Control system for managing primary and secondary fusion circuit breaker

Abstract

The invention belongs to the technical field of circuit breaker management, and discloses a control system for managing a primary and secondary fusion circuit breaker, and the system collects the electrical parameters, mechanical states and environmental conditions of the circuit breaker through multiple channels, and constructs a standardized operation data set; establishing a circuit breaker health characteristic spectrum based on deep characteristic learning; establishing an environmental adaptability control parameter library through environmental factor correlation analysis and multi-scene simulation; performing fault mode identification and predictive diagnosis in combination with the health characteristic spectrum, and generating a fault risk early warning matrix; optimizing a multi-circuit-breaker cooperative control strategy based on the early warning matrix, and generating an optimal control instruction sequence; and reliable execution and effect feedback of the control instruction are realized through security encryption verification and a hierarchical execution mechanism. The problems that a traditional circuit breaker control system is difficult in data integration, insufficient in environment adaptability, weak in fault prediction capacity, incomplete in cooperative control and the like are solved, and the safety and reliability of power grid operation are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of circuit breaker management, and more specifically, to a control system for managing a primary-secondary integrated circuit breaker. Background Art

[0002] As a key protection device in the power system, the operating state of the circuit breaker is directly related to the safe and stable operation of the power grid. With the continuous advancement of the construction of the smart grid, the primary-secondary integrated circuit breaker technology has emerged. This technology deeply integrates traditional primary equipment (such as the circuit breaker body, current transformer, etc.) with secondary equipment (such as protection devices, measurement and control units, etc.), realizing the integrated design and integrated application of equipment. In recent years, the research and application of primary-secondary integrated circuit breakers in the domestic and international power industries have been continuously deepened, and significant progress has been made in related control technologies.

[0003] However, in a complex power grid environment, the information barrier between primary equipment and secondary equipment leads to a serious data island phenomenon, making it impossible for the system to comprehensively grasp the true operating state of the circuit breaker. Especially during power grid fluctuations, the data asynchronization problem is more prominent, delaying the fault handling time. When the circuit breaker is in harsh environments such as high altitude, high humidity, or high pollution, the control system lacks an environmental adaptability adjustment mechanism, resulting in a significant increase in the misoperation rate. For example, the insulation performance of circuit breakers in rainy areas in the south frequently deteriorates. The traditional fault judgment mechanism based on simple thresholds is too rough to identify complex fault development patterns, causing many potential faults to be ignored in the early warning stage, and ultimately leading to large-scale power outages. In the scenario of coordinated operation of multiple circuit breakers in large substations, the lack of system-level coordinated decision-making ability and the isolated operation of each circuit breaker lead to frequent problems such as uncoordinated protection actions and unreasonable load shedding. The security mechanism in the control instruction execution link is weak, and it is particularly vulnerable when facing the increasing network security threats in the context of smart grid construction. In addition, the lack of an effective execution feedback mechanism makes it impossible for the system to optimize control strategies through historical experience, resulting in the recurrence of the same type of faults, seriously affecting the stable operation of the power grid and power supply reliability, and causing high equipment maintenance costs.

[0004] In view of this, the present invention proposes a control system for managing a primary-secondary integrated circuit breaker to solve the above problems. Summary of the Invention

[0005] To overcome the above-mentioned defects of the prior art and to achieve the above object, the present invention provides the following technical solution: A control system for managing a primary-secondary integrated circuit breaker, comprising:

[0006] A data acquisition and synchronization module, configured to comprehensively collect and perform time synchronization processing on the operating parameters of the primary-secondary integrated circuit breaker to obtain a standardized operating data set;

[0007] A state perception and feature extraction module, which is used to perform multi-dimensional state perception and key feature extraction based on the standardized operation data set to obtain a breaker health feature map;

[0008] An operating environment adaptability evaluation module, which is used to perform operating environment adaptability evaluation and parameter self-adaptive optimization based on the breaker health feature map to obtain an environment adaptability control parameter library;

[0009] A fault prediction and diagnosis module, which is used to perform fault mode recognition and predictive diagnosis on the breaker based on the environment adaptability control parameter library and the breaker health feature map to obtain a fault risk warning matrix;

[0010] A collaborative control decision-making module, which is used to perform multi-breaker collaborative control strategy optimization and decision generation based on the fault risk warning matrix to obtain an optimal control instruction sequence;

[0011] A safety execution and feedback module, which is used to perform safety encryption verification and hierarchical execution on the control instruction based on the optimal control instruction sequence, and real-time feedback the execution effect to obtain a breaker control execution report; Each module is connected through a secure communication network to realize secure data transmission and sharing between modules.

[0012] The technical effects and advantages of a control system for managing a primary-secondary integrated breaker of the present invention:

[0013] The present invention greatly improves the safe and reliable operation level of the power system, and significantly reduces the breaker failure rate and power outage risk through a forward-looking fault warning and intelligent prevention mechanism. The system realizes the transformation from passive response to active predictive maintenance, greatly extends the service life of equipment and reduces maintenance costs. Under complex and changeable environmental conditions, the system shows excellent adaptability and effectively copes with external interference factors such as temperature fluctuations and changes in pollution levels. Especially under power grid disturbances and extreme weather conditions, the stability and reliability of the system are significantly enhanced. Through the collaborative optimization control between multiple breakers, the system realizes the efficient allocation of power grid resources and load balancing, improves power quality and power supply reliability. At the same time, the safety execution mechanism of the system provides multiple guarantees for power grid operations, effectively preventing the risks of misoperation and malicious attacks. In addition, the self-learning and continuous optimization ability of the system enables its operating efficiency to continuously improve over time. Description of the Drawings

[0014] Figure 1 It is a schematic diagram of a control system for managing a primary-secondary integrated breaker of the present invention;

[0015] Figure 2 It is a detailed implementation flowchart for obtaining the breaker working state feature set of the present invention. Detailed implementation mode

[0016] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0017] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0018] This application example provides a control system for managing a primary-secondary integrated circuit breaker. The execution subjects of the control system for managing a primary-secondary integrated circuit breaker include, but are not limited to, the following general computing nodes that carry the system: intelligent terminals, power automation devices, edge computing nodes, cloud server clusters, intelligent power grid monitoring centers, etc. Power automation devices include, but are not limited to: at least one of a circuit breaker control unit, an intelligent monitoring device, and an intelligent distribution terminal.

[0019] Please refer to Figure 1 , the present invention provides a control system for managing a primary-secondary integrated circuit breaker, including:

[0020] A data acquisition and synchronization module, which is used to comprehensively acquire the operation parameters of the primary-secondary integrated circuit breaker and perform time synchronization processing to obtain a standardized operation data set;

[0021] A state perception and feature extraction module, which is used to perform multi-dimensional state perception and key feature extraction based on the standardized operation data set to obtain a circuit breaker health feature map;

[0022] An operating environment adaptability evaluation module, which is used to perform operating environment adaptability evaluation and parameter adaptive optimization based on the circuit breaker health feature map to obtain an environment adaptability control parameter library;

[0023] A fault prediction and diagnosis module, which is used to perform fault mode identification and predictive diagnosis on the circuit breaker based on the environment adaptability control parameter library and the circuit breaker health feature map to obtain a fault risk warning matrix;

[0024] A cooperative control decision-making module, which is used to perform multi-circuit breaker cooperative control strategy optimization and decision generation based on the fault risk warning matrix to obtain an optimal control instruction sequence;

[0025] A safety execution and feedback module is used to perform secure encryption verification and hierarchical execution on control instructions based on an optimal control instruction sequence, and to provide real-time feedback on the execution effect to obtain a breaker control execution report. Each module is connected through a secure communication network to achieve secure data transmission and sharing between modules.

[0026] In the present invention, by comprehensively collecting the multi-source operation parameters of the primary-secondary integrated breaker, efficiently synchronizing and processing the data to form a standardized operation data set, using multi-dimensional state perception technology to extract key features and construct a breaker health feature map to comprehensively characterize the operation state of the breaker, performing environmental adaptability evaluation and adaptive parameter optimization based on the health feature map, constructing a control parameter library capable of adapting to various environmental conditions to improve the environmental adaptability of the system, combining the environmental adaptability control parameter library and the health feature map to achieve accurate fault prediction and diagnosis, generating a detailed risk warning matrix to provide a basis for preventive maintenance, optimizing the generation of an optimal control instruction sequence through a multi-breaker collaborative control strategy to achieve the coordinated and efficient operation of the overall system, adopting a secure encryption verification and hierarchical execution mechanism to ensure the secure and reliable execution of control instructions, and forming a complete execution report through real-time feedback to provide a basis for continuous system optimization.

[0027] In an embodiment of the present invention, a control system for managing a primary-secondary integrated breaker includes:

[0028] A data acquisition and synchronization module is used to comprehensively collect and perform time synchronization processing on the operation parameters of the primary-secondary integrated breaker to obtain a standardized operation data set;

[0029] In this embodiment, a high-precision sensor network is deployed to monitor the electrical parameters of the integrated breaker in real time, and electrical characteristic data such as current, voltage, power factor, and harmonic content are collected, including key parameters such as short-circuit current, overload current, dynamic stability current, and thermal stability current. A mechanical state monitoring device (such as an acceleration sensor and a displacement sensor) is installed to capture the motion characteristics of the mechanical components of the breaker and record mechanical state parameters such as operating stroke, speed, acceleration, and vibration characteristics. An environmental monitoring device is deployed to collect the operating environmental conditions of the breaker, including environmental parameters such as temperature, humidity, air pressure, and salt spray concentration, and to record in real time the environmental factors that may affect the performance of the breaker. Operation instructions and feedback signals are extracted from the breaker control system, and control process data such as operation sequences, response times, and execution effects are recorded. The electromagnetic compatibility performance of the breaker is monitored, and relevant parameters such as electromagnetic interference level and immunity performance are collected.

