ring as real-time management faults, and performs health management on the smart collector ring based on the latest fault management methods corresponding to the real-time management faults.

[0046] The beneficial effects of this invention are as follows: First, this application obtains a twin model corresponding to the intelligent collector ring based on digital twins; it obtains the fault types of the intelligent collector ring and the twin simulation parameters corresponding to each fault type based on the operation log of the intelligent collector ring; then, based on the twin simulation parameters of all fault types, it performs fault simulation within the twin model, and obtains the fault evolution link of each fault type based on the fault simulation results. The advantage of this is that by obtaining the twin simulation parameters corresponding to each fault type, it is possible to ensure that in subsequent fault simulations, the state of each fault type when it is in an abnormal state can be analyzed through the twin model, thereby ensuring a more detailed differentiation analysis of all abnormal states of the fault type and obtaining the abnormal evolution link corresponding to each abnormal state. In this way, when a real-time management fault is obtained, the fault evolution link corresponding to the real-time management fault can be used to effectively and comprehensively manage the real-time management fault and the fault chain caused by the real-time management fault, so as to achieve proactive and effective prediction of the hidden dangers caused by the fault and to carry out targeted management, avoiding the problems of false alarms and missed alarms caused by passive monitoring.

[0047] This application also uses link analysis to analyze the fault evolution link and obtains a fault management method for each fault type based on the analysis results. Finally, based on the operating cycle of the smart collector ring, the fault management methods for all fault types corresponding to the smart collector ring are updated. Fault types that detect anomalies within the smart collector ring are recorded as real-time management faults, and the smart collector ring is health-managed based on the latest fault management method corresponding to the real-time management fault. The advantage of this is that by constructing a fault management method for each fault type, it is possible to directly perform health management on the smart collector ring using the fault management method corresponding to the real-time management fault after obtaining the real-time management fault. This enables targeted management of the fault chain caused by the real-time management fault, while improving management efficiency and avoiding other potential fault hazards. Attached Figure Description

[0048] Figure 1 This is a schematic diagram of the system of the present invention;

[0049] Figure 2 This is a flowchart illustrating the steps of the method of the present invention;

[0050] Figure 3 This is a schematic diagram of the abnormal parameter curves for the fault type "high brush wear" of the present invention.

[0051] Figure 4 This is a schematic diagram of the abnormal parameter curves for the fault type "excessive contact resistance" of the present invention.

[0052] Figure 5 This is a schematic diagram of the modified operating curve of the present invention;

[0053] Figure 6 This is a schematic diagram of the electronic device of the present invention. Detailed Implementation

[0054] 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 some embodiments of the present invention, and not all embodiments. 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.

[0055] Example 1, please refer to Figure 1 As shown, this application provides an intelligent collector ring health management system based on digital twins, including a fault type analysis module, a fault management analysis module, and a real-time fault management module;

[0056] The fault type analysis module is used to obtain the digital twin model corresponding to the smart collector ring based on the digital twin, and denot it as the twin model; and to obtain the fault type of the smart collector ring and the twin simulation parameters corresponding to each fault type based on the operation log of the smart collector ring.

[0057] The fault type analysis module includes a fault type analysis unit, which is configured with a fault type analysis strategy. The fault type analysis strategy includes:

[0058] Based on the geometric model, physical model, and digital thread of the smart slip ring, a digital twin model corresponding to the smart slip ring is constructed using digital twins and denoted as the twin model. The geometric model includes the dimensional data of all components used to build the smart slip ring, the physical model includes the equations for integrated electrical contact, heat conduction, mechanical vibration, wear, and fluid multi-field coupling, and the digital thread includes the access design BMO, manufacturing process parameters, and factory test data.

[0059] In the specific implementation process, the dimensional data may include the length, height and width data of the components. The dimensional data of all components can be obtained from the design drawings of the smart slip ring, so as to ensure that the constructed twin model is consistent with the smart slip ring. The physical model and digital thread can be specifically set according to the structured list of hardware, software, interfaces, protocols, cables and installation parts required when connecting the smart slip ring to the digital twin system, monitoring system or whole machine control system.

[0060] Based on the operation log of the intelligent collector ring, all fault types detected within the intelligent collector ring are obtained and denoted as fault type GL1 to fault type GL. t Where t is the total number of faults that the device monitoring the smart collector ring can detect within the smart collector ring, and the device that monitors the fault type is denoted as the fault monitoring device of the fault type.

