4621 results about "Dependability" patented technology

The invention discloses an intelligent operation and maintenance system and method for a high-voltage switch cabinet based on digital twinning, relates to the technical field of intelligent power grids, and solves the problems of nonlinear effect modeling distortion, cross-spatio-temporal scale coupling deviation accumulation, time sequence real-time contradiction and insufficient extreme working condition adaptation in the prior art. Hysteresis parameters of the ferromagnetic material are dynamically calibrated through a quantum annealing optimization algorithm, and electromagnetic-thermal field strong coupling synchronous calculation is realized by combining multi-scale mesh generation and an implicit thermal field iterative algorithm; constructing an incremental transfer learning framework to fuse aging features and real-time data, and correcting boundary conditions of the model by adopting four-dimensional variational assimilation; establishing a hybrid verification platform to dynamically feed back extreme working condition parameters, and generating a credible operation and maintenance instruction in combination with a block chain; according to the method, the contact temperature rise prediction precision, the residual life evaluation reliability and the circuit breaker transient response real-time performance are remarkably improved, and active immune type intelligent operation and maintenance of the high-voltage switch cabinet under the extreme working condition are achieved.

The invention relates to the technical field of hard disk testing and verification, in particular to a multi-thread-based solid state disk reliability automatic testing method and system.The method comprises the steps that firstly, SMART information is deeply analyzed through microsecond-level high-granularity continuous performance monitoring, and multi-thread parallel processing is assisted; according to the method, fine performance fluctuation of the solid state disk under the concurrent load can be quickly captured, a fault mode can be identified, then early warning is realized by utilizing the extracted multi-dimensional features and a machine learning model, and a detailed fault diagnosis report is generated; and through dynamic error correction code strength verification and data integrity verification under pressure, an internal error correction mechanism of the solid state disk is actively detected and optimized. And finally, in combination with prediction reliability modeling, the system can estimate the remaining service life and predict faults, and provides product optimization suggestions for design, manufacturing and firmware optimization of the solid state disk, so that automation, intelligence and full life cycle management of the fault detection reliability of the solid state disk are realized.

The invention discloses an intelligent risk identification and self-adaptive repair method, system and device for a software supply chain and a medium, belongs to the field of network security and automatic software engineering, and aims to solve the technical problem of how to accurately and comprehensively identify software code supply chain risks including code snippets. A reliable and efficient automatic closed-loop repair scheme is provided, and the technical defects that in the prior art, the software code supply chain recognition range is limited, the repair process is rigid and the reliability is low are overcome. Analyzing the declarative dependency; meanwhile, semantic traceability based on artificial intelligence is carried out on the code snippets, and a global software material list is generated; and performing intelligent mapping on the software components in the global software bill of materials and the vulnerability database to identify risks.

The invention belongs to the field of power system engineering, and discloses a power distribution automation terminal diagnosis method and system based on multi-source wave recording feature fusion, and the method comprises the steps: obtaining the electric quantity data and equipment operation state data collected by a power distribution automation terminal; performing adaptive decomposition on the electrical quantity data by using a variational mode decomposition algorithm to obtain an intrinsic mode function; constructing a deep residual network model, carrying out fusion analysis on the time-frequency domain features of the intrinsic mode function, and generating a fault feature vector; establishing a multi-dimensional evaluation matrix based on the fault feature vectors, and integrating a plurality of indexes to output fault types and credibility scores; according to the fault type and the credibility score, generating a fault isolation strategy based on a Petri network model; and executing a dynamically adjusted self-adaptive self-healing control algorithm. According to the method, complex and changeable fault modes can be effectively identified, a complete collaborative verification mechanism is formed, seamless connection from fault diagnosis to self-healing control is realized, and the operation reliability of the power distribution network is remarkably improved.

The invention discloses a drainage basin maintenance intelligent management system based on the Internet of Things and intelligent decision, and relates to the technical field of computers. Comprising a multi-source perception and edge access module which is used for realizing multi-protocol access, time and session alignment, data quality labeling, breakpoint resume and edge side anomaly preliminary screening of industrial data, low-power-consumption point location data and video and thermal image data. According to the invention, through the multi-source sensing and edge access module, various types of data such as industrial data, low-power-consumption point location data, video and thermal image data can be processed, the technical problems of multi-protocol access, time and session alignment, data quality labeling and breakpoint resume and the like are solved, the integrity and reliability of the data on the edge side are ensured, and the service life of the data is prolonged. The edge side anomaly preliminary screening function significantly improves the timeliness of early fault discovery, reduces the network bandwidth pressure, effectively solves the problems of anomaly cooperative detection and alarm flooding in a complex system, and improves the accuracy and timeliness of fault early warning.