[0030] Preliminarily organize the collected heterogeneous data streams, establish a unified data format standard to ensure that the data contains accurate timestamps and device identification information, forming the original heterogeneous data streams. Use GPS or Network Time Protocol (NTP) to synchronize the time of multi-source data to ensure that the data collected by different sensors is accurately aligned in the time dimension, constructing time-synchronized data. Apply filtering algorithms such as Gaussian filtering or wavelet transform to the time-synchronized data for noise suppression, removing the noise introduced by factors such as power grid fluctuations and equipment vibrations. Automatically detect outliers and apply algorithms such as Isolation Forest or Local Outlier Factor to identify the abnormal points in the data. Adopt data interpolation techniques (such as multiple imputation method, K-nearest neighbor imputation) to repair the missing data, ensuring the integrity and continuity of the dataset, and forming the cleaned dataset. Apply wavelet denoising or Kalman filtering techniques to further optimize the data quality, improve the signal-to-noise ratio, and enhance the signal characteristics, generating the optimized dataset. Conduct unified standardization processing on the optimized dataset, such as Z-score standardization or MinMax standardization, to convert parameters with different dimensions to the same scale, constructing the standard feature matrix. Use methods such as Pearson correlation coefficient or mutual information to calculate the correlation between parameters, establish the parameter correlation matrix, and quantify the mutual influence relationship between parameters. Use feature importance evaluation algorithms (such as random forest importance evaluation, recursive feature elimination) to calculate the contribution degree of each parameter to the breaker state, generating the parameter weight distribution. Based on the parameter weight distribution, prioritize the features, screen out the set of parameters that are most critical for characterizing the breaker state, forming the key parameter set. Classify and organize the key parameter set, construct multi-dimensional parameter subsets according to dimensions such as electrical parameters, mechanical states, and environmental conditions, and integrate the multi-dimensional parameter subsets to form a structured standardized operation dataset, providing a complete, accurate, and consistent data basis for subsequent analysis.

[0031] The state perception and feature extraction module is used to perform multi-dimensional state perception and key feature extraction based on the standardized operation dataset to obtain the breaker health feature map;

[0032] In this embodiment, a deep learning network (such as a convolutional neural network, a recurrent neural network, or an autoencoder) is applied to perform feature learning on the standardized operation data set, extract deep feature representations contained in the data, capture the internal patterns of the circuit breaker operation state, form potential feature representations, apply a multi-layer abstraction modeling method to the potential feature representations, construct a hierarchical representation model including low-level features, intermediate features, and high-level features, realize the multi-level representation of the circuit breaker state, form a hierarchical feature structure, apply an ensemble classifier (such as a random forest, a gradient boosting tree, or a deep neural network) to the hierarchical feature structure for state pattern recognition, identify different working states of the circuit breaker (such as normal closing, normal opening, overload protection, short-circuit protection, etc.), generate a circuit breaker working state feature set, based on the circuit breaker working state feature set, extract the transition features between states, such as the transition characteristics from closing to opening, the evolution characteristics from normal to overload, etc., construct a state transition sequence, perform time series correlation analysis on the state transition sequence, apply time series analysis techniques (such as an autoregressive model, a long short-term memory network) to mine the time rules of state changes, form a state evolution rule, based on the state evolution rule, construct a circuit breaker state transition probability matrix, establish a state transition model based on the Markov process, and quantify the transition probabilities and conditions between different states to form a Markov state model.

[0033] Based on the Markov state model, define a health metric index system, including reliability indexes, stability indexes, responsiveness indexes, and life indexes, etc., construct a health assessment index system, apply fuzzy logic or evidence theory methods to calculate the health state indexes of each key component of the circuit breaker (such as the contact system, the arc extinguishing device, the operating mechanism, the control circuit, etc.), form a health state distribution map, perform topological structure modeling on the health state distribution map, construct a network structure with components as nodes and the relationships between components as edges, the weight of the edge represents the association strength between components, and the node attributes include health state indexes, to form a circuit breaker health feature map, comprehensively characterizing the health state and evolution trend of the circuit breaker.

[0034] An operating environment adaptability evaluation module, used to perform operating environment adaptability evaluation and parameter adaptive optimization based on the circuit breaker health feature map to obtain an environment adaptability control parameter library;

[0035] In this embodiment, an association rule mining algorithm (such as the Apriori algorithm or the FP-Growth algorithm) is applied to the circuit breaker health characteristic map to analyze the association relationship between environmental factors (such as temperature, humidity, operating frequency, etc.) and the circuit breaker health status indicators, identify the influence mode of environmental factors on the circuit breaker performance, construct an environmental sensitivity matrix. Based on the environmental sensitivity matrix, a feature selection algorithm (such as recursive feature elimination or model-based feature selection) is used to screen out the environmental factors that have the most significant impact on the circuit breaker performance, such as extreme temperature, high humidity, frequent operation, etc., to form an environmental impact factor set. A multi-factor and multi-level scenario simulation experiment is designed for the environmental impact factor set, and the response characteristics of the circuit breaker are evaluated under different environmental condition combinations. Numerical simulation or physical test methods are applied to collect the operating data of the circuit breaker under various environmental conditions, and an environmental change response model is constructed.

[0036] Based on the environmental change response model, an adaptive parameter adjustment strategy is designed, the adjustment rules of control parameters (such as operation timing, mechanical parameters, protection setting values, etc.) under different environmental conditions are defined to form a parameter adjustment rule library. The parameter adjustment rule library is applied to construct a parameter space, and the value range and constraint conditions of the parameters are defined to form an adaptive control parameter space. In the adaptive control parameter space, a multi-objective optimization algorithm (such as genetic algorithm, particle swarm algorithm or differential evolution algorithm) is used to find the optimal parameter combination that meets the multi-objective requirements (such as reliability, response speed, energy consumption minimization, etc.) to form a parameter optimization combination. The parameter optimization combination is subjected to simulation verification and experimental testing to evaluate the performance of the parameter combination under different working conditions and generate a performance evaluation result. Based on the performance evaluation result, sensitivity analysis and response surface methods are applied to finely adjust the parameters to improve the robustness and stability of the parameters and form a refined control parameter set. A mapping relationship model between environmental conditions and control parameters is established for the refined control parameter set, and methods such as support vector regression, neural network or Gaussian process regression can be used to construct an adaptive model that can automatically generate the optimal control parameters according to environmental conditions, and finally form an environmental adaptability control parameter library to achieve the optimal control of the circuit breaker in a changing environment.

[0037] A fault prediction and diagnosis module, which is used to perform fault mode identification and predictive diagnosis on the circuit breaker based on the environmental adaptability control parameter library and the circuit breaker health characteristic map, and obtain a fault risk warning matrix;

[0038] In this embodiment, feature patterns related to faults are extracted from the circuit breaker health characteristic map, such as abnormal component health, changes in state transition probability, etc., to construct a fault feature set. The historical fault case library and expert knowledge are integrated to establish a typical fault mode library for the circuit breaker, including common fault types such as contact wear, actuator jamming, arc extinguishing device failure, and control circuit abnormality, forming a set of typical fault modes. The similarity calculation algorithm (such as cosine similarity, Euclidean distance, or Mahalanobis distance) is applied to quantify the similarity degree between different fault modes, constructing a fault mode similarity network to describe the correlation and similarity degree between fault types. The clustering analysis and evolutionary calculation methods are applied to the fault mode similarity network to identify the classification structure and development trend of fault modes, constructing a fault development path diagram to reveal the evolution process of faults from early signs to severe faults. Combining the environmental impact model in the environmental adaptability control parameter library, the influence of different environmental conditions on fault development is analyzed, such as high temperature accelerating contact wear, high humidity promoting insulation deterioration, etc., constructing an environment-sensitive fault development model to describe the modulation effect of environmental factors on the fault evolution rate and path. Based on the environment-sensitive fault development model, a fault prediction model is established using machine learning algorithms (such as support vector machines, random forests, or deep learning networks). The current state characteristics and environmental conditions are input, and the future fault probability is output to form a fault probability prediction function. The fault risk of each key component of the circuit breaker (such as main contacts, auxiliary contacts, actuators, arc extinguishing devices, control circuits, etc.) is evaluated using the fault probability prediction function, predicting the occurrence probability and time window of various faults, generating a fault risk assessment result. The risk matrix analysis method is applied to the fault risk assessment result to classify the risk from two dimensions of fault severity and urgency, constructing a risk level matrix. Based on the risk level matrix, a hierarchical early warning strategy is designed, defining the early warning level, early warning method, and response measures corresponding to different risk levels, forming multi-level early warning rules. According to the multi-level early warning rules, a fault risk early warning matrix including fault types, occurrence probability, impact degree, early warning level, and recommended measures is generated, providing a scientific basis for operation and maintenance decision-making.

