Intelligent evaluation method and system for operation state of primary and secondary fusion circuit breaker

Abstract

The invention discloses an intelligent evaluation method and system for the operation state of a primary and secondary fusion circuit breaker. The method comprises the following steps: establishing a'introspection data channel 'and an'appearance data channel' which are parallel; space-time alignment of dual-channel data is achieved through physical synchronization anchor points; on the basis, the consistency of the self-cognition information and the independent external observation information of the intelligent unit is quantitatively evaluated from four dimensions of measurement, diagnosis, decision making and time; the consistency indexes of the four dimensions are fused to obtain a comprehensive cognitive credit score (CCS), and credibility grades are divided according to the CCS; and fault root cause tracing is carried out based on a consistency degradation mode, and an accurate predictive maintenance decision is generated. Through four-dimensional consistency evaluation of measurement, diagnosis, decision and time, the method not only covers a traditional physical state, but also deeply evaluates the reliability of core functions (perception, analysis, decision and recording) of an intelligent unit, and realizes integral evaluation of a primary system and a secondary system.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power equipment state monitoring and intelligent evaluation, and particularly relates to a primary and secondary fusion circuit breaker operation state intelligent evaluation method and system. BACKGROUND

[0002] The circuit breaker is a crucial control and protection device in the power system, and its operation reliability is directly related to the safety and stability of the power grid. With the development of smart grids, the primary and secondary fusion circuit breaker has become the mainstream equipment of smart substations. It deeply integrates the traditional circuit breaker with intelligent units (such as merging units, intelligent terminals) based on microprocessors and digital communication, realizing measurement digitization, control networking, state visualization, and function integration.

[0003] The operation state evaluation of the primary and secondary fusion circuit breaker is the key to carrying out predictive maintenance and avoiding unplanned shutdown. At present, the research and practice in this field at home and abroad mainly focus on the following aspects: Evaluation based on single or multiple types of online monitoring data: by deploying various sensors, the mechanical characteristics (such as opening and closing time, speed, vibration) of the circuit breaker, electrical characteristics (such as loop resistance, contact temperature, SF6 gas state), insulation characteristics (such as partial discharge), etc. are monitored online. The evaluation methods are mainly threshold comparison, trend analysis or simple feature extraction and pattern recognition. For example, by analyzing the time-frequency characteristics of the vibration signal to identify the mechanical failure of the operating mechanism; by monitoring the concentration of SF6 gas decomposition products to evaluate the degree of insulation deterioration. These methods are essentially online traditional offline test projects, and the evaluation object is mainly the "primary part" (i.e. physical entity) of the circuit breaker, and the evaluation paradigm is "external observation-feature extraction-state scoring".

[0004] Evaluation based on intelligent unit self-diagnosis information: using the self-diagnosis function of the intelligent unit in the primary and secondary fusion equipment, the hardware state (such as power supply, CPU, memory), software state, communication state, etc. information is obtained. This method directly reflects the health status of the "secondary part", which is not possessed by the traditional circuit breaker. However, current practice usually processes these self-diagnosis information as independent alarm signals, or performs simple statistical analysis, and does not deeply correlate and verify them with the physical state of the primary equipment.

[0005] Data-driven intelligent assessment model: In recent years, with the development of artificial intelligence technology, some researches attempt to use machine learning and deep learning algorithms to analyze monitoring data to build more accurate health assessment models. For example, multi-source data such as vibration, temperature, and current are used as input, and a neural network model is used to output a health index. This method has advantages in feature fusion and complex pattern recognition, but the "black box" nature of the model leads to poor interpretability, and the training of the model relies heavily on massive fault samples, while power equipment fault samples are scarce, and the model generalization ability is challenged.