[0061] The fault type analysis strategy also includes: for any fault type GL r : Obtain the fault type GL from the operation log of the smart slip ring. r The fault monitoring equipment for fault type GL r All monitoring records are recorded as parameter monitoring records, where r is a positive integer greater than or equal to 1 and less than or equal to t; the fault monitoring equipment is used to monitor fault type GL. r The criteria for determining the presence of an anomaly are recorded as the fault determination criteria, and the fault type GL is assigned. r The corresponding parameter unit is denoted as the fault unit;

[0062] In practical implementation, all fault types detected within the intelligent slip ring can include high brush wear, uneven brush wear, brush jamming, excessive contact resistance, increased leakage current, excessive contact resistance fluctuation, overcurrent, carbon powder accumulation, arcing, insulation degradation, ring erosion, and short circuits. Fault types GL1 to GL1 can be categorized based on the specific fault types that the intelligent slip ring can monitor during actual analysis. t Make specific settings;

[0063] Establish a Cartesian coordinate system, denoted as the parameter analysis coordinate system, where the units of the X-axis and Y-axis are time and fault units, respectively. Plot the parameter monitoring records within the parameter analysis coordinate system, where the fault type GL... r The corresponding parameter-time relationship curve is recorded as the parameter analysis curve.

[0064] In the analysis of this embodiment, for example, a fault type being analyzed is "high brush wear". The criterion for judging the abnormality of high brush wear is that the wear degree is less than 20%, so the fault unit is %. By acquiring parameter monitoring records, the parameter analysis curve corresponding to high brush wear is obtained as follows: Figure 3 As shown, where, Figure 3 Curves DQ1 to DQ3 within the diagram represent the relationship between brush wear and time for the three brushes. Figure 3 The times 20:30, 20:00, and 20:10 are the times when the brushes are replaced. For curve DQ1, curve CZ with the horizontal axis between 20:30 and 17:00 is the normal parameter curve, and curve CY with the horizontal axis between 17:00 and 20:00 is the abnormal parameter curve.

[0065] The fault type analysis strategy also includes: based on fault determination criteria, classifying fault types (GL) from parameter monitoring records. r The curve corresponding to abnormal data in the parameter analysis curve is denoted as the parameter abnormality curve. The fault type GL in the parameter monitoring record is recorded. r The curve corresponding to data without anomalies in the parameter analysis curve is called the normal parameter curve. Multiple abnormal parameter curves and normal parameter curves are allowed to exist.

[0066] Fault type GL r All corresponding abnormal parameter curves and normal parameter curves are denoted as fault type GL. r Twin simulation parameters;

[0067] Obtain the twin simulation parameters corresponding to all fault types.

[0068] The fault management analysis module is used to perform fault simulation within the twin model based on the twin simulation parameters of all fault types, and obtain the fault evolution link for each fault type based on the fault simulation results; it uses link analysis to analyze the fault evolution link, and obtains the fault management method for each fault type based on the analysis results;

[0069] The fault management and analysis module includes a fault simulation unit and a link analysis unit. The fault simulation unit is configured with single simulation strategies and multiple simulation strategies. The single simulation strategies include:

[0070] For any fault type GL r The corresponding abnormal curve α for any parameter: This represents the fault type GL. r The left-hand curves of all corresponding parameter normal curves are denoted as corrected operating curves XY1 to XY1, respectively. e Where e is the fault type GL r The corresponding number of normal curves for parameters; the left domain curves are the curves corresponding to the left half of the normal curves for parameters.

[0071] In the specific implementation process, the purpose of obtaining the left domain curve, that is, the curve corresponding to the left half of the normal parameter curve, is to provide a correction standard for subsequent correction of the fault type, that is, to ensure that the operating state corresponding to the corrected fault type can operate normally according to the left domain curve. When obtaining the corrected operating curve, the complete normal parameter curve can also be used as the corrected operating curve. However, since the corrected operating curve needs to be compared with the real-time simulation curve, and the complete normal parameter curve will cause a large error during comparison, resulting in slow comparison efficiency, this embodiment uses half of the normal parameter curve as the corrected operating curve to ensure that the curve comparison efficiency is improved while correcting the fault type. "Left half" refers to half of the data point. That is, if the interval occupied by the horizontal coordinate of the normal parameter curve is [0, 10:00], then the corrected operating curve obtained by interception is [0, 5:00].

[0072] In the specific implementation process, by obtaining the corrected running curve, it is possible to obtain all the curve types corresponding to the parameters of the fault type after the fault type in the abnormal state is repaired; and by obtaining the first correction process of the abnormal parameter curve α, it is possible to obtain the process of repairing the fault type with the abnormal parameter curve α in the shortest time, thereby realizing targeted management of the fault chain caused by the fault, improving management efficiency, and avoiding other potential fault hazards.

[0073] In the analysis of this embodiment, since the adjustment process for high brush wear only involves replacing the brush, the first correction process for all abnormal parameter curves corresponding to high brush wear can be set to replacing the brush.