[0039] The collaborative control decision-making module is used to optimize the collaborative control strategy of multiple circuit breakers and generate decisions based on the fault risk early warning matrix, obtaining an optimal control instruction sequence;

[0040] In this embodiment, network analysis is performed on the fault risk warning matrix to identify the control dependencies and influence links in the multi - breaker system, and a collaborative control network describing the interaction between breakers is constructed. Based on the collaborative control network, a system - level state - space model of the multi - breaker system is established to describe the overall state transition characteristics and dynamic behavior of the system, forming a system state representation. Based on the system state representation, a multi - objective optimization function for collaborative control is defined, including objectives such as maximizing system reliability, minimizing the number of operations, load balancing, and minimizing maintenance costs, forming a multi - objective optimization problem. The constraint conditions of the multi - objective optimization problem are analyzed, considering various constraints for the safe and stable operation of the power system, such as current - carrying capacity, voltage stability, power balance, etc., forming a control constraint set. Based on the multi - objective optimization problem and the control constraint set, an advanced optimization algorithm (such as multi - agent reinforcement learning, distributed optimization, or model predictive control) is applied to construct an optimization algorithm for the collaborative control strategy, generating a set of candidate control strategies that meet the constraint conditions. The set of candidate control strategies is simulated and evaluated to analyze the performance of each strategy in different scenarios, including normal operating conditions, fault conditions, and extreme conditions, etc., generating a strategy performance evaluation matrix. Based on the strategy performance evaluation matrix, a multi - criterion decision - making method (such as the analytic hierarchy process, TOPSIS, or PROMETHEE) is used to select the best control strategy, forming a decision - making control plan. The decision - making control plan is decomposed in time series, decomposing the overall control plan into a series of control steps with clear execution times and sequences, forming a control instruction time - series plan. Based on the control instruction time - series plan, detailed control instruction parameters are generated, including information such as specific operating equipment, execution time, operation type, parameter settings, and expected results, finally forming an optimal control instruction sequence to provide precise guidance for the collaborative control of breakers.

[0041] A safety execution and feedback module, which is used to perform safety encryption verification and hierarchical execution on the control instructions based on the optimal control instruction sequence, and provide real - time feedback on the execution effect to obtain a breaker control execution report;

[0042] In this embodiment, the optimal control instruction sequence is classified according to the security risk level, such as high-risk instructions (forced disconnection of the circuit breaker), medium-risk instructions (adjustment of protection parameters), low-risk instructions (status monitoring), etc., to form a hierarchical instruction set. An identity authentication and permission verification mechanism is applied to the hierarchical instruction set to ensure that the instruction executor has the corresponding operation permissions. Technologies such as digital signature and two-factor authentication are used to verify the legality of the instruction source, generating an authorized instruction set. A high-strength encryption algorithm (such as AES-256, RSA, or elliptic curve encryption) is applied to the authorized instruction set for secure encryption processing to ensure that the instruction is not stolen or tampered with during transmission, forming a secure instruction package. Based on the secure instruction package, a detailed execution plan is formulated, including the execution sequence, time window, resource allocation, and emergency plan, etc., to form an instruction execution scheduling plan. According to the instruction execution scheduling plan, the control instructions are distributed to each execution terminal through a secure communication network, monitoring the receipt confirmation and execution status of the instructions, capturing the execution process data in real time, forming an execution process data stream, performing real-time analysis on the execution process data stream, evaluating the accuracy, timeliness, and effectiveness of the instruction execution, calculating the execution success rate and performance indicators, generating an execution effect evaluation result. Based on the execution effect evaluation result, a closed-loop control strategy is adopted to perform real-time adjustment on the subsequent instruction execution, such as execution timing optimization, parameter fine-tuning, or emergency response, etc., forming an adaptive execution feedback. The whole process information of the instruction execution is detailedly recorded, including the execution time, operation parameters, response status, abnormal situations, and handling measures, etc., generating a detailed execution process log. The execution effect evaluation result and the execution process log are integrated to form a comprehensive circuit breaker control execution report, including execution statistical analysis, summary of abnormal situations, performance improvement suggestions, and subsequent operation and maintenance suggestions, etc., providing data support for the continuous optimization of the system.

[0043] In the embodiment of the present invention, the data acquisition and synchronization module is used to comprehensively acquire and perform time synchronization processing on the operation parameters of the primary-secondary integrated circuit breaker to obtain a standardized operation data set, specifically used for:

[0044] Perform multi-channel acquisition on the electrical parameters, mechanical state, and environmental conditions of the circuit breaker to obtain an original heterogeneous data stream, and perform timestamp calibration and data quality evaluation on the original heterogeneous data stream to obtain time-synchronized data;

[0045] Perform anomaly detection and missing value repair on the time-synchronized data to obtain a cleaned data set, and perform data denoising and signal enhancement processing according to the cleaned data set to obtain an optimized data set;

[0046] Perform feature normalization and standardization processing on the optimized data set to obtain a standard feature matrix, and calculate the correlation between parameters based on the standard feature matrix to obtain a parameter correlation matrix;

[0047] Evaluate the feature importance of the parameter correlation matrix to obtain the parameter weight distribution, and prioritize and screen the features according to the parameter weight distribution to obtain the key parameter set;

[0048] Classify and organize the data of the key parameter set to obtain multi-dimensional parameter subsets, and construct a standardized operation data set according to the multi-dimensional parameter subsets, including an electrical parameter subset, a mechanical state subset, and an environmental condition subset.

[0049] In this embodiment, a high-precision electrical parameter monitoring system is used to collect electrical parameters such as current, voltage, power factor, and harmonic content of the circuit breaker in real time, record normal operating parameters and extreme condition parameters, and form an electrical parameter database. An intelligent mechanical state monitoring device is deployed to detect mechanical state parameters such as the operating stroke, speed, acceleration, and vibration characteristics of the circuit breaker, obtain the motion characteristics and wear characteristics of the operating mechanism, and establish a mechanical state database. Environmental monitoring equipment is installed to collect the operating environmental conditions of the circuit breaker, including environmental parameters such as temperature, humidity, air pressure, and pollution degree, associate environmental factors with the performance of the circuit breaker, and construct an environmental condition database. Through a multi-channel data acquisition system, parallel acquisition of heterogeneous data is realized to ensure the comprehensiveness and real-time nature of data acquisition, forming an original heterogeneous data stream. High-precision time synchronization technology (such as GPS clock, PTP protocol, or IEEE 1588 standard) is used to calibrate the timestamps of multi-source data, ensuring that the time deviation between different acquisition devices is controlled at the microsecond level. The synchronized data is subjected to quality assessment, calculating the integrity, accuracy, and consistency indicators of the data, marking data segments with abnormal quality, forming time-synchronized data. Anomaly detection algorithms based on statistics and machine learning (such as isolation forest, one-class SVM, or autoencoder anomaly detection) are applied to identify outliers in the data, distinguishing system anomalies from data anomalies through multi-dimensional analysis. Advanced data repair techniques (such as tensor completion, deep generative models, or Bayesian inference) are used to intelligently repair missing data, ensuring the continuity and reliability of the data, and generating a high-quality dataset after cleaning. Signal processing techniques are applied to the cleaned dataset for noise reduction, using methods such as wavelet transform, empirical mode decomposition, or adaptive filtering to suppress random noise and interference signals, and applying signal enhancement algorithms (such as spectral reconstruction, feature resonance, or modal separation) to highlight the key features in the data, improving the signal-to-noise ratio, and forming an optimized dataset. The optimized dataset is subjected to unified normalization and standardization processing to eliminate the influence of dimension differences and scales, enabling direct comparison and analysis of different types of parameters, constructing a standard feature matrix, applying advanced correlation analysis methods (such as Pearson correlation coefficient, Kendall rank correlation, or mutual information) to evaluate the linear and non-linear correlation relationships between parameters, constructing a parameter correlation matrix, forming a relationship network between parameters, applying a feature importance assessment algorithm (such as random forest importance, stability selection, or information gain-based method) to the parameter correlation matrix to calculate the contribution degree of each parameter to the circuit breaker state characterization, forming a parameter weight distribution. Based on the parameter weight distribution, an importance threshold is set for feature screening, retaining high-information parameters and filtering redundant features, forming a feature subset containing key monitoring indicators, that is, a key parameter set. The key parameter set is classified and organized in multiple dimensions, constructing a parameter classification system from different perspectives such as electrical characteristics, mechanical state, and environmental conditions, forming a structured multi-dimensional parameter subset, integrating the multi-dimensional parameter subset, establishing a unified data model and access interface, and finally forming a standardized operation dataset.Provide a high-quality data foundation for subsequent state perception and feature extraction.

[0050] In an embodiment of the present invention, a state perception and feature extraction module is configured to perform multi-dimensional state perception and key feature extraction based on a standardized operation data set to obtain a breaker health feature map, specifically used for:

[0051] Perform deep feature learning on the standardized operation data set to obtain a latent feature representation, and establish a multi-level feature abstraction model based on the latent feature representation to obtain a hierarchical feature structure;

[0052] Perform state pattern recognition on the hierarchical feature structure to obtain a breaker working state feature set, and extract state transition features based on the breaker working state feature set to obtain a state transition sequence;

[0053] Perform temporal correlation analysis on the state transition sequence to obtain a state evolution law, and construct a breaker state transition probability matrix based on the state evolution law to obtain a Markov state model;

[0054] Perform health quantification analysis on the Markov state model to obtain a health assessment index system, and calculate the health index of each component of the breaker based on the health assessment index system to obtain a health status distribution map;

[0055] Perform topological structure modeling on the health status distribution map to obtain a breaker health feature map, including node health status, edge association strength, and state evolution trend information.