The invention relates to the technical field of multi-agent collaboration, and discloses a multi-agent collaboration system with a task automatic decomposition and hierarchical rechecking mechanism. The system comprises a task planning framework construction module, a dynamic role matching module, a layered review triggering module and a cooperative execution monitoring module. Wherein the task planning framework construction module is used for generating a directed acyclic graph task framework consisting of a plurality of logic dependence subtasks after receiving a top-layer complex task; the dynamic role matching module screens skill matching degrees based on sub-node requirements, and dynamically binds execution agent identifiers and sub-task nodes; the hierarchical review triggering module is used for embedding configurable review nodes in a task framework, selecting a review hierarchy of AI cross validation or manual approval according to a sub-task risk level, and generating a node review strategy set; and the cooperative execution monitoring module generates a task execution track sequence according to the node state flow. According to the system, the efficiency and reliability of multi-agent cooperative processing of complex tasks are improved.

The invention provides a property system virtual-real mapping inspection method, device and equipment based on digital twinning and a medium, and belongs to the technical field of property inspection. Real-time mapping of an equipment entity and a virtual model is achieved through layered design of a physical layer, a data layer, a twinning layer and an application layer; collecting static / dynamic data and reducing noise, and establishing a global coordinate system; constructing a 1: 1 parameterized model and binding equipment attributes; the state analysis model is used for evaluating the equipment health degree, and virtual-real identification and work order visualization are triggered when abnormity occurs; the inspection path is dynamically optimized, and the processing efficiency is improved in combination with AR assistance and dual-stage verification; and finally, updating the model weight and adjusting the inspection strategy through data-driven acceptance feedback. Real-time synchronization and intelligent decision making of the equipment state are realized, the inspection efficiency is improved, the fault omission ratio is reduced, the resource allocation is optimized, and the operation and maintenance transparency and reliability are enhanced.

The invention relates to the technical field of data processing systems, and discloses a command and dispatch management method and system based on artificial intelligence, and the method comprises the steps: generating a space-time occupancy statement containing an occupancy area, a time window and a task state through an edge node, and broadcasting the space-time occupancy statement to an adjacent node; and a receiver performs local conflict verification according to a state mutual exclusion rule, and triggers priority-based lightweight negotiation to adjust an occupied time window when detecting that static observation and dynamic intervention states coexist in an overlapped space-time. Postward conflict processing of traditional centralized scheduling is converted into beforehand conflict avoidance, the response speed and the operation toughness of the system are remarkably improved, and meanwhile, through physical reachability verification and a channel self-adaption mechanism, the cooperation reliability in a complex environment is guaranteed.

The invention relates to a reservoir dam operation safety sky-land project intelligent sensing system and an operation method, and relates to the technical field of hydraulic engineering safety monitoring. The system is composed of a sky-land water conservancy project integrated monitoring and sensing system, a self-adaptive sampling module, a layered distributed architecture and a software and hardware integrated module, and multi-source data such as deformation, seepage, stress strain, vibration and environmental quantity are cooperatively collected through five dimensions of sky domain, airspace, territory, water domain and work domain. The monitoring frequency is dynamically adjusted by using an adaptive sampling strategy, and data cleaning, standardization, space-time registration and fusion processing are completed through a distributed architecture to generate a high-quality comprehensive data set. The system can realize total-factor and whole-process refined monitoring, effectively eliminates data islands, improves data quality and monitoring efficiency, has high reliability, real-time performance and expandability, and provides powerful data support and decision basis for dam safety assessment and intelligent early warning.

The invention discloses a digital twin-driven bridge full life cycle damage prediction and evaluation method and system, and belongs to the field of bridge structure health monitoring, and the method comprises the steps: obtaining a monitoring data set of a bridge structure; an initial digital twinborn model embedded with a micro physical layer is constructed and trained, the micro physical layer constructs a damage evolution model applied with monotonic physical constraint based on multi-source monitoring data, and a damage evolution trajectory of the bridge in a future time period is predicted through the damage evolution model based on the physical parameter vector; in combination with an uncertainty quantification method, generating a time-varying reliability index of the bridge in a future time period; and based on the time-varying reliability index, constructing and solving a maintenance decision optimization model to generate a maintenance decision of the bridge. According to the invention, the physical authenticity and reliability of the long-term prediction result are ensured.