[0006] Defects and deficiencies of the prior art: Evaluation object is one-sided: Most existing methods focus on the physical state of the primary part of the circuit breaker (body) or the self-checking state of the secondary part (intelligent unit) in isolation, lacking a perspective of evaluating the primary and secondary systems as an organic whole. It is not realized that in the primary and secondary fusion architecture, the intelligent unit is not only an executive mechanism, but also a "first recognizer" of the device state and a "decision-making brain".

[0007] Evaluation dimension is missing: The existing evaluation system lacks evaluation of the reliability of the intelligent unit's "cognitive ability" itself. A serious problem is that the intelligent unit's "cognition" (i.e., its measurement, diagnosis, and decision-making) of itself or of the primary device state may be incorrect, delayed, or incomplete, and traditional methods cannot detect this "cognitive distortion". For example, the intelligent unit reports that the current sampling value is normal, but the actual transformer has an implicit fault; or the primary device has a mechanical anomaly, but the intelligent unit's self-diagnosis system is unresponsive.

[0008] Single data source and lack of verification: Most methods rely on a single data source, either external sensors or intelligent unit self-reports. There is a lack of an independent and reliable reference system to verify the authenticity of the information provided by the intelligent unit. Without verification, there is no way to assess the credibility of its cognition. Weak fault tracing capability: When the evaluation finds state degradation, existing methods have difficulty in precisely locating the problem as originating from the primary device, the secondary device, or the interface and interaction process between the two, resulting in weak targetedness of maintenance decisions.

[0009] Therefore, a new evaluation paradigm is urgently needed to fundamentally solve the above problems and achieve accurate and reliable evaluation of the overall operation state of the primary and secondary fusion circuit breaker, especially its "cognition-execution" integrated capability. SUMMARY

[0010] The present application aims to overcome the deficiencies of the prior art and provide a primary and secondary fusion circuit breaker operation state intelligent evaluation method and system to comprehensively evaluate the reliability and safety of its operation state and achieve accurate fault tracing.

[0011] To achieve the above object, the present application adopts the following technical solutions: A running state intelligent evaluation method of primary-secondary fusion circuit breaker, comprising the following steps: S1: Establishing parallel introspection data channel and appearance data channel; the introspection data channel obtains self-awareness, self-diagnosis and decision information of the circuit breaker intelligent unit by listening and analyzing internal data; the appearance data channel obtains physical quantity, mechanical quantity, thermal quantity and insulation state quantity homologous or associated with the observation of the intelligent unit through the sensor network independently deployed in the circuit breaker body; S2: Taking the circuit breaker closing and opening coil current pulse as a physical synchronization anchor point, the heterogeneous data collected by the introspection data channel and the appearance data channel are time-synchronized and spatially aligned; S3: Based on the aligned double-channel data, the consistency between the self-awareness information of the intelligent unit and the objective observation information of the independent sensor network is quantitatively evaluated from four dimensions of measurement, diagnosis, decision and time, and the consistency index of each dimension is obtained; S4: Fusing the consistency index of each dimension, calculating the comprehensive cognitive credit score of the primary-secondary fusion circuit breaker, and evaluating the credibility level of its running state based on the score; S5: When the cognitive credit score is lower than the preset threshold or shows a downward trend, the fault root cause is traced based on the degradation mode of the consistency index of each dimension, and a targeted predictive maintenance decision is generated.

[0012] The present application further provides that the specific method of time synchronization in step S2 is to record the first time mark of the physical synchronization anchor point event in the introspection channel and the second time mark in the appearance channel, to build a time alignment model between the double channels by continuously calibrating the deviation of the first time mark and the second time mark, and to realize microsecond-level data synchronization.

[0013] The present application further provides that the specific method of measurement consistency evaluation in step S3 is to calculate the relative error of the introspection channel measurement value and the appearance channel observation value for the same measured quantity; to dynamically generate a reasonable error confidence interval at this moment based on the historical working condition data of the equipment; and the measurement consistency index is the probability that the relative error falls within the reasonable error confidence interval.