[0074] For any corrected running curve XY p Simulate a smart slip ring in standard operating condition within a twin model, and use fault monitoring equipment to detect fault type GL. r The monitoring records correspond to the curves in the parameter analysis coordinate system, denoted as the real-time simulation curves. The standard operating state is when none of the fault types are in an abnormal state, and p is a positive integer less than or equal to e and greater than or equal to 1.

[0075] The single simulation strategy also includes: adjusting the operating state of the smart collector ring in the twin model, and marking the rightmost point of the real-time simulation curve as A1 when the rightmost region of the real-time simulation curve is exactly the same as the parameter anomaly curve α.

[0076] In the specific implementation process, the length of the "rightmost region" is the same as the length of the curve being compared. That is, when the horizontal coordinate length occupied by the parameter anomaly curve α is [15:00, 17:00], the rightmost 2-hour region of the real-time simulation curve should be compared with the parameter anomaly curve α; and when the horizontal coordinate interval occupied by the corrected running curve being compared is [0, 5:00], the rightmost 5-hour region of the real-time simulation curve should be compared with the corrected running curve.

[0077] Continue adjusting the operating state of the intelligent collector ring within the twin model until the rightmost region of the real-time simulation curve matches the corrected operating curve XY. p When they are completely identical, the abscissa of the rightmost point of the real-time simulation curve is marked as A2, the difference between A2 and A1 is recorded as the abnormal correction time, and the adjustment process of all components in the smart collector ring between A1 and A2 is recorded as the abnormal correction process.

[0078] In another analysis of this embodiment, the fault criterion for the fault type "excessive contact resistance" is a contact resistance greater than 100μΩ; an abnormal parameter curve for the fault type "excessive contact resistance" is as follows: Figure 4 The curve YC1 is shown in the figure, and a corrected operating curve corresponding to the fault type "excessive contact resistance" is shown in the figure. Figure 5As shown in curve YC2; in one analysis, when the rightmost region of the real-time simulation curve is exactly the same as curve YC1, the abscissa of the rightmost point of the real-time simulation curve is marked as 12:10. By adjusting the operating state of the smart collector ring in the twin model, when the rightmost region of the real-time simulation curve is exactly the same as YC2, the abscissa of the rightmost point of the real-time simulation curve is marked as 12:15. Through analysis, it can be found that the anomaly correction time is 5 minutes, and the adjustment process of all components in the smart collector ring between 12:10 and 12:15 includes... Clean the ring surface and clean the brush and brush holder; by analyzing all the correction operation curves of the fault type "excessive contact resistance", the minimum abnormal correction time is found to be 5 minutes. Therefore, "cleaning the ring surface and cleaning the brush and brush holder" can be recorded as the first correction process of "excessive contact resistance" and abnormal parameter curve YC1. That is, when the abnormal type is "excessive contact resistance" and the corresponding contact resistance curve is most similar to curve YC1, the most efficient treatment can be carried out by "cleaning the ring surface and cleaning the brush and brush holder".

[0079] Obtain the abnormal correction time of all corrected running curves; denote the abnormal correction process corresponding to the corrected running curve with the smallest abnormal correction time as the first correction process of parameter abnormal curve α.

[0080] The first correction process obtains all abnormal parameter curves for all fault types.

[0081] The multi-simulation strategy includes: for any fault type GL r For any one of the abnormal parameter curves α: Simulate the smart collector ring in standard operating condition within the twin model and obtain the corresponding real-time simulation curve;

[0082] At time B1, the operating state of the smart slip ring within the twin model is adjusted. When the rightmost region of the real-time simulation curve is exactly the same as the parameter anomaly curve α, the first correction process of the anomaly parameter curve α is used to adjust all components within the smart slip ring until the fault type GL is reached. r When not in an abnormal state, the x-coordinate of the rightmost point of the real-time simulation curve is marked as B2.

[0083] The multi-simulation strategy also includes: between time B1 and time B2, the fault-clearing type GL within the intelligent collector loop. r Among the fault types other than those mentioned above, the fault types that are in an abnormal state are denoted as linkage abnormal types, and the time they are in an abnormal state is denoted from first to last as linkage abnormal type LY1 to linkage abnormal type LY. u ;

[0084] In the analysis of this embodiment, for example, in an analysis of a fault type of "high brush wear", for the fault caused by... Figure 3 The obtained abnormal parameter curve CY is used to adjust the operating state of the intelligent slip ring in the twin model at 14:00 in the multi-simulation strategy. When the rightmost region of the real-time simulation curve corresponding to "high brush wear" is exactly the same as the abnormal parameter curve CY, the brush is replaced until the fault type GL is reached. r When not in an abnormal state, the x-coordinate of the rightmost point of the real-time simulation curve is 14:10. Between 14:00 and 14:10, analysis of fault types other than "high brush wear" reveals the following abnormal linkage types in sequence: "increased contact resistance," "localized temperature rise," "arc generation," "circular erosion," "carbon powder accumulation," "insulation degradation," and "short circuit." This indicates that when the abnormal state of brush wear is most similar to the abnormal parameter curve CY, the resulting fault chain is as follows: increased contact resistance → localized temperature rise → arc generation → circular erosion → carbon powder accumulation → insulation degradation → short circuit.

