A self-diagnostic and repair method and system for data processing clock faults

Abstract

This invention relates to the field of data processing technology, and discloses a self-diagnostic repair method and system for data processing clock faults. The method includes: acquiring a master clock signal and a reference clock signal; extracting the instantaneous phase difference sequence and residual harmonic components of the master clock signal based on the reference clock signal; fusing the statistical distribution characteristics of the instantaneous phase difference sequence and the energy of the residual harmonic components to generate a comprehensive stability index; pre-triggering an adaptive repair mechanism when the comprehensive stability index indicates that it will exceed a preset tolerance; matching the characteristics of the current signal with a historical fault evolution pattern library to predict the fault evolution trajectory and pre-select a repair strategy accordingly; executing the pre-selected repair strategy and dynamically optimizing the repair parameters until the comprehensive stability index stabilizes within the preset tolerance, thus completing the adaptive closed-loop repair. This invention can achieve early detection and efficient containment of potential faults, fundamentally ensuring the stability of the clock signal and the timing reliability of data processing.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a self-diagnosis and repair method and system for data processing clock faults. Background Technology

[0002] In data processing systems, clock signals are the core benchmark for driving digital logic operation and ensuring the consistency of data sampling and transmission timing. In critical scenarios such as industrial control, high-speed communication, and artificial intelligence computing, clock signals are susceptible to factors such as temperature drift, hardware aging, and electromagnetic interference, resulting in faults such as phase jitter, frequency offset, and harmonic interference. These faults can directly lead to data calculation errors, disordered transaction sequences, or even system shutdown, causing serious functional failures or economic losses. As data processing speeds continue to increase and clock operating frequencies continue to rise, their stability becomes increasingly critical to the overall system performance. Therefore, timely self-diagnosis and repair of clock faults have become necessary measures to ensure the continuous and reliable operation of the system.

[0003] Existing technologies for self-diagnosis and repair of clock faults in data processing often rely on single characteristic indicators to assess clock stability, making it difficult to comprehensively characterize the complex signal changes caused by the fault. This results in delayed fault warnings or a high false alarm rate. Furthermore, the selection of repair strategies is often based on fixed rules or experience-based judgments, failing to fully integrate with the evolution trend of the fault for targeted adaptation. Moreover, the setting of repair parameters lacks dynamic feedback and adjustment based on real-time repair effects. Limited by insufficient coupling analysis of multi-dimensional fault characteristics and a lack of effective utilization of historical fault data, the repair process struggles to quickly restore clock stability to the preset tolerance, and its long-term stability maintenance capability is insufficient. Summary of the Invention

[0004] This invention provides a self-diagnostic and repair method and system for data processing clock faults to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a self-diagnosis and repair method for data processing clock faults, comprising:

[0006] S1. Obtain the master clock signal and the reference clock signal. Based on the reference clock signal, extract the instantaneous phase difference sequence and residual harmonic components of the master clock signal.

[0007] S2. By integrating the statistical distribution characteristics of the instantaneous phase difference sequence with the energy of the residual harmonic components, a comprehensive stability index is generated.

[0008] S3. When the overall stability index indicates that it will exceed the preset tolerance, the adaptive repair mechanism is pre-triggered.

[0009] S4. Match the characteristics of the current signal with the historical fault evolution pattern library to predict the fault evolution trajectory and pre-select the repair strategy accordingly.

[0010] S5. Execute the pre-selected repair strategy and dynamically optimize the repair parameters until the overall stability index stabilizes within the preset tolerance, thus completing the adaptive closed-loop repair.

[0011] Preferably, the extraction of the instantaneous phase difference sequence of the master clock signal includes:

[0012] The timing reference of the master clock signal is calibrated based on the reference clock signal to generate a timing-aligned master clock signal;

[0013] The phase fluctuations of the timing-aligned master clock signal are tracked and its transmission path delay is compensated to obtain the phase fluctuation characteristics.

[0014] The instantaneous phase difference sequence is derived from the phase fluctuation characteristics, and the sensitivity of the detection process is adaptively adjusted to output the optimized instantaneous phase difference sequence.

[0015] Preferably, the extraction of residual harmonic components of the master clock signal includes:

[0016] Dynamic noise suppression is applied to the master clock signal, and its amplitude is balanced with reference to the optimized instantaneous phase difference sequence to generate a preprocessed clock signal.

[0017] The fundamental and harmonic components are separated from the preprocessed clock signal, and the energy distribution of the harmonics is analyzed to obtain a harmonic component dataset.

[0018] Extract the target residual harmonic components from the harmonic component dataset, optimize the judgment conditions of the extraction process, and output accurate residual harmonic components.

[0019] Preferably, the generation of the comprehensive stability index includes:

[0020] Extract the statistical distribution characteristics of the instantaneous phase difference sequence, evaluate its central tendency and dispersion, and generate phase stability parameters;

[0021] Analyze the energy distribution of residual harmonic components, identify the energy concentration region of the dominant harmonic component, and obtain the harmonic energy characteristics;

[0022] By integrating phase stability parameters and harmonic energy characteristics, and balancing the contributions of both through a dynamic weighting factor, a comprehensive stability index is output.