[0056] In this embodiment, a deep learning architecture (such as a convolutional neural network, long short-term memory network, or variational autoencoder) is applied to perform non-linear feature learning on the standardized operation dataset, extract the deep patterns and implicit relationships contained in the data, generate a high-dimensional latent feature representation, apply feature dimensionality reduction and visualization techniques (such as t-SNE, UMAP, or principal component analysis) to the latent feature representation for low-dimensional mapping, intuitively display the feature distribution and clustering structure, construct a feature representation model containing multiple levels of abstraction, form a hierarchical structure from low-level signal features to high-level semantic features, capture different granularity features of the circuit breaker state at different levels, form a hierarchical feature structure, apply a multi-model integrated classifier (such as a combination of random forest, gradient boosting tree, and support vector machine) to the hierarchical feature structure for state pattern recognition, identify the working state categories and state parameters of the circuit breaker, generate a circuit breaker working state feature set, extract the feature change patterns during state transitions from the circuit breaker working state feature set, analyze the triggering conditions, transition processes, and completion flags of state transitions, form a state transition feature description, organize the state transition features in chronological order, construct a serialized representation reflecting the historical evolution of the circuit breaker state, i.e., a state transition sequence, apply a time series pattern mining algorithm (such as a recurrent neural network, sequence association rule mining) to the state transition sequence to identify the periodicity, regularity, and abnormal patterns of state changes, discover the internal laws of state evolution, form state evolution laws, based on the state evolution laws, construct a probability matrix describing the circuit breaker state transition process, quantify the transition probabilities and conditional probabilities between different states, establish a state transition model based on the Markov process, form a Markov state model, based on the Markov state model, define a circuit breaker health metric system, including reliability metrics (such as failure rate, mean time between failures), stability metrics (such as parameter volatility, state retention ability), responsiveness metrics (such as operation time, action characteristics), and lifetime metrics (such as cumulative operation times, wear degree), etc., construct a health assessment metric system, based on the health assessment metric system, use fuzzy logic reasoning or evidence theory methods to calculate the health state indices of key components of the circuit breaker (such as the contact system, arc extinguishing device, operating mechanism, control circuit, etc.), generate a distribution map reflecting the health status of each component, i.e., a health state distribution map; the specific process is as follows:

[0057] Systematically decompose the key components of the circuit breaker, identify the core components for health state assessment, including the main contact system (fixed and moving contacts, arc extinguishing contacts), arc extinguishing device (arc extinguishing chamber, arc extinguishing grid plate), operating mechanism (energy storage mechanism, transmission mechanism, auxiliary switch), control circuit (secondary circuit, protection unit, monitoring unit), and auxiliary system (insulation support, sealing system), etc., to form a hierarchical component assessment structure system.

[0058] For the implementation process of the fuzzy logic reasoning method: construct a set of fuzzy linguistic variables for the health state of the circuit breaker, define health state levels such as "excellent", "good", "average", "slightly deteriorated", "moderately deteriorated", "severely deteriorated" and "dangerous", and design corresponding membership functions. For the characteristic parameters of each component, such as the contact resistance value, opening distance, overtravel, contact pressure, contact temperature rise of the contact, establish a special fuzzy set and membership function to map the accurate measurement values onto the fuzzy set. Design a fuzzy rule base, integrating expert knowledge and historical operation experience, and formulate inference rules such as "if the contact resistance is high and the temperature rise is large, then the contact state is moderately deteriorated". 30 - 50 dedicated rules are established for each component type.

[0059] Apply the fuzzy inference engine to process the monitored data of the components, and use the Mamdani or Sugeno inference model to perform rule matching, condition evaluation and conclusion synthesis. Taking the contact system as an example, when the input contact resistance is 135 μΩ (close to the upper limit threshold) and the temperature rise is 42 °C (medium to high), then according to the rule activation degree calculation, the membership degree of the "average" state is 0.35, and the membership degree of the "slightly deteriorated" state is 0.65. Defuzzify the inference result, and the centroid method, maximum membership degree method or weighted average method can be used to convert the fuzzy output into an accurate health state index value. For example, convert the fuzzy result of the above contact system into a health index of 68 (out of 100).

[0060] After the health assessment at the component level is completed, adopt the hierarchical weight fusion method, considering the contribution weights of the components to the overall health state of the circuit breaker, such as 0.35 for the contact system, 0.25 for the arc extinguishing device, 0.25 for the operating mechanism, and 0.15 for the control circuit, to generate the overall health state index. To improve the interpretability of the evaluation results, use various forms such as health dashboards, radar charts and heat maps to visually display the health states of each component; then generate a distribution map reflecting the health conditions of each component.

[0061] Apply the complex network modeling method to the health state distribution map, construct a network structure with components as nodes and relationships between components as edges. The node attributes include health state indicators, and the weight of the edge represents the association strength between components. Add time dimension information characterizing the state evolution trend to form a complete health characteristic map of the circuit breaker, comprehensively characterizing the health state, component relationships and evolution trends of the circuit breaker.

[0062] In the embodiment of the present invention, please refer to Figure 2 , for the detailed implementation flowchart of obtaining the working state characteristic set of the circuit breaker, perform state pattern recognition on the hierarchical feature structure to obtain the working state characteristic set of the circuit breaker, specifically including:

[0063] Apply multiple classification algorithms to the hierarchical feature structure for ensemble learning to obtain the initial result of state classification, and perform voting fusion on the initial result of state classification to obtain the fused result of state recognition;

[0064] Combine the preset historical state library and expert knowledge library to verify and correct the fused result of state recognition, obtain the accurate working state identifier, and extract the state feature vector based on the working state identifier to obtain the state feature representation;

[0065] Construct a state feature space according to the state feature representation, and perform density clustering analysis in the state feature space to obtain the working state clustering center;

[0066] Perform boundary learning on the working state clustering center to obtain the state boundary descriptor, and identify the state transition critical point according to the state boundary descriptor to obtain the state transition trigger condition;

[0067] Integrate the working state clustering center, state boundary descriptor and state transition trigger condition to form a complete circuit breaker working state feature set.

[0068] In this embodiment, multiple advanced classification algorithms (such as support vector machine, random forest, gradient boosting tree, deep neural network, etc.) are applied to the hierarchical feature structure for parallel processing. Each algorithm captures the state features from different angles according to its characteristics, generates multiple independent state classification results, and constitutes the initial result set of state classification. The voting fusion strategy (such as hard voting, soft voting or weighted voting) is applied to the initial result of state classification to synthesize the judgment results of multiple classifiers, improve the accuracy and robustness of classification, and form the fusion result of state recognition. The preset state library constructed by introducing historical operation data and the knowledge base formed by industry experts' experience are used as reference benchmarks to verify and correct the fusion result of state recognition. The knowledge graph reasoning or rule matching method is applied to identify potential misclassifications and adjust the classification results to obtain a more accurate working state identifier. Based on the working state identifier, a feature vector that can comprehensively represent the characteristics of this state is extracted, including multi-dimensional information such as electrical characteristics, mechanical characteristics and time characteristics, to form a structured state feature representation. Based on the state feature representation, a multi-dimensional state feature space is constructed, and each dimension represents a type of feature attribute. Different working states of the circuit breaker form different distribution patterns in this space. The density clustering algorithm (such as DBSCAN, OPTICS or mean shift clustering) is applied in the state feature space to identify high-density regions, which represent the typical working states of the circuit breaker, such as normal closing state, normal opening state, overload protection state, etc. The center points of each cluster are calculated to form the working state clustering center. The boundary learning algorithm (such as One-Class SVM, isolation forest or boundary recognition neural network) is applied to the working state clustering center to learn the boundary features of each state, define the normal change range and abnormal judgment criteria of the state, and generate the state boundary descriptor. Based on the state boundary descriptor, the transition region between different states is analyzed to identify the critical features and triggering conditions of state transition, such as the action current threshold from closing to opening, the current-time characteristic from normal to overload, etc., to form the state transition triggering condition.

[0069] The detailed process of identifying the critical features and triggering conditions of state transition in a specific embodiment is as follows:

[0070] For each working state clustering center, a boundary representation model in the state feature space is constructed. The adaptive statistical process control method is used to calculate the normal fluctuation range of each state feature parameter based on historical operation data. For key features such as opening and closing time, peak current, operating energy and contact velocity, the control limit calculation method is applied, and the upper and lower limit thresholds of the feature parameters are determined by the μ±kσ principle (μ is the parameter mean, σ is the standard deviation, and k is the tolerance coefficient). For different working states of the circuit breaker, different tolerance coefficients are adopted. For example, k = 3 is adopted for the normal closing state and k = 2.5 is adopted for the overload protection state to ensure the rationality of boundary definition.

[0071] For complex states characterized by multiple feature combinations, a multivariate statistical process control method is used to construct a T²-Hotelling control chart to monitor the multivariate covariance structure. The Mahalanobis distance between the state feature vector x and the cluster center μ is calculated;

[0072] For nonlinear boundary features, anomaly detection algorithms such as One-Class SVM or Isolation Forest are used to construct closed boundaries of states in high-dimensional feature space. By adjusting the kernel function parameters and anomaly ratio parameters, precise control of boundary tightness is achieved. In particular, for continuous features such as the characteristic curve of the operating mechanism, the function envelope method is used to construct upper and lower envelope curves as dynamic boundaries to achieve boundary monitoring of the entire process characteristics.

[0073] Define multi-level abnormality judgment criteria and establish classification boundaries from slight deviation, obvious abnormality to serious fault. Design a progressive abnormality judgment mechanism based on fuzzy membership to avoid decision jitter caused by traditional binary judgment. For each type of state feature, define the warning threshold and fault threshold respectively. For example, for the contact resistance of the contact, 120μΩ is the warning threshold and 150μΩ is the fault threshold. Construct an abnormality severity evaluation function ES(x), map the feature deviation degree to the [0, 1] interval, and realize the quantitative expression of abnormality.

[0074] Integrate boundary definitions and anomaly judgment criteria to build a complete state boundary descriptor data structure, including: state center vector, state covariance matrix, boundary model parameters, anomaly judgment threshold and state stability index, etc. Establish a multi-scale boundary description system and set differentiated boundary constraints for characteristic fluctuations at different time granularities (such as instantaneous state, short-term steady state and long-term trend).

[0075] Based on the generated state boundary descriptors, the cross-state transition region analysis is carried out. The whole process data of the circuit breaker transitioning from one working state to another is collected to construct a state migration data set. The state transition sequence is segmented in time series to identify the pre-steady-state region, transition region, and post-steady-state region. The sliding window method is used to calculate the feature change rate and locate the key time point with the most drastic feature change as the potential trigger point for state transition.