[0014] The present application further provides that the specific method of diagnosis consistency evaluation in step S3 is to run an independent external diagnosis model based on the appearance channel data to obtain a first diagnosis conclusion; to analyze the self-diagnosis information of the intelligent unit in the introspection channel to obtain a second diagnosis conclusion; to perform semantic mapping and logical association on the first diagnosis conclusion and the second diagnosis conclusion to evaluate the degree of agreement of both on the same fault root cause, and to obtain the diagnosis consistency index.

[0015] The present invention further specifies that the specific method for decision consistency assessment in step S3 is as follows: when the introspection channel records a protection action or control command, the time and logical basis of the decision are traced back; high-precision physical quantity data at the same time are extracted from the external channel to independently verify whether the physical conditions on which the decision basis depends are valid; statistical analysis is performed based on the sufficiency of the basis of historical decision events to obtain the decision consistency index.

[0016] The present invention further specifies that the specific method for time consistency evaluation in step S3 is as follows: based on the physical synchronization anchor point, compare the time scale of the entire link event sequence of "command issuance - coil excitation - mechanical action - status feedback" in the introspection channel and the appearance channel; analyze the conformity and stability of the time interval of each link to obtain the time consistency index.

[0017] The present invention further specifies that the calculation formula for the cognitive credit score in step S4 is as follows: ,in , , , These are measurement, diagnosis, decision-making, and time consistency indices, respectively, with weighting coefficients α, β, γ, and δ dynamically allocated based on the functional emphasis of the circuit breaker in the system.

[0018] The present invention further includes the following steps in step S5: tracing the root cause of the fault, which includes: distinguishing the fault origin from internal functional abnormalities of the intelligent unit, deterioration of the primary components of the circuit breaker, or abnormalities in the interface and interaction between the two, based on the combined degradation patterns of the consistency indices of each dimension; and associating specific consistency degradation characteristics with potential defects in hardware boards, software algorithms, or mechanical components.

[0019] The present invention also provides an evaluation system for implementing the method, comprising: A dual-channel data acquisition module is used to establish and synchronize introspection data channels and appearance data channels; The consistency quantification evaluation engine is used to calculate the consistency index across four dimensions: measurement, diagnosis, decision-making, and time. The cognitive credit comprehensive scoring module is used to integrate consistency indices from various dimensions and output a cognitive credit score and credibility level. The intelligent tracing and decision-making module is used to perform root cause analysis based on consistent degradation patterns and generate maintenance recommendations. The human-computer interaction interface is used to display cognitive radar charts, credit score historical trajectories, and assessment reports.

[0020] The present invention also provides a computer-readable storage medium having a computer program stored thereon, characterized in that the program, when executed by a processor, implements the intelligent evaluation method for the operating status of the primary and secondary integrated circuit breaker.

[0021] The beneficial effects of this invention are as follows: Through a four-dimensional consistency assessment encompassing measurement, diagnosis, decision-making, and time, it not only covers traditional physical states but also deeply evaluates the reliability of the core functions (sensing, analysis, decision-making, and recording) of intelligent units, achieving a holistic evaluation of primary and secondary systems. By monitoring the decline in "cognitive credibility," it can detect functional degradation of the intelligent system itself or early anomalies of primary equipment before physical parameters deteriorate significantly. The dual-channel comparison mechanism enables precise fault location (distinguishing between primary, secondary, or interactive problems); enhancing the proactive defense capabilities of the power grid: This method can effectively identify high-risk equipment, providing dispatching and operation personnel with deeper equipment risk information and supporting the proactive safety defense of the power grid. Attached image description: Figure 1 This is a flowchart illustrating an embodiment of the present invention. Detailed Implementation

[0022] The embodiments of this application will be described in detail below, providing a clear and complete description of the technical solutions within this application. Obviously, the described embodiments are merely a portion of the embodiments of this application, and not all of them. The components of this application described and shown herein can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0023] like Figure 1 As shown, the present invention provides an intelligent evaluation method for the operating status of a primary and secondary integrated circuit breaker, which specifically includes the following steps: S1: Establishing parallel introspection data channels and appearance data channels.