[0085] For any linkage anomaly type, the curve corresponding to the data when the linkage anomaly type is in an abnormal state in the parameter analysis coordinate system is denoted as the linkage anomaly curve; among all the abnormal parameter curves of the linkage anomaly type, the curve with the highest similarity to the linkage anomaly curve is denoted as the linkage evolution curve of the linkage anomaly type.

[0086] Linking exception type LY1 to linkage exception type LY u This is denoted as fault type GL. r The fault evolution link corresponding to the abnormal parameter curve α;

[0087] Obtain the anomaly evolution link corresponding to all fault types and each anomaly parameter curve.

[0088] The link analysis unit is configured with a link analysis strategy, which includes: for any fault type GL r The fault evolution link corresponding to the abnormal parameter curve α: linking the fault evolution link from the linked abnormal type LY1 to the linked abnormal type LY. u The linked evolution curves are denoted sequentially as linked evolution curve LQ1 to linked evolution curve LQ. u ;

[0089] Fault type GL r The fault management method corresponding to the abnormal parameter curve α is set as follows: execute the first correction process of the abnormal parameter curve α, and then sequentially execute the linkage evolution curves LQ1 to LQ. u The first revision process;

[0090] In the specific implementation process, the first correction process of the abnormal parameter curve α is executed, and then the linkage evolution curves LQ1 to LQ are executed sequentially. uThe first correction process can effectively and comprehensively manage the real-time management faults monitored in the real-time fault management module and the fault chains caused by the real-time management faults, so as to proactively and effectively predict the hidden dangers caused by the faults and carry out targeted management.

[0091] Get Fault Type GL r With fault type GL r The fault management methods corresponding to all abnormal parameter curves.

[0092] The real-time fault management module is used to update the fault management methods for all fault types corresponding to the smart collector ring based on the operating cycle of the smart collector ring; it records the fault types that are detected as abnormal within the smart collector ring as real-time management faults, and performs health management on the smart collector ring based on the latest fault management methods corresponding to the real-time management faults.

[0093] The real-time fault management module includes a real-time fault management unit, which is configured with real-time fault management policies. These policies include:

[0094] When the smart slip ring enters a new operating cycle, the fault management methods for all fault types corresponding to the smart slip ring are updated;

[0095] When a real-time management fault is detected, based on the monitoring records of the real-time management fault by the fault monitoring equipment, the curve corresponding to the abnormal real-time management fault in the parameter analysis coordinate system is obtained and recorded as the real-time fault curve.

[0096] Among all the abnormal parameter curves of real-time management faults, the curve with the highest similarity to the real-time fault curve is recorded as the first correction curve; the fault management method corresponding to the real-time management fault and the first correction curve is executed.

[0097] Example 2, please refer to Figure 2 As shown, this application also provides a smart collector ring health management method based on digital twins, including the following steps:

[0098] Step S1: Obtain the digital twin model corresponding to the intelligent collector ring based on the digital twin, and record it as the twin model; obtain the fault type of the intelligent collector ring and the twin simulation parameters corresponding to each fault type based on the operation log of the intelligent collector ring;

[0099] Step S1 includes: Step S101, based on the geometric model, physical model and digital thread of the smart slip ring, constructing a digital twin model corresponding to the smart slip ring using digital twin, and denoting it as the twin model. The geometric model includes the dimensional data of all components used to build the smart slip ring, the physical model includes the equations for integrated electrical contact, heat conduction, mechanical vibration, wear and fluid multi-field coupling, and the digital thread includes access to the design BMO, manufacturing process parameters and factory test data.

[0100] Step S102: Based on the operation log of the intelligent collector ring, obtain all fault types detected within the intelligent collector ring and record them as fault type GL1 to fault type GL1 respectively. t Where t is the total number of faults that the device monitoring the smart collector ring can detect within the smart collector ring, and the device that monitors the fault type is denoted as the fault monitoring device of the fault type.