[0076] The sequence pattern mining algorithm is used to extract characteristic change patterns with time series characteristics from the state transition data. The key transition event chains are identified, such as "current mutation → contact action → arc extinguishing process → mechanical locking" and other time series association patterns. For each type of state transition, a decision tree or rule set is constructed to formally express the combination of conditions that trigger the state transition.

[0077] For different types of state transition processes, such as "normal closed → open", "normal closed → overcurrent protection action", etc., dedicated transition feature models are established respectively. Through statistical analysis of a large number of transition samples, the most discriminative critical features are identified. For example, for overcurrent protection triggering, it is identified that "the phase current exceeds the set value and the duration t > threshold T" is the key triggering feature.

[0078] Based on dynamic system theory, the stability of critical points in the state space is analyzed to distinguish stable transitions, unstable transitions, and critically unstable states. The state switching hyperplane equation is established to describe the boundaries between different state domains, and the contribution weights of each feature to the hyperplane are calculated. Through the confusion matrix analysis of boundary samples, the feature combinations most likely to lead to misjudgment of states are identified, and the boundary description is optimized accordingly.

[0079] For state transitions that are easily affected by environmental factors, an adaptive threshold adjustment mechanism is designed. A threshold correction model is established based on environmental parameters (such as temperature, humidity, etc.) to achieve automatic adjustment of state transition conditions in different environments. For example, in a low-temperature environment, the friction of the circuit breaker operating mechanism increases, and the response time boundary should be appropriately relaxed. The state boundary descriptor and the state transition trigger condition are integrated into a unified state transition model. A function for calculating the state transition probability is established to achieve an end-to-end mapping from feature changes to state transition judgments.

[0080] The working state clustering center (representing the typical features of the state), the state boundary descriptor (defining the change range of the state), and the state transition trigger condition (determining the conditions for state transition) are systematically integrated to construct a complete set of circuit breaker working state features, comprehensively representing the working state features of the circuit breaker under various working conditions.

[0081] In the embodiment of the present invention, the operation environment adaptability evaluation module is used to perform operation environment adaptability evaluation and parameter adaptive optimization based on the circuit breaker health feature map, and obtain an environment adaptability control parameter library, specifically used for:

[0082] Perform environmental factor correlation analysis on the circuit breaker health feature map to obtain an environmental sensitivity matrix, and based on the environmental sensitivity matrix, identify key environmental impact factors to obtain an environmental impact factor set;

[0083] Perform multi-scenario simulations on the environmental impact factor set to obtain an environmental change response model, and based on the environmental change response model, define an adaptive parameter adjustment strategy to obtain a parameter adjustment rule library;

[0084] Based on the parameter adjustment rule library, construct an adaptive control parameter space, and perform multi-objective optimization in the adaptive control parameter space to obtain a parameter optimization combination;

[0085] Verify and test the optimized combination of parameters to obtain the performance evaluation results, and perform parameter fine-tuning based on the performance evaluation results to obtain a refined control parameter set;

[0086] Based on the refined control parameter set, establish an environment-parameter mapping relationship model to obtain an environment adaptability control parameter library, which supports the optimal control of the circuit breaker under different environmental conditions.

[0087] In this embodiment, the graph mining algorithm and association rule analysis are applied to perform environmental factor association analysis on the circuit breaker health feature map, identify the correlation and influence mode between environmental parameters (such as temperature, humidity, altitude, pollution degree, etc.) and the circuit breaker health status indicators, construct an environmental sensitivity matrix for quantifying the influence degree of environmental factors, and apply feature importance evaluation methods (such as evaluation based on information gain, stability selection, or model-based feature importance) to analyze the environmental sensitivity matrix, identify the environmental factors that have the most significant impact on the performance and reliability of the circuit breaker, such as extreme temperature, high humidity, severe pollution environment, etc., form a set of key environmental impact factors, design a multi-factor and multi-level experimental scheme, evaluate the performance response of the circuit breaker under different environmental condition combinations, optimize the experimental scheme using orthogonal experimental design or response surface method to improve the experimental efficiency, and based on historical operation data and experimental results, apply methods such as regression analysis, neural network, or Gaussian process to construct an environment-response relationship model, quantify the influence function of different environmental conditions on the circuit breaker performance parameters, and form an environmental change response model.

[0088] Based on the environmental change response model, design an adaptive parameter adjustment strategy, define the optimal values or adjustment methods of control parameters (such as operating speed, operating timing, protection setting values, etc.) under different environmental conditions, form a systematic parameter adjustment rule library, apply the parameter adjustment rule library to define the adaptive space of control parameters, clarify the value range, adjustment step size and constraint conditions of each parameter, construct a multi-dimensional parameter space reflecting the relationship between parameters, that is, the adaptive control parameter space. In the adaptive control parameter space, apply advanced optimization algorithms (such as genetic algorithm, particle swarm optimization, simulated annealing or differential evolution algorithm) for multi-objective optimization, balance multiple performance objectives such as reliability, response speed, operating life, etc., find the comprehensive optimal parameter combination, conduct simulation verification and experimental testing on the optimized parameter combination, evaluate the performance of the parameter combination under different working conditions, including reliability indicators, stability indicators and economic indicators, etc., generate a comprehensive performance evaluation result. Based on the performance evaluation result, apply sensitivity analysis and response surface method to finely adjust the parameters, optimize the robustness and adaptability of the parameters, form a refined control parameter set after fine-tuning optimization. Based on the refined control parameter set, apply machine learning methods (such as support vector regression, random forest regression or deep neural network) to establish a mapping relationship model between environmental conditions and optimal control parameters, realize the end-to-end mapping from environmental monitoring data to automatic generation of control parameters, and finally form an environmental adaptability control parameter library to support the optimal control of the circuit breaker under variable environmental conditions.

[0089] In the embodiment of the present invention, multi-scenario simulation is performed on the environmental impact factor set to obtain the environmental change response model, which specifically includes:

[0090] According to the pre-collected historical operation data and environmental records, construct a typical environmental scenario data set, and perform data enhancement and mutation processing on the typical environmental scenario data set to obtain an extended environmental scenario library;

[0091] Use the extended environmental scenario library to define the simulation experiment scheme, obtain the scenario test matrix, and perform simulation on the circuit breaker response according to the scenario test matrix to obtain multi-scenario response data;

[0092] Perform regression analysis on the multi-scenario response data to obtain a quantitative relationship model between environmental factors and circuit breaker performance, and perform non-linear characteristic analysis on the quantitative relationship model to obtain a set of environmental critical points;

[0093] Based on the set of environmental critical points and the quantitative relationship model, construct an environmental change sensitivity model, and verify and calibrate the environmental change sensitivity model to obtain a calibrated sensitivity model;

[0094] Integrate the calibrated sensitivity model and the quantitative relationship model to construct a complete environmental change response model, which can predict the impact of different environmental conditions on the performance of the circuit breaker.

[0095] In this embodiment, historical operation data is collected and sorted out, including the operation records, fault records, maintenance records, etc. of the circuit breaker under different environmental conditions. The environmental parameters are associated with the performance indicators to form an initial environmental-performance relationship data set. Statistical analysis methods are applied to identify typical environmental scenarios, such as high-temperature and high-humidity environments, low-temperature and low-pressure environments, high-altitude environments, strong-corrosion environments, etc. A typical environmental scenario data set covering the working environment range of the circuit breaker is constructed. The data enhancement technology (such as noise injection, parameter perturbation or Monte Carlo simulation) is applied to the typical environmental scenario data set to generate richer variants of environmental scenarios, expand the scenario coverage range, improve the generalization ability of the model, and form an extended environmental scenario library. Based on the extended environmental scenario library, a multi-factor experimental scheme is designed, the test scenarios and evaluation indicators are defined, and the orthogonal experimental design or the optimal experimental design method is used to optimize the experimental workload to generate a scenario test matrix. According to the scenario test matrix, using multi-physics field coupling simulation tools (such as COMSOL Multiphysics, Ansys, etc.), the response characteristics of the circuit breaker under different environmental conditions, such as mechanical characteristics, electrical characteristics and thermal characteristics, are simulated to generate multi-scenario response data. The regression analysis method (such as multiple linear regression, support vector regression or neural network regression) is applied to the multi-scenario response data to establish the mapping relationship between the environmental factors and the performance indicators of the circuit breaker, quantify the influence degree and direction of different environmental factors on the performance, and form a quantitative relationship model. The nonlinear characteristic analysis is carried out on the quantitative relationship model to identify the nonlinear regions and mutation points of the influence of environmental parameter changes on the performance, and locate the environmental thresholds that may cause significant changes in the performance of the circuit breaker, such as the contact icing temperature, the insulation material softening temperature, etc., to form a set of environmental critical points. Combining the set of environmental critical points and the quantitative relationship model, a model describing the sensitivity of the circuit breaker to environmental changes is constructed, the amplitude of the performance change caused by a unit environmental change is quantified, and an environmental change sensitivity model is generated. Through experimental verification or historical data comparison, the environmental change sensitivity model is verified and calibrated, the model parameters are corrected, and the prediction accuracy is improved to form a calibrated sensitivity model. The calibrated sensitivity model and the quantitative relationship model are integrated to construct a unified environmental change response model framework, realizing the end-to-end mapping from environmental parameter input to performance impact output, and finally forming a complete environmental change response model, providing a theoretical basis for the environmental adaptability control of the circuit breaker.