[0023] Preferably, the fusion of phase stability parameters and harmonic energy characteristics includes:

[0024] The phase stability parameters are normalized to unify the parameter benchmark and generate standardized phase parameters.

[0025] An energy focusing operation is performed on the harmonic energy characteristics to adjust the energy distribution ratio and obtain enhanced harmonic characteristics;

[0026] The standardized phase parameters are coupled with the enhanced harmonic characteristics, and the two types of characteristics are integrated through nonlinear combination to generate an optimized comprehensive stability index.

[0027] Preferably, the step of pre-triggering the adaptive repair mechanism when the comprehensive stability index indicates that it will exceed the preset tolerance includes:

[0028] Track the dynamic changes of the comprehensive stability index, identify its critical state near the preset tolerance, and generate an early warning trigger signal;

[0029] The repair resources are initialized based on the early warning trigger signal, and the response parameters of the repair strategy are adjusted to build an active repair framework.

[0030] Within the proactive repair framework, verify the expected effects of repair actions, select the optimal execution path, and output instances of pre-triggered repair mechanisms.

[0031] Preferably, the step of matching the characteristics of the current signal with a historical fault evolution pattern library to predict the fault evolution trajectory and pre-select a repair strategy accordingly includes:

[0032] Extract key features of the current signal, compare them with typical patterns in the historical fault evolution pattern library, and generate the current fault feature vector;

[0033] Map the current fault feature vector to the time series of fault development, deduce its potential evolution path, and obtain a set of fault evolution paths;

[0034] For each path in the set of fault evolution paths, assess its corresponding repair requirements, configure an appropriate combination of repair strategies, and output a pre-selected set of repair strategies.

[0035] Preferably, the step of executing the pre-selected repair strategy and dynamically optimizing the repair parameters until the overall stability index stabilizes within a preset tolerance, thus completing the adaptive closed-loop repair, includes:

[0036] Invoke the target strategy from the pre-selected repair strategy set, configure the corresponding repair execution unit, and generate the initial repair execution framework;

[0037] The repair actions are performed within the initial repair execution framework, while the real-time response of the comprehensive stability index is monitored to obtain feedback data of the repair process;

[0038] The repair intensity and duration are adjusted based on feedback data from the repair process, and an optimized set of repair parameters is output.

[0039] Preferably, the step of executing the pre-selected repair strategy and dynamically optimizing the repair parameters until the overall stability index stabilizes within a preset tolerance, thus completing the adaptive closed-loop repair, further includes:

[0040] Deep repair is performed based on the optimized repair parameter set to verify the stability characteristics of the repaired signal and generate repair verification results.

[0041] The repair verification results are compared with the preset tolerance to confirm the degree of recovery of the comprehensive stability index and obtain a stability recovery assessment.

[0042] Based on the stability recovery assessment, the closed-loop control of the repair process is completed, the historical fault evolution mode library is updated, and a repair completion signal is output.

[0043] To address the aforementioned problems, the present invention also provides a self-diagnostic and repair system for data processing clock faults, the system comprising:

[0044] The signal processing module is used to acquire the master clock signal and the reference clock signal, and extract the instantaneous phase difference sequence and residual harmonic components of the master clock signal based on the reference clock signal.

[0045] The index generation module is used to integrate the statistical distribution characteristics of the instantaneous phase difference sequence with the energy of the residual harmonic components to generate a comprehensive stability index.

[0046] The mechanism triggering module is used to pre-trigger the adaptive repair mechanism when the comprehensive stability index indicates that it will exceed the preset tolerance.

[0047] The strategy pre-selection module is used to match the characteristics of the current signal with the historical fault evolution pattern library, predict the fault evolution trajectory, and pre-select the repair strategy accordingly.

[0048] The closed-loop repair module is used to execute the pre-selected repair strategy and dynamically optimize the repair parameters until the overall stability index stabilizes within the preset tolerance, thus completing the adaptive closed-loop repair.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] 1. By integrating the statistical distribution characteristics of instantaneous phase difference sequences with the energy of residual harmonic components to construct a comprehensive stability index, and combining it with a historical fault evolution pattern library to achieve fault trajectory prediction and targeted repair strategy pre-selection, the accuracy of fault identification and the pertinence of repair actions are greatly improved, enabling early detection and efficient containment of potential faults, and ensuring the stability of clock signals and the timing reliability of data processing from the root.

[0051] 2. By relying on the adaptive closed-loop repair mechanism to dynamically optimize repair parameters and updating the historical fault evolution mode library in reverse through repair results, a continuous iterative optimization loop is formed. This not only strengthens the real-time response capability of the repair process, but also improves the long-term adaptability of the technical solution to complex and ever-changing fault scenarios. It provides core support with both stability and adaptability for high-frequency and high-complexity data processing scenarios, and significantly enhances the continuous reliability of system operation. Attached Figure Description

[0052] Figure 1 This is a flowchart illustrating a data processing clock fault self-diagnosis and repair method according to an embodiment of the present invention.