[0024] The introspective data channel uses technical means (such as network bypass monitoring, internal bus reading, and mirroring ports) to non-intrusively monitor and analyze the internal data streams of circuit breaker intelligent units (intelligent terminals, merging units, etc.) in real time. The acquired data includes: Self-sensing data comes from electrical quantity measurements (SV / MV messages) such as current, voltage, power, and frequency, which are published or calculated by the intelligent unit.

[0025] Self-diagnostic information comes from intelligent unit hardware self-test status words (such as power failure, AD sampling failure, memory error), software running status flags, event logs, and fault record codes.

[0026] Decision and action information comes from protection start signals, protection action output information, switch opening and closing control commands (GOOSE messages) and their related logical judgment flags (such as overcurrent setting trigger flags).

[0027] Endogenous timestamps are internal timestamps attached to the aforementioned data via intelligent units.

[0028] The appearance data channel utilizes a dedicated, miniaturized, low-power sensor network deployed at key locations on the circuit breaker body, independent of the original sensing system of the intelligent unit. The acquired data includes: Synchronous electrical quantities: High-precision Rogowski coils or miniature magnetoresistive sensors are used to directly measure the main circuit current of each phase and the opening and closing coil current, which are from the same source as the intelligent unit transformer. The opening and closing coil current serves as a key physical synchronization anchor point.

[0029] Multimodal mechanical signals: Vibration sensor arrays based on microelectromechanical systems (MEMS) are distributed and installed in parts such as the operating mechanism housing, arc-extinguishing chamber shell, and transmission linkage to collect vibration acceleration signals during operation.

[0030] Global temperature field: Using distributed fiber optic temperature sensing (DTS) technology or wireless temperature sensing network, the temperature distribution of key parts of the circuit breaker (such as contacts, conductor connections, and mechanism box) is obtained.

[0031] Gas and insulation status: Integrating a miniature spectral analysis sensor chip to monitor in situ the pressure, purity, trace water content, and concentration of characteristic decomposition products (such as SO2, H2S) of SF6 gas; deploying ultra-high frequency (UHF) sensors to monitor partial discharge signals.

[0032] External precision time stamp: All data in this channel is provided with a unified and reliable external time stamp by an independent high-precision time synchronization module (such as a high-stability temperature-controlled crystal oscillator tamed by BeiDou / GPS).

[0033] S2: Spatiotemporal alignment of dual-channel heterogeneous data.

[0034] The aforementioned physical synchronization anchor point (such as the rising edge of the trip coil current pulse) is used as the alignment reference event. The occurrence time of this event is captured from both the introspection channel (such as the control command issuance time stamp) and the external channel (coil current pulse time stamp). By continuously learning and calibrating the deviation sequence of these two moments, a dynamic "internal-external observation time offset model" is constructed to calibrate all data from the two channels onto a unified physical time axis, achieving microsecond-level time synchronization and laying a rigorous foundation for subsequent consistency comparisons.

[0035] S3: Four-dimensional consistency quantitative assessment.

[0036] Based on the spatiotemporally aligned dual-channel data, the consistency between the intelligent unit's self-perception and objective reality is quantified from the following four dimensions: Measurement consistency assessment: Object: For the same measured physical quantity (such as the effective value of phase A current), compare the measured values ​​of the intelligent unit. Compared with the observation values ​​of the external channel The relative error is calculated based on the two observations mentioned above: .

[0037] Dynamic threshold: Unlike fixed thresholds, this invention introduces Bayesian estimation based on historical equipment operating conditions (load, ambient temperature) and current operating status to dynamically generate a reasonable error confidence interval [L(t), U(t)] for each moment; where the measurement consistency index... Defined as the relative error over the evaluation period T The probability of falling within the dynamic reasonable range: ,in As an indicator function, the long-term accuracy and stability of the smart unit's sensing link are evaluated through measurement consistency.