[0096] In the embodiment of the present invention, the fault prediction and diagnosis module is used to perform fault mode identification and predictive diagnosis on the circuit breaker based on the environmental adaptability control parameter library and the circuit breaker health feature map, and obtain a fault risk warning matrix, specifically for:

[0097] Extracting fault-related features from the circuit breaker health feature map to obtain a fault feature set, and establishing a fault mode library according to the fault feature set to obtain a set of typical fault modes;

[0098] Calculate the similarity of the typical fault mode set to obtain the fault mode similarity network, and based on the fault mode similarity network, conduct fault mode clustering and evolution analysis to obtain the fault development path diagram;

[0099] Combined with the environmental adaptability control parameter library, conduct environmental impact analysis on the fault development path diagram to obtain the environmentally sensitive fault development model, and based on the environmentally sensitive fault development model, construct a fault prediction algorithm to obtain the fault probability prediction function;

[0100] Use the fault probability prediction function to predict the fault risks of different components of the circuit breaker to obtain the fault risk assessment results, and classify the severity and urgency of the fault risk assessment results to obtain the risk level matrix;

[0101] Define the risk warning strategy based on the risk level matrix to obtain multi-level warning rules, and generate the fault risk warning matrix according to the multi-level warning rules, including fault type, occurrence probability, impact degree and recommended measures.

[0102] In this embodiment, feature patterns related to the fault state are extracted from the circuit breaker health feature map, such as abnormal node health index, change in edge association strength, abnormal state transition probability, etc., to construct a fault characterization feature set. Integrate the historical fault case base and industry expert knowledge to establish a fault mode library covering common fault types of circuit breakers, including various fault modes such as contact ablation, actuator jamming, arc extinguishing device failure, control circuit failure, etc., to form a set of typical fault modes. Apply various similarity measurement methods (such as cosine similarity, Euclidean distance, Mahalanobis distance, or DTW distance) to calculate the similarity degree between different fault modes, construct a fault mode similarity network that quantifies the association relationship of fault types, apply community discovery algorithms (such as Louvain method, spectral clustering, or InfoMap algorithm) to the fault mode similarity network to identify closely associated fault type groups, perform fault mode clustering, analyze the evolution relationship and development law between fault types, construct a fault development path map reflecting the evolution path of faults from early signs to severe faults, combine the environment-performance mapping model in the environmental adaptability control parameter library, analyze the influence mechanism of different environmental conditions on the fault development process, such as high-temperature environment accelerating the deterioration of contact materials, humid environment promoting insulation aging, etc., construct an environment-sensitive fault development model, based on the environment-sensitive fault development model, apply advanced machine learning algorithms (such as random forest, gradient boosting tree, deep learning network, or survival analysis model) to construct a fault prediction algorithm, input the current state features and environmental conditions, output the future fault probability and expected occurrence time, form a fault probability prediction function, use the fault probability prediction function to predict the fault risks of each key component of the circuit breaker (such as main contact system, actuator, arc extinguishing device, control circuit, etc.), calculate the occurrence probability, confidence interval, and expected occurrence time window of various faults, form a fault risk assessment result, apply the risk assessment matrix method to the fault risk assessment result, conduct a hierarchical assessment of the risk from two dimensions of the severity of fault consequences (such as safety risk, economic loss, system impact, etc.) and urgency (such as occurrence probability, expected time window, etc.), construct a risk level matrix, based on the risk level matrix, design a hierarchical early warning strategy, define the early warning level, early warning method, response time limit, and disposal measures corresponding to different risk levels, form a multi-level early warning rule system, and generate a fault risk early warning matrix containing fault types, occurrence probability, impact degree, early warning level, and recommended measures according to the multi-level early warning rules, providing a scientific basis for the preventive maintenance decision-making of the circuit breaker.

[0103] In the embodiment of the present invention, based on the environment-sensitive fault development model, a fault prediction algorithm is constructed to obtain a fault probability prediction function, which specifically includes:

[0104] Conduct a parameter sensitivity analysis on the environment-sensitive fault development model, identify key prediction factors, obtain a prediction feature subset, and construct multiple prediction models based on the prediction feature subset to obtain a set of prediction models;

[0105] Verify the historical data of the prediction model set, calculate the prediction accuracy index, obtain the model evaluation result, and select the optimal basic model according to the model evaluation result to obtain the basic prediction model;

[0106] Introduce a time window sliding mechanism on the basis of the basic prediction model to enhance the ability to capture time series characteristics, obtain a time series enhanced prediction model, and introduce an uncertainty quantification method into the time series enhanced prediction model to obtain a probability distribution prediction function;

[0107] Perform online learning optimization on the probability distribution prediction function to improve the adaptive ability of the model, obtain an adaptive prediction algorithm, and integrate multiple adaptive prediction algorithms through an ensemble learning method to obtain a robust prediction system;

[0108] Based on the robust prediction system, construct an end-to-end fault probability prediction function that can real-time output the occurrence probability of different fault types and their confidence intervals.

[0109] In this embodiment, a sensitivity analysis is performed on the environmental - sensitive fault development model to evaluate the influence degree of different parameters on the model prediction results. Methods such as analysis of variance, partial derivative method, or Sobol index are used to quantify the parameter sensitivity, and key factors that have the most influence on fault prediction, such as the number of electrical operations, the degree of contact ablation, and the mechanism friction coefficient, are identified to form a prediction feature subset. Based on the prediction feature subset, multiple fault prediction models are constructed, including statistical models (such as time - series models, life - analysis models), machine - learning models (such as support vector machines, random forests, gradient - boosting trees), and deep - learning models (such as long - short - term memory networks, convolutional neural networks, variational autoencoders), etc., to form a prediction model set. Historical data is used for cross - validation of the prediction model set to evaluate the prediction accuracy, stability, and generalization ability of each model, and performance indicators such as ROC curves, F1 scores, and mean absolute error are calculated to generate model evaluation results. Based on the model evaluation results, the model with the optimal comprehensive performance is selected as the basic prediction model, balancing prediction accuracy and computational complexity to form the basic prediction model. A time - window sliding mechanism is introduced into the basic prediction model to capture the long - and short - term dependencies of time - series data by dynamically adjusting the feature - extraction window, enhancing the model's learning ability for time - series characteristics, and generating a time - series enhanced prediction model. Uncertainty quantification techniques such as Bayesian methods, ensemble learning, or Monte Carlo simulation are introduced into the time - series enhanced prediction model, enabling the model to not only output prediction results but also provide confidence intervals and probability distributions of the predictions, forming a probability - distribution prediction function to effectively quantify the uncertainty of the prediction results. The probability - distribution prediction function is deployed into an online learning framework, enabling the model to learn from continuously generated new data, continuously update and adjust model parameters, improve the model's adaptive ability and timeliness, and generate an adaptive prediction algorithm. Ensemble learning methods (such as Bagging, Boosting, or Stacking) are applied to integrate multiple adaptive prediction algorithms with different characteristics, leveraging the complementarity between algorithms to improve the overall prediction performance and robustness, constructing a robust prediction system. Based on the robust prediction system, an end - to - end fault - probability prediction function is constructed to achieve a complete mapping from multi - source monitoring data input to the output of fault probabilities and their confidence intervals, and finally form a prediction function that can accurately predict the occurrence probabilities and uncertainties of different fault types.

[0110] In a specific embodiment, the process of constructing the end - to - end fault - probability prediction function is as follows:

[0111] Design an end - to - end fault - probability prediction function architecture with multi - level fusion. Define the input space to include multi - source heterogeneous monitoring data streams, such as electrical parameters, mechanical characteristics, environmental conditions, etc.; define the output space to include the occurrence probabilities and their confidence intervals of various faults, and achieve a full - process mapping from data to prediction results.

[0112] Unify the processing of multi-source heterogeneous data. Apply the sliding window method to time-series data to extract time-domain features (such as mean, standard deviation, skewness, kurtosis, etc.) and frequency-domain features (such as spectral power, band energy, etc.). Implement a feature dynamic selection mechanism, comprehensively evaluate the feature importance based on methods such as Fisher score, mutual information, and recursive feature elimination, and select the optimal feature subset.

[0113] Design dedicated prediction base models for different fault types, including gradient boosting trees, random forests, deep neural networks, etc. Implement uncertainty quantification based on Bayesian methods, introduce techniques such as Bayesian neural networks or Monte Carlo dropout, and calculate the prediction distribution through multiple samplings. Construct a deep ensemble learning framework, integrate the outputs of multiple heterogeneous prediction models, and calculate the integrated prediction probability as:

[0114] ; where represents the prediction probability of the th base model for the th type of fault, is the model weight; represents the multi-dimensional feature vector of the circuit breaker, which is the input parameter of the fault prediction model. The model weight is dynamically optimized and adjusted through performance evaluation on the validation set.

[0115] Introduce an adaptive weight adjustment mechanism, dynamically adjust the weight distribution according to the performance of each model under different working conditions, and improve the system robustness. Implement the time-scale conversion of fault prediction, extend from short-term state prediction to long-term trend prediction, construct a remaining useful life (RUL) estimation model, and combine survival analysis methods to predict the fault occurrence time window.

[0116] Based on the integrated output of the prediction model, construct a complete confidence interval for the fault probability. Generate multiple sets of prediction values through Bootstrap sampling and model parameter perturbation, and calculate the empirical quantiles to form the confidence interval:

[0117] ; where represents the th sorted prediction value, is the th sorted prediction value, is the significance level (usually taken as 0.05), is the sample size, represents the confidence interval of the prediction probability of the th type of fault. For example, if the system predicts that the probability of the circuit breaker contact ablation fault is 30%, its confidence interval CI may be [25%, 35%], which means that at the 95% confidence level (assuming α = 0.05), the true fault probability falls within this interval.

[0118] Design a prediction reliability scoring function that comprehensively considers the width of the confidence interval and the entropy of the prediction probability distribution to quantify the credibility of the current prediction. For different working condition requirements, construct an environment-sensitive prediction correction mechanism to adaptively adjust the prediction probability based on the environmental condition vector, making the prediction results more adaptable to the actual operating environment.

[0119] Implement an online learning and adaptive update mechanism for the prediction model, and use incremental learning methods to continuously update the model parameters. Introduce the Elastic Weight Consolidation (EWC) mechanism to prevent catastrophic forgetting, maintain the model's memory ability for historical patterns, and at the same time adapt to the characteristics of new data distributions.