[0053] Figure 2 This is a functional block diagram of a data processing clock fault self-diagnosis and repair system provided in an embodiment of the present invention;

[0054] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0055] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0056] This application provides a self-diagnostic and repair method for data processing clock faults. The execution subject of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the self-diagnostic and repair method for data processing clock faults can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0057] Example 1, referring to Figure 1 The diagram shown is a flowchart illustrating a self-diagnosis and repair method for data processing clock faults according to an embodiment of the present invention. In this embodiment, the self-diagnosis and repair method for data processing clock faults includes:

[0058] S1. Obtain the master clock signal and the reference clock signal. Based on the reference clock signal, extract the instantaneous phase difference sequence and residual harmonic components of the master clock signal.

[0059] S2. By integrating the statistical distribution characteristics of the instantaneous phase difference sequence with the energy of the residual harmonic components, a comprehensive stability index is generated.

[0060] S3. When the overall stability index indicates that it will exceed the preset tolerance, the adaptive repair mechanism is pre-triggered.

[0061] S4. Match the characteristics of the current signal with the historical fault evolution pattern library to predict the fault evolution trajectory and pre-select the repair strategy accordingly.

[0062] S5. Execute the pre-selected repair strategy and dynamically optimize the repair parameters until the overall stability index stabilizes within the preset tolerance, thus completing the adaptive closed-loop repair.

[0063] In a preferred embodiment, extracting the instantaneous phase difference sequence of the master clock signal includes:

[0064] The timing reference of the master clock signal is calibrated based on the reference clock signal to generate a timing-aligned master clock signal;

[0065] The phase fluctuations of the timing-aligned master clock signal are tracked and its transmission path delay is compensated to obtain the phase fluctuation characteristics.

[0066] The instantaneous phase difference sequence is derived from the phase fluctuation characteristics, and the sensitivity of the detection process is adaptively adjusted to output the optimized instantaneous phase difference sequence.

[0067] Specifically, the rising edge of the reference clock is used as the timing reference point. The rising edge position of the master clock signal is compared cycle by cycle. When the rising edge of the master clock deviates from the rising edge of the reference clock, the trigger delay circuit of the master clock signal is adjusted to accurately align the rising edge of the master clock to the rising edge position of the reference clock. The alignment status of each cycle is continuously compared and corrected in real time, and finally a timing-aligned master clock signal is generated.

[0068] A phase detection circuit is used to capture the phase change of the timing-aligned master clock signal in real time for each cycle. The fixed delay value of the master clock signal transmission path is measured in advance. The phase offset corresponding to the fixed delay is subtracted from the detected phase change data. The phase change trajectory after subtraction is continuously recorded, thereby obtaining the phase fluctuation characteristics.

[0069] The difference between the phase value and the reference phase value at each sampling moment in the phase fluctuation feature is extracted in chronological order to form an initial instantaneous phase difference sequence. By monitoring the change amplitude of the difference in the sequence, the sampling density of the detection circuit is increased when the change amplitude is small, and the sampling density is maintained and glitch signals are filtered when the change amplitude is large. Finally, the optimized instantaneous phase difference sequence is output.

[0070] In this embodiment, the extraction of residual harmonic components of the master clock signal includes:

[0071] Dynamic noise suppression is applied to the master clock signal, and its amplitude is balanced with reference to the optimized instantaneous phase difference sequence to generate a preprocessed clock signal.

[0072] The fundamental and harmonic components are separated from the preprocessed clock signal, and the energy distribution of the harmonics is analyzed to obtain a harmonic component dataset.

[0073] Extract the target residual harmonic components from the harmonic component dataset, optimize the judgment conditions of the extraction process, and output accurate residual harmonic components.

[0074] Specifically, the master clock signal is filtered in real time using a dedicated filtering component to intercept high-frequency noise and low-frequency interference signals to achieve dynamic noise suppression. At the same time, the amplitude of each period of the signal is adjusted by comparing it with the optimized instantaneous phase difference sequence to keep the amplitude of different periods consistent, thus generating a pre-processed clock signal.

[0075] A frequency separation module is used to identify the fundamental wave component with the lowest frequency and the strongest energy in the preprocessed clock signal. The remaining signals with frequencies that are integer multiples of the fundamental wave are defined as harmonic components. The energy values ​​of each harmonic component are detected and recorded one by one, and the harmonic components are organized in order of frequency to form a harmonic component dataset.

[0076] Based on the energy values ​​of the harmonic component dataset, a fixed judgment criterion is set to screen out the target residual harmonic components that have not been eliminated by the noise suppression process. By comparing the extraction results under different judgment criteria, the leniency of the judgment criteria is adjusted to ensure that the extraction results are complete and without redundancy, and to output accurate residual harmonic components.