[0101] Step S1 further includes: Step S103, for any fault type GL r : Obtain the fault type GL from the operation log of the smart slip ring. r The fault monitoring equipment for fault type GL r All monitoring records are recorded as parameter monitoring records, where r is a positive integer greater than or equal to 1 and less than or equal to t; the fault monitoring equipment is used to monitor fault type GL. r The criteria for determining the presence of an anomaly are recorded as the fault determination criteria, and the fault type GL is assigned. r The corresponding parameter unit is denoted as the fault unit;

[0102] Step S104: Establish a Cartesian coordinate system, denoted as the parameter analysis coordinate system. The units of the X-axis and Y-axis in the fault analysis coordinate system are time and fault units, respectively. Plot the parameter monitoring records within the parameter analysis coordinate system, including the fault type GL. r The corresponding parameter-time relationship curve is recorded and denoted as the parameter analysis curve.

[0103] Step S1 further includes: Step S105, based on the fault determination criteria, the fault type GL in the parameter monitoring record is... r The curve corresponding to abnormal data in the parameter analysis curve is denoted as the parameter abnormality curve. The fault type GL in the parameter monitoring record is recorded. r The curve corresponding to data without anomalies in the parameter analysis curve is called the normal parameter curve. Multiple abnormal parameter curves and normal parameter curves are allowed to exist.

[0104] Step S106, classify the fault type GL r All corresponding abnormal parameter curves and normal parameter curves are denoted as fault type GL. rTwin simulation parameters;

[0105] Step S107: Obtain the twin simulation parameters corresponding to all fault types.

[0106] Step S2: Based on the twin simulation parameters of all fault types, perform fault simulation within the twin model, and obtain the fault evolution link for each fault type based on the fault simulation results; use link analysis to analyze the fault evolution link, and obtain the fault management method for each fault type based on the analysis results;

[0107] Fault simulation includes single-fault simulation and multi-fault simulation. Step S201, single-fault simulation includes: Step S2011, for any fault type GL r The corresponding abnormal curve α for any parameter: This represents the fault type GL. r The left-hand curves of all corresponding parameter normal curves are denoted as corrected operating curves XY1 to XY1, respectively. e Where e is the fault type GL r The corresponding number of normal curves for parameters; the left domain curves are the curves corresponding to the left half of the normal curves for parameters.

[0108] Step S2012, for any corrected running curve XY p Simulate a smart slip ring in standard operating condition within a twin model, and use fault monitoring equipment to detect fault type GL. r The monitoring records correspond to the curves in the parameter analysis coordinate system, denoted as the real-time simulation curves. The standard operating state is when none of the fault types are in an abnormal state, and p is a positive integer less than or equal to e and greater than or equal to 1.

[0109] The single fault simulation also includes: step S2013, adjusting the operating state of the smart collector ring in the twin model, when the rightmost region of the real-time simulation curve is exactly the same as the parameter abnormality curve α, marking the abscissa of the rightmost point of the real-time simulation curve as A1.

[0110] Step S2014: Continue adjusting the operating state of the intelligent collector ring within the twin model until the rightmost region of the real-time simulation curve matches the corrected operating curve XY. p When they are completely identical, the abscissa of the rightmost point of the real-time simulation curve is marked as A2, the difference between A2 and A1 is recorded as the abnormal correction time, and the adjustment process of all components in the smart collector ring between A1 and A2 is recorded as the abnormal correction process.

[0111] Step S2015: Obtain the abnormal correction time of all corrected running curves; record the abnormal correction process corresponding to the corrected running curve with the smallest abnormal correction time as the first correction process of parameter abnormal curve α.

[0112] Step S2016: Obtain the first correction process for all abnormal parameter curves of all fault types.

[0113] Step S202, multi-fault simulation includes: Step S2021, for any fault type GL r For any one of the abnormal parameter curves α: Simulate the smart collector ring in standard operating condition within the twin model and obtain the corresponding real-time simulation curve;

[0114] Step S2022: At time B1, adjust the operating state of the smart collector ring in the twin model. When the rightmost region of the real-time simulation curve is exactly the same as the parameter anomaly curve α, use the first correction process of the anomaly parameter curve α to adjust all components in the smart collector ring until the fault type GL is reached. r When not in an abnormal state, the x-coordinate of the rightmost point of the real-time simulation curve is marked as B2.

[0115] Multi-fault simulation also includes: step S2023, between time B1 and time B2, troubleshooting fault type GL within the intelligent collector ring. r Among the fault types other than those mentioned above, the fault types that are in an abnormal state are denoted as linkage abnormal types, and the time they are in an abnormal state is denoted from first to last as linkage abnormal type LY1 to linkage abnormal type LY. u ;

[0116] Step S2024: For any linkage anomaly type, the curve corresponding to the data when the linkage anomaly type is in an abnormal state in the parameter analysis coordinate system is denoted as the linkage anomaly curve; among all the abnormal parameter curves of the linkage anomaly type, the curve with the highest similarity to the linkage anomaly curve is denoted as the linkage evolution curve of the linkage anomaly type.