[0038] For diagnostic consistency assessment: It targets the comparison between the self-diagnostic conclusions of the introspective channel and the external independent diagnostic conclusions based on the appearance channel data.

[0039] The method employed involves utilizing multi-source data such as vibration, temperature, and gas from the external monitoring channel to run an independent AI diagnostic model (e.g., a neural network incorporating physics knowledge). This model outputs diagnostic results for the mechanical, insulation, and electrical conditions of the circuit breaker (e.g., "Slight jamming risk in phase B mechanism transmission," "SF6 gas moisture content close to warning level"). The self-diagnostic information from the intelligent unit (e.g., "AD sampling verification error," "GOOSE chain break") is semantically mapped and logically correlated with external diagnostic conclusions. Diagnostic consistency index. The consistency is assessed by calculating the degree of agreement between the conclusions drawn by both external diagnostics and the intelligent unit in describing the same physical entity or functional state. For example, if external diagnostics detects a mechanical abnormality but the intelligent unit does not issue any related alarms, the consistency is low; if the intelligent unit reports "abnormal current sampling" and external diagnostics also indicate "abnormal conduction circuit parameters," the consistency is high. This index can be quantified using rule-based matching or similarity calculation methods to directly evaluate the sensitivity, coverage, and reliability of the intelligent unit's self-diagnostic function, revealing its "cognitive blind spots."

[0040] Decision consistency assessment: The evaluation focuses on whether the decision-making basis of the intelligent unit matches the externally observed physical conditions when the circuit breaker performs protective tripping or remote opening and closing. The method is as follows: when the introspection channel captures a protection action or control command output event, the logical judgment process of the decision and the measurement data it relies on are traced back.

[0041] High-precision, high-reliability electrical quantity data (time-aligned) are extracted from the external channel at the same moment to independently verify whether the decision conditions are truly met (e.g., whether the current actually exceeds the set value, whether the voltage actually drops). Decision Consistency Index It is based on statistical analysis of all decision-making events over a period of time, and the calculation formula is: in, The number of times the decision-making basis has been externally verified as correct; This represents the number of times the action conditions were met by external verification but the intelligent unit failed to take action (weighted). (Usually less than 0, indicating a refusal to act penalty). This represents the total number of events assessed. It also assesses the alignment between decision delays and externally observed physical processes, evaluating the reliability and correctness of protection and control decisions.

[0042] Time consistency assessment evaluates the consistency between the timing of events within the intelligent unit, the SOE (Sequence of Events), and the sequence of external physical events. Using physical synchronization anchors, the timelines of key events such as "command generation -> output relay action -> coil excitation -> mechanical start -> position feedback" in the internal and external channels are accurately reconstructed. The time intervals between each stage are analyzed. The values ​​and probability distributions of the time consistency index are compared with historical benchmarks or expected values ​​from physical models. The stability of key timing intervals (such as the reciprocal of variance) and the accuracy of SOE timestamps can be comprehensively quantified. Its purpose is to evaluate the internal clock synchronization quality of the intelligent unit, the stability of logic processing delays, and the accuracy of SOE functionality. Timing anomalies are often precursors to complex system failures.

[0043] S4: Cognitive Credit Comprehensive Score and Status Classification.

[0044] By integrating the consistency indices of the above four dimensions, the comprehensive cognitive credit score of this primary and secondary integrated circuit breaker is calculated: ,in , , , These are the consistency indices for measurement, diagnosis, decision-making, and time. α+β+γ+δ=1. These weights can be dynamically or preset based on factors such as the circuit breaker's specific role in the power grid (e.g., whether it focuses more on protection or control), voltage level, and historical importance.