[0077] In summary, when extracting residual harmonic components, dynamic noise suppression is first applied to reduce interference, and then the amplitude is balanced by referring to the optimized instantaneous phase difference sequence to ensure the accuracy of the preprocessed signal. By separating the fundamental wave and harmonics, analyzing the energy distribution, and combining optimized judgment conditions to extract the target residual harmonics, the omission or redundancy of components is effectively avoided, and the accuracy of residual harmonic extraction is improved.

[0078] When generating the comprehensive stability index, both the statistical characteristics of the instantaneous phase difference sequence and the energy characteristics of the residual harmonic components are taken into account, so as to comprehensively characterize the complex state of the clock signal and solve the problem of the one-sided evaluation of the single index in the existing technology.

[0079] By using dynamic weighting factors to balance the contributions of the two types of features, the comprehensive stability index can truly reflect the clock stability, providing a reliable basis for subsequent fault warning and repair strategy selection, helping to quickly restore the clock stability to the preset tolerance, and improving repair efficiency and long-term stability.

[0080] In a preferred embodiment, generating the comprehensive stability index includes:

[0081] Extract the statistical distribution characteristics of the instantaneous phase difference sequence, evaluate its central tendency and dispersion, and generate phase stability parameters;

[0082] Analyze the energy distribution of residual harmonic components, identify the energy concentration region of the dominant harmonic component, and obtain the harmonic energy characteristics;

[0083] By integrating phase stability parameters and harmonic energy characteristics, and balancing the contributions of both through a dynamic weighting factor, a comprehensive stability index is output.

[0084] Specifically, all data points in the instantaneous phase difference sequence are processed one by one, and the frequency of occurrence of each phase difference value is counted. The phase difference value with the highest frequency is used to reflect the central tendency. At the same time, the deviation range of all data points from the high-frequency phase difference value is calculated to fully present the degree of dispersion, and the phase stability parameter is generated accordingly.

[0085] The energy values ​​of residual harmonic components are captured by an energy detection component, sorted from largest to smallest, and the dominant harmonic component with the largest energy value is identified. The frequency range of energy concentration corresponding to this component is tracked, and the start and end range of the range is determined, thereby obtaining the harmonic energy characteristics.

[0086] Based on the phase fluctuations reflected by the phase stability parameter and the strength of the harmonic energy characteristics, the weighting ratio of the two is dynamically adjusted. When the phase fluctuation is small, the weight of the phase stability parameter is increased, and when the harmonic energy is strong, the weight of the harmonic energy characteristics is increased. The two weighted results are then integrated to output a comprehensive stability index.

[0087] In this embodiment, the phase stability parameter and harmonic energy characteristics are integrated, including:

[0088] The phase stability parameters are normalized to unify the parameter benchmark and generate standardized phase parameters.

[0089] An energy focusing operation is performed on the harmonic energy characteristics to adjust the energy distribution ratio and obtain enhanced harmonic characteristics;

[0090] The standardized phase parameters are coupled with the enhanced harmonic characteristics, and the two types of characteristics are integrated through nonlinear combination to generate an optimized comprehensive stability index.

[0091] Specifically, the maximum and minimum values ​​of the phase stability parameters are determined, all phase stability parameter values ​​are compressed into a fixed range by the same proportion, the relative magnitude relationship between the parameters remains unchanged, the parameter measurement benchmark is unified, and standardized phase parameters are generated.

[0092] The energy proportion of dominant harmonics in the harmonic energy characteristics is screened, and the numerical representation of this proportion is amplified by an energy adjustment component. At the same time, the energy influence of non-dominant harmonic components is weakened, and the overall energy distribution ratio is adjusted to obtain enhanced harmonic characteristics.

[0093] The coupling correlation strength is set according to the contribution strength of the two types of features to the stability assessment. The standardized phase parameters are correlated with the enhanced harmonic features. By adjusting the correlation depth of feature fusion, a nonlinear combination is achieved, allowing the advantages of the two types of features to complement each other and generating an optimized comprehensive stability index.

[0094] In summary, when extracting phase and harmonic features, the core information of phase fluctuations can be accurately captured by evaluating the central tendency and dispersion of the instantaneous phase difference sequence, avoiding single-dimensional bias; the energy concentration area of ​​the dominant residual harmonic component is identified, highlighting key interference sources, so that both phase stability parameters and harmonic energy characteristics are targeted, solving the problem of one-sided feature extraction in existing technologies.

[0095] The normalization process in the fusion stage unifies the phase parameter benchmark and eliminates interference caused by dimensional differences; the energy focusing operation strengthens the influence of the dominant harmonics and weakens the interference of secondary components, making the two types of features more suitable for subsequent integration.

[0096] Nonlinear combination can capture the complex coupling relationship between phase and harmonics. Combined with dynamic weighting factors, the contributions of the two are balanced in real time. For example, when the phase fluctuation is large, the phase parameter is emphasized, and when the harmonic interference is strong, the harmonic characteristics are emphasized. The resulting comprehensive stability index is closer to the real state of the clock, providing accurate basis for fault warning and repair strategy selection, and reducing the risk of false alarms or lag.