The invention relates to the technical field of informatization processing, and provides an IT asset intelligent management system and an implementation method thereof, and the system comprises an asset discovery subsystem which outputs a standardized asset list through a feature comparison and deduplication fusion program; the unified modeling subsystem forms an asset topological graph by processing association and dependence between information technology assets; the intelligent analysis and prediction subsystem outputs insight results and prediction indexes about resource capacity prediction, anomaly detection, cost optimization suggestions, risk trends and the like; the life cycle management subsystem drives an automatic operation and maintenance workflow through a strategy engine to realize closed-loop management of the information technology asset state; the security and compliance subsystem generates an alarm, a repair suggestion or executes an automated repair policy. According to the invention, intelligence, automation and safety compliance of information technology asset management are realized, and the efficiency and reliability of information technology operation, maintenance and management are significantly improved.

The invention provides a multi-target production scheduling method and system based on reinforcement learning, and the method comprises the steps: carrying out the matching analysis based on a task demand list and an equipment capability baseline, and obtaining a scheduling constraint space containing a task execution dependency relationship and an equipment operation state boundary condition; performing structured feature extraction on the scheduling constraint space through a graph neural network, generating a production scheduling association graph containing node features and edge features, and calling a multi-target reinforcement learning strategy model to perform strategy iteration optimization on the production scheduling association graph, obtaining a scheduling strategy parameter set fusing the task completion timeliness and the resource utilization balance degree; and generating an equipment task time sequence allocation scheme. According to the invention, the overall effectiveness and reliability of production scheduling can be effectively improved.

The invention belongs to the technical field of automatic control, and relates to a self-adaptive control method based on multi-physical modeling. According to the method, by collecting multi-source data of a controlled object, a thermal, electric and force coupling relation used for control analysis is established so as to describe dynamic responses under different operation conditions. And calculating stress, motor power, energy consumption and temperature rise change in the operation process based on a coupling relation to obtain system performance data, verifying stability and safety of different control parameter combinations in a simulation environment, and obtaining performance indexes including operation retardation risk, overload safety margin and safety response time limit. And according to a simulation result, under the condition of meeting safety constraints, taking energy consumption and temperature rise as optimization targets, adjusting control parameters, generating optimized control parameter configuration data, and feeding back the optimized control parameter configuration data to a control unit, so that closed-loop adaptive control and performance optimization are realized. According to the invention, through multi-physical coupling modeling and simulation optimization, the adaptability and reliability of the automatic control system are improved.

The invention provides a robot control method based on Model-Based and RL, and relates to the technical field of automatic control. According to the method, a high-level task atlas model and a predefined task action set are constructed, a target task is mapped into a state node, graph search is carried out in combination with a current world state, an optimal task action path is planned, and a first action instruction is output. And calling a baseline strategy in the simulation environment to generate a control instruction, executing feasibility verification, compensating the deviation between the simulation and the real environment through the error prediction model, correcting the real control instruction, and then driving the robot to execute. And dynamically updating the success probability weight of state transition in the atlas based on the execution result, and feeding back the executed state for subsequent decision. According to the method, the reliability, the adaptability and the execution safety of migrating a simulation strategy to a real robot are improved, and closed-loop optimization and continuous self-adaption of a complex task are realized.

The invention discloses a building equipment fault rapid attribution and self-optimization method, which relates to the field of intelligent operation and maintenance of building equipment, establishes a multi-dimensional association relationship among equipment, building space, sensors and fault modes, and deeply combines a fault attribution engine with system dynamics. Data driving flexibility and physical logic preciseness are simultaneously realized in fault attribution, and reasoning from abnormal data to root cause analysis is realized. According to the method, the optimization efficiency is remarkably improved through a specific incremental learning / local updating mode, the weight of the knowledge graph and the Bayesian network conditional probability table are adjusted according to the maintenance result, physical equation parameters are calibrated, and dynamic updating of node attributes of the knowledge graph is achieved; through dynamic weight optimization, the adaptive ability of real-time reasoning confidence evaluation and scene context is improved; a physical equation is used as an executable knowledge unit to be embedded into a knowledge graph through establishment and deep integration of a system dynamics model, and a safety and reliability mechanism is designed for verification.