[0120] Design a three-level fusion strategy for the feature layer, model layer, and decision layer. Fuse the features extracted from different data sources at the feature layer; integrate the prediction results of multiple algorithms at the model layer; combine expert rules and the historical fault case library for final judgment at the decision layer. Add an interpretability module to make the prediction results traceable and understandable through feature importance analysis, SHAP value calculation, and decision path visualization.

[0121] The finally constructed end-to-end fault probability prediction function realizes an accurate mapping from monitoring data to the prediction probabilities and confidence intervals of multiple fault types.

[0122] In the embodiment of the present invention, the cooperative control decision module is used to optimize and generate a multi-breaker cooperative control strategy based on the fault risk warning matrix to obtain an optimal control instruction sequence, specifically used for:

[0123] Conduct multi-breaker correlation analysis on the fault risk warning matrix, identify control dependencies, obtain the breaker cooperative control network, and construct a system-level state space model based on the breaker cooperative control network to obtain the system state representation;

[0124] According to the system state representation, define the cooperative control objective function to obtain a multi-objective optimization problem, and analyze the constraint conditions of the multi-objective optimization problem to obtain a control constraint set;

[0125] Based on the multi-objective optimization problem and the control constraint set, construct a cooperative control strategy optimization algorithm to obtain a candidate control strategy set, and conduct simulation evaluation on the candidate control strategy set to obtain a strategy performance evaluation matrix;

[0126] According to the strategy performance evaluation matrix, select the best control strategy to obtain a decision control plan, and conduct time series decomposition on the decision control plan to obtain a control instruction time series plan;

[0127] Generate detailed control instruction parameters based on the control instruction time series plan to obtain an optimal control instruction sequence, including execution time, execution device, operation parameters, and expected results.

[0128] In this embodiment, complex network analysis is performed on the fault risk warning matrix to identify the control dependencies, influence propagation paths, and key control nodes in the multi - circuit breaker system, construct a collaborative control network reflecting the interaction and coordination relationship between circuit breakers. Based on the collaborative control network, the state - space modeling method is applied to establish a system - level state - space model describing the overall dynamic behavior of the multi - circuit breaker system, including state variables, control variables, disturbance variables, and observation variables, forming a system state representation. Based on the system state representation, a collaborative control objective function reflecting the overall performance objectives of the system is defined, including multiple objective dimensions such as maximizing system reliability, minimizing operation cost, optimizing maintenance efficiency, and load balancing, constructing a multi - objective optimization problem description. Analyze the constraint conditions of the multi - objective optimization problem, considering various constraints for the safe and stable operation of the power system, such as current - carrying capacity constraints, voltage stability constraints, power balance constraints, equipment operation constraints, etc., to form a complete set of control constraints. Based on the multi - objective optimization problem and the set of control constraints, an advanced optimization algorithm (such as genetic algorithm, particle swarm optimization, differential evolution algorithm, or multi - agent reinforcement learning) is applied to construct a collaborative control strategy optimization algorithm, generate multiple candidate control strategies that meet the constraint conditions, forming a candidate control strategy set. Conduct multi - scenario simulation evaluation on the candidate control strategy set, test the performance of each strategy under normal operating conditions, fault conditions, and extreme conditions, and the evaluation indicators include reliability, economy, real - time performance, and robustness, etc., generating a strategy performance evaluation matrix. Based on the strategy performance evaluation matrix, a multi - criterion decision - making method (such as analytic hierarchy process, TOPSIS, or fuzzy comprehensive evaluation method) is applied to select the control strategy with the best comprehensive performance, forming the final decision - making control plan. Decompose the decision - making control plan in time series, decompose the overall control plan into a sequence of control steps with clear execution order and time nodes, considering the front - to - back dependency relationship and the possibility of parallel execution, forming a control instruction time - series plan. Based on the control instruction time - series plan, generate control instructions containing detailed execution parameters, clearly specifying information such as execution time, execution device, operation type, parameter settings, and expected results, and integrate them to form an optimal control instruction sequence, providing precise guidance for the actual execution of circuit breaker collaborative control.

[0129] In the embodiment of the present invention, the safety execution and feedback module is used to perform safety encryption verification and hierarchical execution on the control instructions based on the optimal control instruction sequence, and real - time feedback the execution effect to obtain a circuit breaker control execution report, specifically used for:

[0130] Classify the optimal control instruction sequence according to the safety level to obtain a hierarchical instruction set, and perform identity authentication and permission verification on the hierarchical instruction set to obtain an authorized instruction set;

[0131] Perform encryption processing on the authorized instruction set to ensure transmission security, obtain a secure instruction packet, and generate an execution plan according to the secure instruction packet to obtain an instruction execution scheduling plan;

[0132] Execute the control instructions step by step based on the instruction execution scheduling scheme, capture the execution status in real time, obtain the data stream of the execution process, and perform real-time analysis on the data stream of the execution process to obtain the execution effect evaluation result;

[0133] According to the execution effect evaluation result, adjust the instruction execution, optimize the subsequent instruction execution, obtain the adaptive execution feedback, and generate the execution process log based on the adaptive execution feedback to record the detailed execution situation;

[0134] Integrate the execution effect evaluation result and the execution process log to generate a circuit breaker control execution report, including the execution success rate, abnormal situations, performance improvement, and subsequent suggestions.

[0135] In this embodiment, the optimal control instruction sequence is classified according to the operation risk level and the degree of safety impact. The instructions are divided into different safety levels such as high-risk instructions (such as circuit breaker opening and closing operations), medium-risk instructions (such as protection parameter modification), and low-risk instructions (such as status query), etc., to form a hierarchical instruction set. The identity authentication and permission verification mechanism is applied to the hierarchical instruction set. Multifactor authentication technologies (such as digital certificates, dynamic passwords, biometric features, etc.) are used to verify the identity of the operator. Role-based access control (RBAC) is used to ensure that the operation permissions match, and a verified authorized instruction set is generated. A high-strength encryption algorithm (such as AES-256, RSA, or elliptic curve encryption) is applied to the authorized instruction set for data encryption, and a digital signature is generated to ensure the integrity of the instructions. A secure communication tunnel is constructed to prevent man-in-the-middle attacks, and a secure instruction packet is formed. Based on the secure instruction packet, a detailed execution plan is formulated, including execution sequence, time window, resource allocation, emergency plan, etc. Considering the dependency relationship and the possibility of parallel execution between instructions, an instruction execution scheduling scheme is formed. According to the instruction execution scheduling scheme, the control instructions are distributed to each execution terminal through a secure communication network, and the receipt confirmation and execution status of the instructions are monitored. The status data, response signals, and feedback information during the execution process are collected in real time to form an execution process data stream. Real-time analysis algorithms are applied to the execution process data stream to evaluate the accuracy, timeliness, and effectiveness of the instruction execution, calculate key performance indicators such as execution success rate and response time, and generate an execution effect evaluation result. Based on the execution effect evaluation result, an adaptive control strategy is used to adjust the subsequent instruction execution, such as adjusting the execution timing, optimizing the operation parameters, or activating the emergency plan, etc., to form a closed-loop control adaptive execution feedback mechanism. The whole process information of the instruction execution is recorded in detail, including instruction content, execution time, operation object, execution result, abnormal situation, and handling measures, etc. A structured execution process log is constructed, and the execution effect evaluation result and the execution process log are integrated to form a comprehensive circuit breaker control execution report, including execution statistical analysis (such as success rate, average response time), summary of abnormal situations, performance improvement suggestions, and subsequent operation and maintenance suggestions, etc., to provide data support for the continuous optimization of the system and management decision-making.

[0136] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

[0137] It should be noted that in this text, the term "comprise", "include" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or apparatus comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or apparatus. Without further limitation, an element limited by the phrase "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or apparatus comprising said element.

[0138] In the description of the present invention, it should be understood that terms such as "first", "second", etc. are used only for distinguishing descriptions and cannot be construed as indicating or implying relative importance.

[0139] In the description of the present invention, unless otherwise specified, the meaning of "a plurality of" is two or more.

[0140] In the description of the present invention, the meaning of "several" is one or more, and the meaning of "a large number of" is two or more.

[0141] In the description of this specification, the descriptions referring to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0142] For the formulas in this specification, the dimensional quantities are removed and only the numerical values are calculated. The formula is obtained by collecting a large amount of data and performing software simulation to get a formula that is closest to the actual situation. The preset parameters and threshold values in the formula are set by those skilled in the art according to the actual situation.

[0143] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the claims and their equivalents.

Claims

1. A control system for managing a primary-secondary integrated circuit breaker, characterized in that Including: A data acquisition and synchronization module, which is used to comprehensively acquire the operation parameters of the primary-secondary integrated circuit breaker and perform time synchronization processing to obtain a standardized operation data set; A state perception and feature extraction module, which is used to perform multi-dimensional state perception and key feature extraction based on the standardized operation data set to obtain a circuit breaker health feature map; An operating environment adaptability evaluation module, which is used to perform operating environment adaptability evaluation and parameter self-adaptive optimization based on the circuit breaker health feature map to obtain an environment adaptability control parameter library; A fault prediction and diagnosis module, which is used to perform fault mode recognition and predictive diagnosis on the circuit breaker based on the environment adaptability control parameter library and the circuit breaker health feature map to obtain a fault risk warning matrix; A cooperative control decision-making module, which is used to optimize the cooperative control strategy of multiple circuit breakers and generate a decision based on the fault risk warning matrix to obtain an optimal control instruction sequence; A safety execution and feedback module, which is used to perform safety encryption verification and hierarchical execution on the control instruction based on the optimal control instruction sequence, and real-time feedback the execution effect to obtain a circuit breaker control execution report; Each module is connected through a secure communication network to achieve secure data transmission and sharing between modules.