[0097] As a preferred implementation, to achieve the integration of two types of features through nonlinear combination, the present invention employs the following nonlinear fusion function to generate the optimized comprehensive stability index:

[0098]

[0099] In the formula, This represents the standardized phase parameter, which is a measure of phase stability after normalization. This indicates enhanced harmonic characteristics and represents the harmonic energy level after energy focusing operation; The balance coefficient is a dynamic weighting factor with a value between 0 and 1. It is used to adjust the relative importance of phase parameters and harmonic characteristics in the final index in real time. This represents the phase sensitivity coefficient, used to adjust the response strength of the phase stability parameter in a nonlinear function, making it more sensitive to or smoother for phase fluctuations. It represents the harmonic gain coefficient, which is used to amplify or reduce the influence of harmonic characteristics, ensure that it matches the phase parameter in magnitude, and highlight the role of key harmonic components; The hyperbolic tangent function is used as a nonlinear activation function to compress the phase parameter to the range of (-1,1), preventing a single feature from dominating the entire index and introducing saturation characteristics to enhance the robustness of the system. This represents a logarithmic function, which performs a nonlinear transformation on the harmonic characteristics, compressing their dynamic range, highlighting the contribution of the main harmonic components, and suppressing the interference caused by abnormally high values.

[0100] It should be noted that, The phase stability parameter is used to determine the energy intensity of the enhanced harmonic characteristic by comparing the degree of phase fluctuation. A smaller phase fluctuation results in higher energy intensity. The value is reduced when the harmonic energy is strong. The values ​​are dynamically derived directly from the actual performance of the two types of features.

[0101] The setting is based on the actual fluctuation range of the standardized phase parameters; when the phase fluctuation range is large, the setting is increased. The value is chosen to increase the response strength; it is decreased when the fluctuation range is small. The values ​​are selected to smooth the response and are obtained through statistical analysis of historical fluctuation data of the phase stability parameter.

[0102] The value of the harmonic characteristic is determined based on the matching requirement between the energy level and the numerical range of the normalized phase parameter. When the harmonic energy level is too high, the value is reduced. The value increases when the magnitude is low. The value is calculated by comparing the difference in numerical magnitude between the two types of features.

[0103] This formula compresses the range of standardized phase parameters using a hyperbolic tangent function, adjusts the dynamic range of enhanced harmonic characteristics using a logarithmic function, and integrates the contributions of the two types of characteristics using a balance coefficient, ultimately generating a comprehensive stability index that can comprehensively reflect phase stability and harmonic energy levels.

[0104] As the standardized phase parameter increases, the output value of the hyperbolic tangent function gradually approaches the upper limit, causing the corresponding part to saturate the contribution to the final index; as the enhanced harmonic characteristic increases, the output value of the logarithmic function increases slowly, causing the corresponding part to steadily increase the contribution to the final index. As the value increases, the impact of the standardized phase parameter on the final performance index strengthens. When the frequency decreases, the effect of the enhanced harmonic characteristics increases.

[0105] In summary, the formula effectively captures the complex, nonlinear coupling relationship between phase stability and harmonic energy through the synergistic effect of nonlinear functions, producing stability and predictability superior to simple linear weighting.

[0106] In a preferred embodiment, when the overall stability index indicates that it will exceed a preset tolerance, an adaptive repair mechanism is pre-triggered, including:

[0107] Track the dynamic changes of the comprehensive stability index, identify its critical state near the preset tolerance, and generate an early warning trigger signal;

[0108] The repair resources are initialized based on the early warning trigger signal, and the response parameters of the repair strategy are adjusted to build an active repair framework.

[0109] Within the proactive repair framework, verify the expected effects of repair actions, select the optimal execution path, and output instances of pre-triggered repair mechanisms.

[0110] Specifically, the system continuously monitors the real-time changes in the comprehensive stability index, compares each collected value with the preset tolerance, delineates a fixed critical range below the tolerance threshold, and immediately triggers the signal generation module to output an early warning trigger signal when the index value enters this range and shows a continuous upward trend.

[0111] Upon receiving the warning trigger signal, the system's reserved hardware repair module and software repair program are activated. Based on the urgency of the critical state of the indicator, the execution speed and intensity of the repair actions are adjusted, and the initialized repair resources and adjusted response parameters are integrated to form a complete active repair framework.

[0112] In the proactive repair framework, the effects of different repair actions on the overall stability index are simulated. The speed and sustained stability of the index returning to the safe range after each action are compared. The execution path that can restore the index the fastest and maintain long-term stability is selected. The specific operation process and parameter configuration of this path are organized into a complete scheme, and an example of the pre-triggered repair mechanism is output.