[0045] Based on the CCS score, the reliability of the circuit breaker's operating status is divided into different levels, for example: Trustworthy: CCS ≥ 0.8. Cognition is reliable; the equipment is in a highly trustworthy state. Critical: 0.6 ≤ CCS < 0.8. Cognition shows some bias; closer attention is needed. Suspicious: 0.4 ≤ CCS < 0.6. Cognition has significant problems; reliability is questionable. Untrustworthy: CCS < 0.4. Cognition is severely distorted; the equipment is untrustworthy and carries high risk.

[0046] Generate dynamic cognitive profiles (such as four-dimensional radar charts) to intuitively display the strength of consistency in each dimension, and plot historical CCS change curves to identify credit decay trends.

[0047] S5: Root cause analysis and predictive maintenance decisions.

[0048] When CCS decreases or is at a low level, initiate in-depth analysis: Pattern recognition and root cause localization: This involves analyzing which consistency dimensions(s) are deteriorating, and combining this with the specific characteristics of the deterioration (such as measurement error bias, type of diagnostic gaps, stages of decision delay, and nodes of temporal disorder) to pinpoint the possible root causes of the problem. For example: Individual dropout: This could indicate a problem with the current transformer, sampling loop, or ADC module of the smart unit.

[0049] A single drop indicates a defect in the intelligent unit's self-diagnostic algorithm or an improperly set threshold.

[0050] and Decreased correlation: This strongly suggests a defect in the hardware (relay, optocoupler) of the intelligent unit's output circuit.

[0051] A simultaneous decrease in multiple dimensions and abnormal appearance data may indicate a serious primary equipment malfunction affecting the secondary system.

[0052] Generate predictive maintenance strategies: Based on the CCS level and root cause analysis results, differentiated maintenance recommendations are output. For example, for a "critical" state and the root cause is... If the condition is low, it is recommended to "shorten the evaluation cycle and upgrade the intelligent unit's self-diagnostic algorithm during the next maintenance"; if the condition is "suspicious" and the root cause is an issue with the output circuit, it is recommended to "arrange a power outage as soon as possible to inspect the intelligent terminal's output board".

[0053] The strategy is specifically targeted at either "enhancing cognitive credibility" or "fixing physical defects" to achieve precise maintenance.

[0054] If certain terms are used in the specification and claims to refer to specific components, those skilled in the art will understand that hardware manufacturers may use different names to refer to the same component. This specification and claims do not distinguish components based on differences in name, but rather on differences in function. The term "comprising" as used throughout the specification and claims is an open-ended term and should be interpreted as "comprising but not limited to." "Approximately" means that within an acceptable margin of error, those skilled in the art can solve the technical problem and substantially achieve the technical effect within a certain margin of error.

[0055] It should be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a product or system comprising a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a product or system. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the product or system that includes that element.

[0056] The foregoing description illustrates and describes several preferred embodiments of the present invention. However, as previously stated, it should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the inventive concept by means of the foregoing teachings or techniques or knowledge in related fields. Any modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims.

Claims

1. A method for intelligent evaluation of the operating status of a primary and secondary integrated circuit breaker, characterized in that, Includes the following steps: S1: Establish parallel introspection data channels and appearance data channels; the introspection data channel obtains the self-sensing, self-diagnosis and decision-making information of the circuit breaker intelligent unit by listening to and parsing its internal data; the appearance data channel obtains physical quantities, mechanical quantities, thermal quantities and insulation state quantities that are the same as or related to the observations of the intelligent unit through a sensor network independently deployed on the circuit breaker body. S2: Using the circuit breaker opening and closing coil current pulse as the physical synchronization anchor point, the heterogeneous data collected by the introspection data channel and the appearance data channel are synchronized in time and aligned in space. S3: Based on the aligned dual-channel data, the consistency between the self-awareness information of the intelligent unit and the objective observation information of the independent sensor network is quantitatively evaluated from four dimensions: measurement, diagnosis, decision-making and time, to obtain the consistency index of each dimension. S4: Integrate the consistency indices of each dimension to calculate the comprehensive cognitive credit score of the primary and secondary integrated circuit breaker, and evaluate the credibility level of its operating status based on the score; S5: When the cognitive credit score is lower than the preset threshold or shows a downward trend, the root cause of the failure is traced based on the deterioration pattern of the consistency index of each dimension, and targeted predictive maintenance decisions are generated.