The invention relates to the technical field of application state diagnosis, in particular to an application state dynamic diagnosis and automatic repair method and system.The method comprises the steps that multi-source heterogeneous logs are collected and standardized in real time, key fields are extracted, context information is injected, and structured log data are generated; inputting the structured log data into a dynamic anomaly detection model, constructing a dual-path detection mechanism based on LSTM time sequence analysis and a graph neural network, identifying an abnormal mode and positioning a fault root cause; according to the output of the anomaly detection model, a repair action is triggered in a grading manner through an intelligent repair strategy engine; in the repairing process, system state changes are stored and recorded through a pre-writing type redundancy log, automatic rollback during abnormity is achieved on the basis of check point information, and data consistency and system stability are guaranteed. The problem of service interruption or data inconsistency possibly caused by traditional automatic repair is avoided, and the reliability of automatic operation and maintenance is improved.

The invention provides a code generation method and system based on a waterfall model and multi-agent collaboration, and the method comprises the steps: constructing a multi-agent collaboration framework which comprises a problem analysis agent, a solution agent, a pseudo-code agent, a coding agent and a restoration agent based on a software development waterfall model thought; and the interaction process of each agent is coordinated through a dynamic cooperation algorithm. Wherein the problem analysis intelligent body checks similar problems and solutions; the solution intelligent agent generates and evaluates candidate schemes; performing scheme conversion by the pseudo-code intelligent agent; the coding agent generates an executable code; and the repair agent performs fine-grained repair on the code in grammar, runtime and semantic levels through a two-dimensional repair mechanism. According to the method, the limitation of a single agent in a complex programming task is broken through, automatic code generation covering the whole process of demand analysis, scheme design, code implementation and test repair is realized, and the quality of generated codes and the reliability of operation are remarkably improved.

The invention discloses a streaming knowledge injection and adversarial self-optimization large language model training method and system, and the method comprises the steps: collecting the newest knowledge of an authoritative information source in real time, converting the newest knowledge into a structured constraint rule through a semantic analyzer, and dynamically updating a knowledge base; based on the updated knowledge base, adopting a PPO algorithm to optimize a generator, actively constructing a high-risk adversarial sample, and forcing the model to expose security vulnerabilities; after user input and adversarial samples are input into the model, total loss is calculated through constraint detection, gradient updating is blocked if the total loss exceeds a threshold value, and otherwise, a multi-level safety verification stage is started; and finally, fusing a security verification result and the adversarial loss, updating a total loss function and cooperatively adjusting model parameters to form a continuous self-optimization training cycle. According to the method, compliance is guaranteed through streaming knowledge injection, vulnerabilities are actively mined in combination with adversarial training, verification precision is improved by means of multi-level detection and dynamic threshold adjustment, and safety and reliability of a large language model in a complex scene are enhanced.

The embodiment of the invention provides a humanoid robot industrial task scene generation method and system based on a large language model, and belongs to the technical field of industrial automation and artificial intelligence. The method comprises the steps of obtaining an industrial task database; extracting and converting the task parameters according to an industrial task database to obtain first task prompt information; performing thinking chain task decomposition on the industrial task to obtain subtask sequence information; training the large language model to obtain an initial task execution strategy; performing multi-dimensional evaluation on the initial task execution strategy to obtain an evaluation result; optimizing the first task prompt information according to the evaluation result to obtain second task prompt information; and adjusting the large language model according to the second task prompt information to obtain a target task execution strategy. According to the embodiment of the invention, the task adaptability and strategy reliability of the humanoid robot in a complex industrial scene can be improved, and the flexibility and efficiency of industrial automatic production are remarkably improved.

The invention relates to the technical field of machine tool fault diagnosis and maintenance, and discloses a machine tool fault predictive maintenance method based on vibration analysis, which comprises the following steps: collecting vibration, temperature and acoustic emission signals and machine tool working condition parameters through a multi-modal sensor, extracting multi-domain features after preprocessing the vibration signals, and combining the working condition parameters through feature fusion and dimension reduction to obtain a machine tool fault predictive maintenance result. And establishing a fault classification model by using transfer learning, and performing hierarchical optimization. Model parameters are updated in real time based on an online learning mechanism, a fault early warning agent model is constructed to predict fault probability distribution, a dynamic threshold strategy is designed to avoid false report and missing report, and finally, related models and strategies are integrated to edge computing equipment. According to the method, multi-source data are integrated, multiple advanced algorithms are applied, machine tool faults can be accurately predicted, real-time monitoring and maintenance decision output are achieved, the machine tool operation reliability is improved, and the maintenance cost is reduced.