[0113] In this embodiment, the characteristics of the current signal are matched with a historical fault evolution pattern library to predict the fault evolution trajectory and pre-select a repair strategy accordingly, including:

[0114] Extract key features of the current signal, compare them with typical patterns in the historical fault evolution pattern library, and generate the current fault feature vector;

[0115] Map the current fault feature vector to the time series of fault development, deduce its potential evolution path, and obtain a set of fault evolution paths;

[0116] For each path in the set of fault evolution paths, assess its corresponding repair requirements, configure an appropriate combination of repair strategies, and output a pre-selected set of repair strategies.

[0117] Specifically, key information such as the fluctuation amplitude, frequency change, and phase shift of the current signal is captured, and each of these is compared with the feature items of typical patterns in the historical fault evolution pattern library. Pattern elements that highly match the characteristics of the current signal are extracted and integrated to form the current fault feature vector.

[0118] The current fault feature vector is mapped to the timeline nodes of the fault development according to the chronological order. By referring to the subsequent evolution patterns of similar feature vectors in the historical pattern library, the possible state changes of the current fault at different time nodes are deduced, and multiple potential evolution directions are identified to obtain a set of fault evolution paths.

[0119] Analyze the failure development speed, severity and impact range of each path in the failure evolution path set one by one, clarify the required repair efforts and core repair objectives for each path, select suitable single or multiple strategies from the preset repair strategy library and combine them to ensure that the combined strategies can accurately deal with the failures of the corresponding paths, and output a pre-selected repair strategy set.

[0120] In summary, the pre-triggered adaptive repair mechanism identifies critical states by tracking the dynamic trends of comprehensive stability indicators, enabling early detection of fault precursors and generating warnings, thus addressing the issue of delayed warnings in existing technologies. Initializing repair resources and adjusting response parameters based on these warnings ensures timely availability of repair resources and adaptation of response parameters to critical states, rapidly constructing an active repair framework. Verifying the expected effects of repair actions and selecting the optimal path avoids ineffective repair operations, reduces resource waste, and ensures the output repair mechanism instance is efficient and feasible.

[0121] In the process of matching the historical fault evolution pattern library, the current signal features are extracted to generate vectors for comparison with historical patterns, thereby improving the accuracy of fault identification based on historical data. The feature vectors are mapped to time series to deduce the evolution path, enabling the prediction of fault development trends. For path evaluation needs, strategy combinations are configured to output a set of suitable pre-selected strategies, solving the problem of poor adaptability of fixed strategies and providing accurate direction for subsequent repairs.

[0122] In a preferred embodiment, a pre-selected repair strategy is executed, and repair parameters are dynamically optimized until the overall stability index stabilizes within a preset tolerance, thus completing the adaptive closed-loop repair, including:

[0123] Invoke the target strategy from the pre-selected repair strategy set, configure the corresponding repair execution unit, and generate the initial repair execution framework;

[0124] The repair actions are performed within the initial repair execution framework, while the real-time response of the comprehensive stability index is monitored to obtain feedback data of the repair process;

[0125] The repair intensity and duration are adjusted based on feedback data from the repair process, and an optimized set of repair parameters is output.

[0126] Specifically, a target strategy that fits the current fault evolution path is selected from the pre-selected repair strategy set. The hardware execution module and software control flow corresponding to the strategy are defined. Each module is connected according to the strategy requirements, dedicated execution resources are allocated, the module startup order and coordination logic are determined, and a complete and directly executable initial repair execution framework is formed.

[0127] In the initial repair execution framework, all repair execution units are started and repair operations are carried out step by step according to the established process. At the same time, real-time values ​​of comprehensive stability indicators are continuously collected through dedicated detection components, and the rise, fall and fluctuation of indicators as repair actions are recorded. These real-time monitoring information are fully collected to obtain feedback data of the repair process.

[0128] Analyze the changing trends of comprehensive stability indicators in the feedback data of the repair process. If the indicators rapidly approach the preset tolerance, reduce the repair intensity and shorten the action time. If the indicators improve slowly, increase the repair intensity and extend the action time. Dynamically adjust to the optimal state based on the real-time response of the indicators. Compile and summarize the final repair intensity level and action time standards, and output the optimized repair parameter set.

[0129] In this embodiment, a pre-selected repair strategy is executed, and repair parameters are dynamically optimized until the overall stability index stabilizes within a preset tolerance, thus completing the adaptive closed-loop repair. The method also includes:

[0130] Deep repair is performed based on the optimized repair parameter set to verify the stability characteristics of the repaired signal and generate repair verification results.

[0131] The repair verification results are compared with the preset tolerance to confirm the degree of recovery of the comprehensive stability index and obtain a stability recovery assessment.

[0132] Based on the stability recovery assessment, the closed-loop control of the repair process is completed, the historical fault evolution mode library is updated, and a repair completion signal is output.

[0133] Specifically, based on the repair intensity and duration standards in the optimized repair parameter set, all repair execution units are activated to continuously perform deep repair operations. Through dedicated detection components, stability characteristics such as the fluctuation amplitude, phase stability, and harmonic energy distribution of the repaired signal are captured, and the specific manifestations of these characteristics are fully recorded to generate repair verification results.