[0117] Step S2025: Change the linkage exception type LY1 to linkage exception type LY u This is denoted as fault type GL. r The fault evolution link corresponding to the abnormal parameter curve α;

[0118] Step S2026: Obtain the anomaly evolution link corresponding to all fault types and each anomaly parameter curve.

[0119] Step S203, the link analysis method includes: Step S2031, for any fault type GL r The fault evolution link corresponding to the abnormal parameter curve α: linking the fault evolution link from the linked abnormal type LY1 to the linked abnormal type LY. u The linked evolution curves are denoted sequentially as linked evolution curve LQ1 to linked evolution curve LQ. u ;

[0120] Step S2032, classify the fault type GL r The fault management method corresponding to the abnormal parameter curve α is set as follows: execute the first correction process of the abnormal parameter curve α, and then sequentially execute the linkage evolution curves LQ1 to LQ. u The first revision process;

[0121] Step S2033, obtain the fault type GL r With fault type GL r The fault management methods corresponding to all abnormal parameter curves.

[0122] Step S3: Based on the operating cycle of the smart collector ring, update the fault management methods for all fault types corresponding to the smart collector ring; record the fault types that are detected as abnormal within the smart collector ring as real-time management faults, and perform health management on the smart collector ring based on the latest fault management methods corresponding to the real-time management faults.

[0123] Step S3 includes: Step S301, when the smart collector ring runs to a new operating cycle, update the fault management methods for all fault types corresponding to the smart collector ring;

[0124] Step S302: When a real-time management fault is detected, based on the monitoring records of the real-time management fault by the fault monitoring device, obtain the curve in the parameter analysis coordinate system corresponding to the real-time management fault when it is abnormal, and record it as the real-time fault curve.

[0125] Step S303: Among all the abnormal parameter curves of real-time management faults, the curve with the highest similarity to the real-time fault curve is recorded as the first correction curve; execute the fault management method corresponding to the real-time management fault and the first correction curve.

[0126] Example 3, please refer to Figure 6 As shown, Figure 6The example illustrates the structure of an electronic device, which may include a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus. The memory stores computer-readable instructions, which the processor can call. When the computer-readable instructions are executed by the processor, the steps in the intelligent collector ring health management method based on digital twins are performed to achieve the following functions: First, a digital twin model corresponding to the intelligent collector ring is obtained based on the digital twin and recorded as the twin model; the fault types of the intelligent collector ring and the twin simulation parameters corresponding to each fault type are obtained based on the operation log of the intelligent collector ring; then, based on the twin simulation parameters of all fault types, fault simulation is performed within the twin model, and the fault evolution link of each fault type is obtained based on the fault simulation results; the fault evolution link is analyzed using the link analysis method, and the fault management method for each fault type is obtained based on the analysis results; finally, the fault management methods for all fault types corresponding to the intelligent collector ring are updated based on the operating cycle of the intelligent collector ring; the fault types that are detected as abnormal within the intelligent collector ring are recorded as real-time management faults, and the intelligent collector ring is health managed based on the latest fault management method corresponding to the real-time management faults.

[0127] Furthermore, when the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0128] Example 4: This application also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it performs the steps of the above-described intelligent slip ring health management method based on digital twins to achieve the following functions: First, it obtains a digital twin model corresponding to the intelligent slip ring based on the digital twin and records it as the twin model; it obtains the fault types of the intelligent slip ring and the twin simulation parameters corresponding to each fault type based on the operation log of the intelligent slip ring; then, based on the twin simulation parameters of all fault types, it performs fault simulation within the twin model and obtains the fault evolution link for each fault type based on the fault simulation results; it analyzes the fault evolution link using the link analysis method and obtains the fault management method for each fault type based on the analysis results; finally, it updates the fault management methods for all fault types corresponding to the intelligent slip ring based on the operating cycle of the intelligent slip ring; it records the fault types that are detected as abnormal within the intelligent slip ring as real-time management faults and performs health management on the intelligent slip ring based on the latest fault management method corresponding to the real-time management faults.

[0129] Based on the above description of the embodiments, the embodiments of the present invention can be provided as methods, systems, or computer program products. Based on this understanding, the above technical solutions, in essence or in terms of their contribution to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or certain parts of the embodiments.

[0130] In the embodiments provided in this application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are merely illustrative. For example, the division of modules or units is only a logical functional division, and there may be other division methods in actual implementation. Furthermore, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interfaces. The indirect coupling or communication connection between systems, modules, and units may be electrical, mechanical, or other forms.