The invention discloses a method for processing an uncorrectable error of bus equipment and a server, relates to the technical field of servers, and aims to reduce the reporting frequency of the uncorrectable error by setting a suppression identifier of the uncorrectable error of the bus equipment and a time window of uncorrectable error statistics, and improve the reporting efficiency of the uncorrectable error when the uncorrectable error is triggered for the first time. According to the method, uncorrectable errors are reported, the uncorrectable errors are captured, it is ensured that initial errors are not missed, and when the error count of a time window is larger than a reporting threshold value, reporting is carried out again, error missing processing of error aggravation is avoided, redundant error reporting is reduced, an error reporting storm is inhibited through an inhibiting identifier, and the error reporting efficiency is improved. Therefore, the influence of frequent error triggering on the availability of a server system and normal operation of other services is reduced, the reliability of the server is improved, and the technical problems that frequent error triggering cannot be corrected, other service requirements are covered and the availability of the server system is influenced in the related technology are solved.

The invention discloses a large model and knowledge graph dual-drive-based guided reasoning system and method, belongs to the technical field of artificial intelligence reasoning, and solves the problem of how to improve the process reasoning ability of a large language model for engineering subject courses and the reliability of solving complex engineering problems. According to the method, metadata is extracted from teaching materials, structured problem representation is constructed, and a knowledge graph is constructed to form a complete knowledge system; the method comprises the following steps: decomposing a complex engineering problem into a structured solving plan with knowledge marks, and carrying out iterative loop based on a Monte Carlo tree search algorithm to generate a search tree comprising a plurality of high-quality candidate problem solving paths; then determining an optimal answer and a corresponding reasoning path in all simulated paths in a voting mode, performing confidence evaluation on each node of each path in the candidate path set, and further selecting a path of an optimal solution; and the process reasoning capability of the large language model on engineering subject courses and the reliability of solving complex engineering problems are effectively improved.

The invention relates to the technical field of production logistics scheduling management, and discloses a digital twin workshop production logistics real-time scheduling management method and system, and the method comprises the steps: 1, building a unified state data and standard parameter baseline, calculating a consistency deviation value, and setting a two-stage deviation judgment threshold; 2, receiving a field event, and determining a scheduling time window; 3, determining task priorities, and generating a production to-be-executed list; 4, generating a carrying task set according to the production to-be-executed list, and determining carrying equipment and task allocation under constraint; step 5, performing channel conflict-free path planning, and generating a space-time occupation table; step 6, performing cross consistency check, and executing issuing or local recalculation according to a threshold; and step 7, executing field operation and updating unified state data, and executing consistency recovery when the deviation exceeds the limit. According to the invention, real-time collaborative scheduling and high-reliability operation of workshop production and logistics tasks are realized.

The invention discloses a multi-condition simulation capacitance detection relay life prediction method, system, equipment and medium, and relates to the technical field of relays, and the method comprises the steps: applying a plurality of simulation conditions to a relay through a programmable driving power supply, simulating the complex conditions in actual operation, and collecting the multi-dimensional parameters of the relay in real time in the execution process of the simulation conditions, extracting characteristic parameters from the collected multi-dimensional parameters, carrying out data preprocessing on the characteristic parameters, constructing a life prediction model, carrying out prediction calculation and reliability grade evaluation on the residual life of the relay by utilizing the life prediction model, and carrying out reliability grade evaluation on the residual life of the relay by monitoring capacitance change between contacts of the relay. And early performance degradation early warning is carried out in combination with a prediction result. According to the invention, through fusion of multi-working-condition simulation, hybrid modeling and capacitance detection, full-chain optimization from data acquisition, accurate prediction to early warning is realized, and the accuracy, timeliness and engineering application value of relay life prediction are improved.

The invention provides a double-machine lifting dynamic balance control method based on real-time load feedback, which comprises the following steps of: after a system is initialized, synchronously acquiring load, height, attitude, position, motion and environment data of two machines, and fusing and filtering; load, height, angle, position and motion coordination multiple deviations are calculated, and balance and safety are evaluated; generating a speed, position, attitude and safety comprehensive control strategy by using multi-objective optimization according to an evaluation result, and setting a priority and a look-ahead mechanism; executing instructions in a distributed manner and monitoring the instructions in real time; self-adaptive parameter adjustment and machine learning continuous optimization are carried out based on a feedback closed loop; and multi-stage safety monitoring and emergency processing are synchronized. According to the invention, the precision, safety and efficiency of hoisting operation can be improved, the operation difficulty and energy consumption are reduced, and the adaptability and reliability of the system in a complex environment are enhanced.