[0134] The specific values ​​of the comprehensive stability indicators included in the repair verification results are compared one by one with the range of the preset tolerance to determine whether the indicators fall completely within the preset tolerance, to determine the specific extent to which the indicators have recovered from the critical state to the safe level, to determine whether the recovery effect meets expectations, and to obtain a stability recovery assessment.

[0135] If the stability recovery assessment shows that the indicators have stabilized within the preset tolerance, the repair process is terminated; if the indicators are not met, the optimized repair parameter set is called again based on the assessment results to repeat the deep repair. At the same time, the characteristics of this fault, the repair strategy adopted, the optimized parameters and the final repair results are compiled and archived and added to the historical fault evolution mode library. Finally, the signal generation module is started to output the repair completion signal.

[0136] In summary, calling the target strategy configuration execution unit to generate the initial framework can quickly start the repair process and avoid disordered operations; during execution, indicators are monitored in real time and feedback data is obtained, providing real-time basis for parameter optimization and solving the problem that existing technologies have fixed repair parameters and cannot adapt to dynamic faults.

[0137] Adjusting the repair intensity and duration based on feedback data and outputting an optimized parameter set allows repair parameters to accurately match the fault status, avoiding resource waste or insufficient repair efforts and improving repair efficiency.

[0138] By performing deep repair based on optimized parameters and verifying stability, we can ensure thorough repair and avoid superficial compliance. By comparing the verification results to evaluate the degree of recovery, we can accurately confirm whether the indicators are stable within the tolerance range and prevent the repair from failing to meet the standards.

[0139] Closed-loop control combined with historical database updates not only completes the current repair loop but also accumulates data for subsequent fault matching and strategy selection, improving the accuracy and efficiency of long-term repairs and enhancing the system's ability to operate continuously and reliably.

[0140] Example 2, as Figure 2 The diagram shown is a functional block diagram of a data processing clock fault self-diagnosis and repair system provided in an embodiment of the present invention.

[0141] This invention discloses a data processing clock fault self-diagnosis and repair system 100, which can be installed in an electronic device. Depending on the functions implemented, the data processing clock fault self-diagnosis and repair system 100 may include a signal processing module 101, an indicator generation module 102, a mechanism triggering module 103, a strategy pre-selection module 104, and a closed-loop repair module 105. The module of this invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and are stored in the memory of the electronic device.

[0142] In this embodiment, the functions of each module / unit are as follows:

[0143] Signal processing module 101 is used to acquire the master clock signal and the reference clock signal, and extract the instantaneous phase difference sequence and residual harmonic components of the master clock signal based on the reference clock signal;

[0144] The index generation module 102 is used to integrate the statistical distribution characteristics of the instantaneous phase difference sequence with the energy of the residual harmonic components to generate a comprehensive stability index.

[0145] Mechanism triggering module 103 is used to pre-trigger the adaptive repair mechanism when the comprehensive stability index indicates that it will exceed the preset tolerance.

[0146] The strategy pre-selection module 104 is used to match the characteristics of the current signal with the historical fault evolution pattern library, predict the fault evolution trajectory, and pre-select a repair strategy accordingly.

[0147] The closed-loop repair module 105 is used to execute the pre-selected repair strategy and dynamically optimize the repair parameters until the overall stability index is stable within the preset tolerance, thus completing the adaptive closed-loop repair.

[0148] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0149] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0150] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0151] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0152] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0153] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A self-diagnostic and repair method for data processing clock faults, characterized in that, The method includes: S1. Obtain the master clock signal and the reference clock signal. Based on the reference clock signal, extract the instantaneous phase difference sequence and residual harmonic components of the master clock signal. S2. Extract the statistical distribution characteristics of the instantaneous phase difference sequence, evaluate its central tendency and dispersion, and generate phase stability parameters; Analyze the energy distribution of residual harmonic components, identify the energy concentration region of the dominant harmonic component, and obtain the harmonic energy characteristics; The phase stability parameters are normalized to unify the parameter benchmark and generate standardized phase parameters. An energy focusing operation is performed on the harmonic energy characteristics to adjust the energy distribution ratio and obtain enhanced harmonic characteristics; The standardized phase parameters are coupled with the enhanced harmonic features, and the two types of features are nonlinearly combined by a dynamic weighting factor to generate an optimized comprehensive stability index. S3. When the overall stability index indicates that it will exceed the preset tolerance, the adaptive repair mechanism is pre-triggered. S4. Match the characteristics of the current signal with the historical fault evolution pattern library to predict the fault evolution trajectory and pre-select the repair strategy accordingly. S5. Execute the pre-selected repair strategy and dynamically optimize the repair parameters until the overall stability index stabilizes within the preset tolerance, thus completing the adaptive closed-loop repair.