[0131] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for intelligent collector ring health management based on digital twins, characterized in that, Includes the following steps: The digital twin model corresponding to the intelligent collector ring is obtained based on the digital twin and recorded as the twin model; the fault types of the intelligent collector ring and the twin simulation parameters corresponding to each fault type are obtained based on the operation log of the intelligent collector ring. Based on the twin simulation parameters of all fault types, fault simulation is performed within the twin model, and the fault evolution link of each fault type is obtained based on the fault simulation results. Link analysis is used to analyze the fault evolution link, and fault management methods for each fault type are obtained based on the analysis results. Based on the operating cycle of the intelligent slip ring, the fault management methods for all fault types corresponding to the intelligent slip ring are updated. The fault type detected by the intelligent collector ring is recorded as a real-time management fault, and the intelligent collector ring is health managed based on the latest fault management method corresponding to the real-time management fault. Link analysis methods include: For any fault type GL r The fault evolution link corresponding to the abnormal parameter curve α: linking the fault evolution link from the linked abnormal type LY1 to the linked abnormal type LY. u The linked evolution curves are denoted sequentially as linked evolution curve LQ1 to linked evolution curve LQ. u ; Fault type GL r The fault management method corresponding to the abnormal parameter curve α is set as follows: execute the first correction process of the abnormal parameter curve α, and then sequentially execute the linkage evolution curves LQ1 to LQ. u The first revision process; Get Fault Type GL r With fault type GL r The fault management methods corresponding to all abnormal parameter curves.

2. The intelligent collector ring health management method based on digital twin according to claim 1, characterized in that, The digital twin model corresponding to the intelligent collector ring is obtained based on the digital twin and denoted as the twin model; The fault types of the smart slip ring obtained from the operation log include: Based on the geometric model, physical model, and digital thread of the smart slip ring, a digital twin model corresponding to the smart slip ring is constructed using digital twins and denoted as the twin model. The geometric model includes the dimensional data of all components used to build the smart slip ring, the physical model includes the equations for integrated electrical contact, heat conduction, mechanical vibration, wear, and fluid multi-field coupling, and the digital thread includes the access design BMO, manufacturing process parameters, and factory test data. Based on the operation log of the intelligent collector ring, all fault types detected within the intelligent collector ring are obtained and denoted as fault type GL1 to fault type GL. t Where t is the total number of faults that the device monitoring the smart collector ring can detect within the smart collector ring, and the device that monitors the fault type is denoted as the fault monitoring device of the fault type; For any fault type GL r : Obtain the fault type GL from the operation log of the smart slip ring. r The fault monitoring equipment for fault type GL r All monitoring records are recorded as parameter monitoring records, where r is a positive integer greater than or equal to 1 and less than or equal to t; the fault monitoring equipment is used to monitor fault type GL. r The criteria for determining the presence of an anomaly are recorded as the fault determination criteria, and the fault type GL is assigned. r The corresponding parameter unit is denoted as the fault unit; Establish a Cartesian coordinate system, denoted as the parameter analysis coordinate system, where the units of the X-axis and Y-axis are time and fault units, respectively. Plot the parameter monitoring records within the parameter analysis coordinate system, where the fault type GL... r The corresponding parameter-time relationship curve is recorded and denoted as the parameter analysis curve.

3. The intelligent collector ring health management method based on digital twin according to claim 2, characterized in that, Based on the operation logs of the smart slip ring, the fault types of the smart slip ring and the corresponding twin simulation parameters for each fault type are obtained, including: Based on the fault determination criteria, the fault type GL in the parameter monitoring records will be... r The curve corresponding to abnormal data in the parameter analysis curve is denoted as the parameter abnormality curve. The fault type GL in the parameter monitoring record is recorded. r The curve corresponding to data without anomalies in the parameter analysis curve is called the normal parameter curve. Multiple abnormal parameter curves and normal parameter curves are allowed to exist. Fault type GL r All corresponding abnormal parameter curves and normal parameter curves are denoted as fault type GL. r Twin simulation parameters; Obtain the twin simulation parameters corresponding to all fault types.

4. The intelligent collector ring health management method based on digital twin according to claim 3, characterized in that, Fault simulation includes single-fault simulation and multi-fault simulation. Single-fault simulation includes: For any fault type GL r The corresponding abnormal curve α for any parameter: This represents the fault type GL. r The left-hand curves of all corresponding parameter normal curves are denoted as corrected operating curves XY1 to XY1, respectively. e Where e is the fault type GL r The corresponding number of normal curves for parameters; the left domain curves are the curves corresponding to the left half of the normal curves for parameters. For any corrected running curve XY p Simulate a smart slip ring in standard operating condition within a twin model, and use fault monitoring equipment to detect fault type GL. r The monitoring records correspond to the curves in the parameter analysis coordinate system, denoted as the real-time simulation curves. The standard operating state is when none of the fault types are in an abnormal state, and p is a positive integer less than or equal to e and greater than or equal to 1.