2. The method according to claim 1, characterized in that, The specific method for time synchronization in step S2 is as follows: record the first timescale of the physical synchronization anchor event in the introspection channel and the second timescale in the appearance channel respectively. By continuously calibrating the deviation between the first timescale and the second timescale, a time alignment model between the two channels is constructed to achieve microsecond-level data synchronization.

3. The method according to claim 1, characterized in that, The specific method for measurement consistency assessment in step S3 is as follows: for the same measurand, calculate the relative error between the introspection channel measurement value and the appearance channel observation value; dynamically generate a reasonable error confidence interval at that moment based on the equipment's historical operating data; the measurement consistency index is the probability that the relative error falls within the reasonable error confidence interval.

4. The method according to claim 1, characterized in that, The specific method for diagnostic consistency assessment in step S3 is as follows: run an independent external diagnostic model using the appearance channel data to obtain a first diagnostic conclusion; parse the intelligent unit self-diagnostic information in the introspection channel to obtain a second diagnostic conclusion; perform semantic mapping and logical association between the first diagnostic conclusion and the second diagnostic conclusion, evaluate the degree of consistency between the two on the same root cause of the fault, and obtain a diagnostic consistency index.

5. The method according to claim 1, characterized in that, The specific method for decision consistency assessment in step S3 is as follows: when the introspection channel records a protection action or control command, the timing and logical basis of the decision are traced back; high-precision physical quantity data at the same moment are extracted from the external channel to independently verify whether the physical conditions on which the decision basis depends are valid; statistical analysis is performed based on the sufficiency of the basis of historical decision events to obtain the decision consistency index.

6. The method according to claim 1, characterized in that, The specific method for time consistency assessment in step S3 is as follows: based on the physical synchronization anchor point, compare the time scales of the entire link event sequence of "command issuance - coil excitation - mechanical action - status feedback" in the introspection channel and the appearance channel; analyze the conformity and stability of the time interval of each link to obtain the time consistency index.

7. The method according to claim 1, characterized in that, The formula for calculating the cognitive credit score in step S4 is as follows: ,in , , , These are measurement, diagnosis, decision-making, and time consistency indices, respectively, with weighting coefficients α, β, γ, and δ dynamically allocated based on the functional emphasis of the circuit breaker in the system.

8. The method according to claim 1, characterized in that, The fault root cause tracing in step S5 includes: distinguishing the fault origin from internal functional abnormalities of the intelligent unit, the deterioration of the primary component status of the circuit breaker, or the abnormality of the interface and interaction between the two, based on the combined degradation mode of the consistency index of each dimension; and associating specific consistency degradation characteristics with potential defects in hardware boards, software algorithms, or mechanical components.

9. An evaluation system for implementing the method according to any one of claims 1-8, characterized in that, include: A dual-channel data acquisition module is used to establish and synchronize introspection data channels and appearance data channels; The consistency quantification evaluation engine is used to calculate the consistency index across four dimensions: measurement, diagnosis, decision-making, and time. The cognitive credit comprehensive scoring module is used to integrate consistency indices from various dimensions and output a cognitive credit score and credibility level. The intelligent tracing and decision-making module is used to perform root cause analysis based on consistent degradation patterns and generate maintenance recommendations. The human-computer interaction interface is used to display cognitive radar charts, credit score historical trajectories, and assessment reports.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the intelligent evaluation method for the operating status of the primary and secondary integrated circuit breaker as described in any one of claims 1-8.

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