2. The data processing clock fault self-diagnosis and repair method as described in claim 1, characterized in that, The extraction of the instantaneous phase difference sequence of the master clock signal includes: The timing reference of the master clock signal is calibrated based on the reference clock signal to generate a timing-aligned master clock signal; The phase fluctuations of the timing-aligned master clock signal are tracked and its transmission path delay is compensated to obtain the phase fluctuation characteristics. The instantaneous phase difference sequence is derived from the phase fluctuation characteristics, and the sensitivity of the detection process is adaptively adjusted to output the optimized instantaneous phase difference sequence.

3. The data processing clock fault self-diagnosis and repair method as described in claim 2, characterized in that, The extraction of residual harmonic components from the master clock signal includes: Dynamic noise suppression is applied to the master clock signal, and its amplitude is balanced with reference to the optimized instantaneous phase difference sequence to generate a preprocessed clock signal. The fundamental and harmonic components are separated from the preprocessed clock signal, and the energy distribution of the harmonics is analyzed to obtain a harmonic component dataset. Extract the target residual harmonic components from the harmonic component dataset, optimize the judgment conditions of the extraction process, and output accurate residual harmonic components.

4. The data processing clock fault self-diagnosis and repair method as described in claim 1, characterized in that, The pre-triggered adaptive repair mechanism when the comprehensive stability index indicates that it will exceed the preset tolerance includes: Track the dynamic changes of the comprehensive stability index, identify its critical state near the preset tolerance, and generate an early warning trigger signal; The repair resources are initialized based on the early warning trigger signal, and the response parameters of the repair strategy are adjusted to build an active repair framework. Within the proactive repair framework, verify the expected effects of repair actions, select the optimal execution path, and output instances of pre-triggered repair mechanisms.

5. The data processing clock fault self-diagnosis and repair method as described in claim 4, characterized in that, The step of matching the characteristics of the current signal with a historical fault evolution pattern library to predict the fault evolution trajectory and pre-select a repair strategy accordingly includes: Extract key features of the current signal, compare them with typical patterns in the historical fault evolution pattern library, and generate the current fault feature vector; Map the current fault feature vector to the time series of fault development, deduce its potential evolution path, and obtain a set of fault evolution paths; For each path in the set of fault evolution paths, assess its corresponding repair requirements, configure an appropriate combination of repair strategies, and output a pre-selected set of repair strategies.

6. The data processing clock fault self-diagnosis and repair method as described in claim 5, characterized in that, The process of executing a pre-selected repair strategy and dynamically optimizing repair parameters until the overall stability index stabilizes within a preset tolerance, thus completing the adaptive closed-loop repair, includes: Invoke the target strategy from the pre-selected repair strategy set, configure the corresponding repair execution unit, and generate the initial repair execution framework; The repair actions are performed within the initial repair execution framework, while the real-time response of the comprehensive stability index is monitored to obtain feedback data of the repair process; The repair intensity and duration are adjusted based on feedback data from the repair process, and an optimized set of repair parameters is output.

7. The data processing clock fault self-diagnosis and repair method as described in claim 6, characterized in that, The process of executing a pre-selected repair strategy and dynamically optimizing repair parameters until the overall stability index stabilizes within a preset tolerance, thus completing the adaptive closed-loop repair, also includes: Deep repair is performed based on the optimized repair parameter set to verify the stability characteristics of the repaired signal and generate repair verification results. The repair verification results are compared with the preset tolerance to confirm the degree of recovery of the comprehensive stability index and obtain a stability recovery assessment. Based on the stability recovery assessment, the closed-loop control of the repair process is completed, the historical fault evolution mode library is updated, and a repair completion signal is output.

8. A self-diagnostic and repair system for data processing clock faults, characterized in that, The system includes: The signal processing module is used to acquire the master clock signal and the reference clock signal, and extract the instantaneous phase difference sequence and residual harmonic components of the master clock signal based on the reference clock signal. The index generation module is used to extract the statistical distribution characteristics of the instantaneous phase difference sequence, evaluate its central tendency and dispersion, and generate phase stability parameters; analyze the energy distribution of residual harmonic components, identify the energy concentration region of the dominant harmonic component, and obtain harmonic energy characteristics; normalize the phase stability parameters, unify the parameter benchmark, and generate standardized phase parameters; perform energy focusing operation on the harmonic energy characteristics, adjust the energy distribution ratio, and obtain enhanced harmonic characteristics; couple the standardized phase parameters and enhanced harmonic characteristics, and perform nonlinear combination of the two types of characteristics after coupling through dynamic weighting factors to generate an optimized comprehensive stability index. The mechanism triggering module is used to pre-trigger the adaptive repair mechanism when the comprehensive stability index indicates that it will exceed the preset tolerance. The strategy pre-selection module is used to match the characteristics of the current signal with the historical fault evolution pattern library, predict the fault evolution trajectory, and pre-select the repair strategy accordingly. The closed-loop repair module is used to execute the pre-selected repair strategy and dynamically optimize the repair parameters until the overall stability index stabilizes within the preset tolerance, thus completing the adaptive closed-loop repair.

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