5. The intelligent collector ring health management method based on digital twin according to claim 4, characterized in that, Single-fault simulation also includes: Adjust the operating state of the intelligent collector ring in the twin model. When the rightmost region of the real-time simulation curve is exactly the same as the parameter anomaly curve α, mark the abscissa of the rightmost point of the real-time simulation curve as A1. Continue adjusting the operating state of the intelligent collector ring within the twin model until the rightmost region of the real-time simulation curve matches the corrected operating curve XY. p When they are completely identical, the abscissa of the rightmost point of the real-time simulation curve is marked as A2, the difference between A2 and A1 is recorded as the abnormal correction time, and the adjustment process of all components in the smart collector ring between A1 and A2 is recorded as the abnormal correction process. Obtain the abnormal correction time of all corrected running curves; denote the abnormal correction process corresponding to the corrected running curve with the smallest abnormal correction time as the first correction process of parameter abnormal curve α. The first correction process obtains all abnormal parameter curves for all fault types.

6. The intelligent collector ring health management method based on digital twin according to claim 5, characterized in that, Multi-fault simulation includes: For any fault type GL r For any one of the abnormal parameter curves α: Simulate the smart collector ring in standard operating condition within the twin model and obtain the corresponding real-time simulation curve; At time B1, the operating state of the smart slip ring within the twin model is adjusted. When the rightmost region of the real-time simulation curve is exactly the same as the parameter anomaly curve α, the first correction process of the anomaly parameter curve α is used to adjust all components within the smart slip ring until the fault type GL is reached. r When not in an abnormal state, the x-coordinate of the rightmost point of the real-time simulation curve is marked as B2.

7. The intelligent collector ring health management method based on digital twin according to claim 6, characterized in that, Multi-fault simulation also includes: Between time B1 and time B2, troubleshooting type GL within the intelligent collector ring. r Among the fault types other than those mentioned above, the fault types that are in an abnormal state are denoted as linkage abnormal types, and the time they are in an abnormal state is denoted from first to last as linkage abnormal type LY1 to linkage abnormal type LY. u ; For any linkage anomaly type, the curve corresponding to the data when the linkage anomaly type is in an abnormal state in the parameter analysis coordinate system is denoted as the linkage anomaly curve; among all the abnormal parameter curves of the linkage anomaly type, the curve with the highest similarity to the linkage anomaly curve is denoted as the linkage evolution curve of the linkage anomaly type. Linking exception type LY1 to linkage exception type LY u This is denoted as fault type GL. r The fault evolution link corresponding to the abnormal parameter curve α; Obtain the anomaly evolution link corresponding to all fault types and each anomaly parameter curve.

8. The intelligent collector ring health management method based on digital twin according to claim 7, characterized in that, Based on the operating cycle of the intelligent slip ring, the fault management methods for all fault types corresponding to the intelligent slip ring are updated. Fault types detected as abnormalities within the smart collector ring are categorized as real-time management faults. Based on the latest fault management methods corresponding to these real-time management faults, health management of the smart collector ring includes: When the smart slip ring enters a new operating cycle, the fault management methods for all fault types corresponding to the smart slip ring are updated; When a real-time management fault is detected, based on the monitoring records of the real-time management fault by the fault monitoring equipment, the curve corresponding to the abnormal real-time management fault in the parameter analysis coordinate system is obtained and recorded as the real-time fault curve. Among all the abnormal parameter curves of real-time management faults, the curve with the highest similarity to the real-time fault curve is recorded as the first correction curve; the fault management method corresponding to the real-time management fault and the first correction curve is executed.

9. A digital twin-based intelligent slip ring health management system, used to implement the digital twin-based intelligent slip ring health management method according to any one of claims 1-8, characterized in that, It includes a fault type analysis module, a fault management analysis module, and a real-time fault management module; The fault type analysis module is used to obtain the digital twin model corresponding to the smart collector ring based on the digital twin, and denot it as the twin model; and to obtain the fault type of the smart collector ring and the twin simulation parameters corresponding to each fault type based on the operation log of the smart collector ring. The fault management and analysis module is used to perform fault simulation within the twin model based on the twin simulation parameters of all fault types, and to obtain the fault evolution link for each fault type based on the fault simulation results. Link analysis is used to analyze the fault evolution link, and fault management methods for each fault type are obtained based on the analysis results. The real-time fault management module is used to update the fault management methods for all fault types corresponding to the smart collector ring based on the operating cycle of the smart collector ring. Fault types that are detected as abnormalities within the smart collector ring are recorded as real-time management faults, and health management of the smart collector ring is performed based on the latest fault management method corresponding to the real-time management faults.

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