2. The control system for managing a primary-secondary integrated circuit breaker according to claim 1, wherein The comprehensive acquisition and time synchronization processing of the operation parameters of the primary-secondary integrated circuit breaker to obtain a standardized operation data set includes: Multi-channel acquisition of the electrical parameters, mechanical state and environmental conditions of the circuit breaker to obtain an original heterogeneous data stream, and perform timestamp calibration and data quality evaluation on the original heterogeneous data stream to obtain time-synchronized data; Perform anomaly detection and missing value repair on the time-synchronized data to obtain a cleaned data set, and perform data denoising and signal enhancement processing on the cleaned data set to obtain an optimized data set; Perform feature normalization and standardization processing on the optimized data set to obtain a standard feature matrix, and calculate the correlation between parameters based on the standard feature matrix to obtain a parameter correlation matrix; Perform feature importance evaluation on the parameter correlation matrix to obtain a parameter weight distribution, and perform priority sorting and screening on the features according to the parameter weight distribution to obtain a key parameter set; Perform data classification and organization on the key parameter set to obtain multi-dimensional parameter subsets, and construct a standardized operation data set according to the multi-dimensional parameter subsets, including an electrical parameter subset, a mechanical state subset and an environmental condition subset.

3. The control system for managing a primary-secondary integrated circuit breaker according to claim 1, wherein, The multi-dimensional state perception and key feature extraction based on the standardized operation data set to obtain a circuit breaker health feature map includes: Perform deep feature learning on the standardized operation data set to obtain a latent feature representation, and establish a multi-level feature abstraction model according to the latent feature representation to obtain a hierarchical feature structure; Perform state mode recognition on the hierarchical feature structure to obtain a circuit breaker working state feature set, and extract state transition features according to the circuit breaker working state feature set to obtain a state transition sequence; Perform temporal correlation analysis on the state transition sequence to obtain the state evolution law, and construct a circuit breaker state transition probability matrix based on the state evolution law to obtain a Markov state model; Perform health quantification analysis on the Markov state model to obtain a health assessment index system, and calculate the health index of each component of the circuit breaker based on the health assessment index system to obtain a health status distribution map; Perform topological structure modeling on the health status distribution map to obtain a circuit breaker health feature map, including node health status, edge association strength, and state evolution trend information.

4. The control system for managing a primary-secondary integrated circuit breaker according to claim 3, wherein, Perform state pattern recognition on the hierarchical feature structure to obtain a circuit breaker working state feature set, including: Apply multiple classification algorithms to the hierarchical feature structure for ensemble learning to obtain an initial state classification result, and perform voting fusion on the initial state classification result to obtain a state recognition fusion result; Combine a preset historical state library and an expert knowledge library to verify and correct the state recognition fusion result to obtain an accurate working state identifier, and extract a state feature vector based on the working state identifier to obtain a state feature representation; Construct a state feature space based on the state feature representation, and perform density clustering analysis in the state feature space to obtain a working state clustering center; Perform boundary learning on the working state clustering center to obtain a state boundary descriptor, and identify the state transition critical point based on the state boundary descriptor to obtain the state transition trigger condition; Integrate the working state clustering center, the state boundary descriptor, and the state transition trigger condition to form a complete circuit breaker working state feature set.

5. The control system for managing a primary-secondary integrated circuit breaker according to claim 1, wherein, Perform operating environment adaptability assessment and parameter adaptive optimization based on the circuit breaker health feature map to obtain an environment adaptability control parameter library, including: Perform environmental factor association analysis on the circuit breaker health feature map to obtain an environmental sensitivity matrix, and identify key environmental impact factors based on the environmental sensitivity matrix to obtain an environmental impact factor set; Perform multi-scenario simulation on the environmental impact factor set to obtain an environmental change response model, and define an adaptive parameter adjustment strategy based on the environmental change response model to obtain a parameter adjustment rule library; Construct an adaptive control parameter space based on the parameter adjustment rule library, and perform multi-objective optimization in the adaptive control parameter space to obtain a parameter optimization combination; Perform verification tests on the parameter optimization combination to obtain a performance evaluation result, and perform parameter fine-tuning based on the performance evaluation result to obtain a refined control parameter set; Establish an environment-parameter mapping relationship model based on the refined control parameter set to obtain an environment adaptability control parameter library.

6. The control system for managing a primary-secondary integrated circuit breaker according to claim 5, wherein Perform multi-scenario simulation on the environmental impact factor set to obtain an environmental change response model, including: Construct a typical environmental scenario data set based on pre-collected historical operation data and environmental records, and perform data augmentation and mutation processing on the typical environmental scenario data set to obtain an extended environmental scenario library; Define a simulation experiment plan using the extended environmental scenario library to obtain a scenario test matrix, and simulate the breaker response according to the scenario test matrix to obtain multi-scenario response data; Conduct a regression analysis on the multi-scenario response data to obtain a quantitative relationship model between environmental factors and breaker performance, and perform a non-linear characteristic analysis on the quantitative relationship model to obtain a set of environmental critical points; Construct an environmental change sensitivity model based on the set of environmental critical points and the quantitative relationship model, and verify and calibrate the environmental change sensitivity model to obtain a calibrated sensitivity model; Integrate the calibrated sensitivity model and the quantitative relationship model to construct a complete environmental change response model.

7. The control system for managing a primary-secondary integrated circuit breaker according to claim 1, wherein Based on the environmental adaptability control parameter library and the breaker health characteristic map, conduct fault mode identification and predictive diagnosis on the breaker to obtain a fault risk warning matrix, including: Extract fault-related features based on the breaker health characteristic map to obtain a fault feature set, and establish a fault mode library according to the fault feature set to obtain a set of typical fault modes; Calculate the similarity of the set of typical fault modes to obtain a fault mode similarity network, and perform fault mode clustering and evolution analysis according to the fault mode similarity network to obtain a fault development path diagram; Combine the environmental adaptability control parameter library to conduct an environmental impact analysis on the fault development path diagram to obtain an environmentally sensitive fault development model, and construct a fault prediction algorithm based on the environmentally sensitive fault development model to obtain a fault probability prediction function; Use the fault probability prediction function to predict the fault risks of different components of the breaker to obtain a fault risk assessment result, and perform severity and urgency grading on the fault risk assessment result to obtain a risk level matrix; Define a risk warning strategy based on the risk level matrix to obtain multi-level warning rules, and generate a fault risk warning matrix according to the multi-level warning rules, including fault type, occurrence probability, impact degree, and recommended measures.

8. The control system for managing a primary-secondary integrated circuit breaker according to claim 7, characterized in that, Based on the environmentally sensitive fault development model, construct a fault prediction algorithm to obtain a fault probability prediction function, including: Conduct a parameter sensitivity analysis on the environmentally sensitive fault development model to identify key prediction factors to obtain a prediction feature subset, and construct multiple prediction models based on the prediction feature subset to obtain a set of prediction models; Verify the set of prediction models with historical data, calculate the prediction accuracy index to obtain a model evaluation result, and select the optimal basic model according to the model evaluation result to obtain a basic prediction model; Introduce a time window sliding mechanism on the basis of the basic prediction model to enhance the ability to capture time series characteristics to obtain a time series enhanced prediction model, and introduce an uncertainty quantification method into the time series enhanced prediction model to obtain a probability distribution prediction function; Conduct online learning optimization on the probability distribution prediction function to obtain an adaptive prediction algorithm, and integrate multiple adaptive prediction algorithms through an ensemble learning method to obtain a robust prediction system; Construct an end-to-end fault probability prediction function based on the robust prediction system.

9. The control system for managing a primary-secondary integrated circuit breaker according to claim 1, wherein Based on the fault risk early warning matrix, optimize the multi - breaker collaborative control strategy and generate a decision, obtaining an optimal control instruction sequence, including: Conduct multi - breaker correlation analysis on the fault risk early warning matrix, identify control dependencies, obtain a breaker collaborative control network, and build a system - level state - space model based on the breaker collaborative control network to obtain a system state representation; According to the system state representation, define a collaborative control objective function, obtain a multi - objective optimization problem, and conduct constraint condition analysis on the multi - objective optimization problem to obtain a control constraint set; Based on the multi - objective optimization problem and the control constraint set, build a collaborative control strategy optimization algorithm, obtain a candidate control strategy set, and conduct simulation evaluation on the candidate control strategy set to obtain a strategy performance evaluation matrix; According to the strategy performance evaluation matrix, select the best control strategy, obtain a decision - making control plan, and conduct time - series decomposition on the decision - making control plan to obtain a control instruction time - series plan; Generate detailed control instruction parameters based on the control instruction time - series plan to obtain an optimal control instruction sequence, including execution time, execution device, operation parameters, and expected results.

10. The control system for managing a primary-secondary integrated circuit breaker according to claim 1, wherein, Based on the optimal control instruction sequence, conduct security encryption verification and hierarchical execution of control instructions, and provide real - time feedback on the execution effect to obtain a breaker control execution report, including: Classify the optimal control instruction sequence by security level to obtain a hierarchical instruction set, and conduct identity authentication and permission verification on the hierarchical instruction set to obtain an authorized instruction set; Encrypt the authorized instruction set to obtain a secure instruction package, and generate an execution plan based on the secure instruction package to obtain an instruction execution scheduling plan; Execute control instructions step - by - step based on the instruction execution scheduling plan to obtain an execution - process data stream, and conduct real - time analysis on the execution - process data stream to obtain an execution - effect evaluation result; According to the execution - effect evaluation result, adjust the instruction execution, optimize subsequent instruction execution, obtain an adaptive execution feedback, and generate an execution - process log based on the adaptive execution feedback to record the detailed execution situation; integrate the execution - effect evaluation result and the execution - process log to generate a breaker control execution report.

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