System and methods are disclosed for providing unforgeable telemetry on computer platforms. Mathematical modeling and theorem proving are utilized to guarantee the integrity of telemetry probe execution flow and trigger, thereby preventing circumvention and tampering of logged probe data. In contrast to current state-of-the-art solutions that rely implicitly on the operating environment, this approach provides a sound and complete assurance of telemetry output. The system enables organizations to map unforgeable telemetry probe data to industry and government cybersecurity regulatory controls, ensuring compliance therewith. This invention addresses the shortcomings of existing solutions, including their vulnerability to sophisticated attacks, operational complexity, and inability to provide unforgeable telemetry data, thereby providing a reliable and accurate monitoring output in the presence of cyberattacks on computer platforms.

The computer fault diagnosis method based on the causal reasoning and mapping knowledge domain hybrid architecture comprises the steps of obtaining and processing multi-source heterogeneous data of an embedded computer, constructing a system mapping knowledge domain integrating the multi-source data of the embedded computer, and forming a knowledge base containing components, functions, fault phenomena and association relationships thereof; the method is characterized in that a causal reasoning engine is designed, domain knowledge constraints are utilized, a real cross-level fault propagation causal chain is mined from atlas association, and root causes are verified through anti-fact reasoning. And the engine dynamically feeds back the mined causal knowledge to the atlas, so that the causal knowledge is continuously optimized. According to the hybrid architecture, accurate and rapid tracing with causal explanation from a fault phenomenon to a root cause is realized, and the diagnosis capability in high-reliability fields such as aerospace and industrial control is remarkably improved.

The invention relates to the technical field of equipment management, in particular to an equipment full-life-cycle digital twinborn decision-making system enabling a large model. Comprising a digital twin modeling unit; a large model enabling analysis unit, wherein the large model enabling analysis unit adopts a hydroelectric equipment multi-modal causal constraint analysis model; a whole-process closed-loop management and control unit; and an intelligent decision output unit. According to the method, the multi-modal causal constraint analysis model adaptive to the working condition of the hydroelectric equipment is constructed, and a causal chain verification backtracking mechanism is introduced, so that the problem that the reasoning result lacks logic verification is effectively solved, the logic consistency of a fault reasoning conclusion is guaranteed, and the reliability of decision output is improved; through a scene adaptation mode of'pre-training + fine tuning 'of a large model, multi-modal feature fusion processing and deep linkage of a workflow engine and a digital twinborn body, full-life-cycle management requirements of equipment are fully covered, and the refinement and intelligence level of hydroelectric equipment management is further improved.

The invention provides a storage type time-depth logging method, and relates to the technical field of logging control, and the method comprises the steps: obtaining an encoder, tension and acceleration data, and drawing an encoding curve, a tension curve and an acceleration curve. And receiving an effective movement time interval of single complete drilling rod operation intercepted by the user according to the curve characteristics. And for the original coding curve section of each interval, the total variation and the total duration of the coding value are kept unchanged, and the coding value and the time point are matched again according to the layered scale relationship between the encoder data and the actual displacement of the hook, so that a corrected target coding curve is obtained. And all the target coding curves are converted into time-depth curves according to the depth interval corresponding to each drill rod, and a time-depth file is generated through splicing according to the operation sequence of the drill rod operation event interval. According to the scheme, the technical problem of how to improve the precision of storage type time-depth logging depth measurement and the reliability of the whole system is solved.

The invention discloses a dynamic closed-loop management method for bridge-shaped contact performance, and relates to the technical field of power system protection. The method comprises the following steps: S1, a dynamic sensing process: acquiring multi-source dynamic parameters of a bridge-shaped contact in a non-intrusive mode; s2, a quantitative evaluation process: fusing the multi-source dynamic parameters and the static parameters, and calculating a health index or a health level of the contact through a preset model; s3, adaptively optimizing the process, dynamically adjusting the closing energy or protection parameters of the circuit breaker according to the health state, and compensating the performance change of the contact; and S4, a predictive maintenance process: performing trend extrapolation based on the historical data of the health index, predicting the residual life and generating a maintenance suggestion. The processes are sequentially connected and cyclically iterated to form dynamic closed-loop management, so that the problems of one-sided parameter sensing, inaccurate evaluation, fixed parameters and maintenance lag in the prior art are solved, the operation reliability of the bridge-shaped contact is improved, the service life of equipment is prolonged, and the operation and maintenance cost is reduced.