121 results about "System Fault Tolerance" patented technology

The invention discloses an intelligent scheduling method for a comprehensive virtual power plant, and relates to the technical field of power plant intelligent scheduling, and the method comprises the steps: carrying out the redundancy elimination filtering and unified time sequence calibration of multi-node sensing data of a cold and hot energy station, energy storage equipment and the like through the deployment of an edge side high-concurrency collection module, generating a high-quality time sequence fusion sequence, and screening schedulable resources; on-line model correction is realized through an error dynamic evaluation mechanism, a multi-stage scheduling strategy is generated by adopting an improved particle swarm algorithm aiming at the targets of economy, reliability and environmental protection, and an optimal instruction is output in combination with an equipment constraint condition. Through priority control and real-time monitoring index evaluation deviation, a rolling correction and standby strategy is triggered, equipment-level fault risk quantification and self-healing control are realized, abnormity is rapidly isolated, and redundant resources are started. Through data-prediction-scheduling-execution closed-loop cooperation, the resource utilization rate, the scheduling precision and the system fault-tolerant capability are significantly improved, and the scheduling capability of source heterogeneous resources is enhanced.

The invention discloses a cooperative autonomous synchronous networking method for a multi-networking type energy storage converter without communication interconnection. The method comprises the following steps: S1, state perception and estimation based on local measurement: acquiring local electrical parameters in real time by using a sensor, and performing state estimation through an improved Kalman filtering algorithm and a local dynamic model; s2, a cooperation mechanism of the virtual synchronous machine and droop control: combining the virtual synchronous machine with the droop control mechanism, and realizing automatic power adjustment and cooperation work by setting a virtual inertia, a damping coefficient and a droop coefficient; s3, self-adaptive synchronization algorithm: based on the local state estimation and the output of the virtual synchronous machine, adjusting the phase and frequency of the output voltage, and dynamically adjusting synchronization parameters according to the state of the power grid; and S4, detecting a fault by monitoring local electrical parameters and a self state in real time, and automatically adjusting a control strategy and realizing system fault tolerance when the fault occurs.

The invention relates to the technical field of mine safety monitoring, and discloses a mine high-temperature heat damage monitoring system based on multi-mode Internet of Things data, and the system comprises a prediction and control integrated system architecture module which constructs a prediction and control integrated system architecture, and builds a three-layer PCIS architecture of a perception layer, a prediction layer and a control layer; the thermal field and flow field combined optimizer module is used for constructing a thermal field and flow field combined optimizer and realizing direct coupling of a prediction model and a control algorithm; the self-adaptive anti-interference control mechanism module is used for implementing a self-adaptive anti-interference control mechanism and realizing system fault tolerance through a fault mode library and dynamic reconstruction; the performance self-optimization module is used for implementing system performance self-optimization and continuously improving the system performance through incremental learning and knowledge migration; according to the method, through multi-modal data fusion and the space-time diagram convolutional network, the heat damage prediction precision is improved, the prediction time range is expanded, and more sufficient response time is provided for heat damage prevention and control.

The invention provides a complex task multi-agent collaborative disassembling method and system based on a business process, and relates to the technical field of artificial intelligence, and the method comprises the steps: constructing a graph through analyzing a business process document, matching a complex task with a process node, identifying a closed loop dependence path, and selecting an agent based on a constraint logic formula. And establishing a causal tracing chain to process abnormal conditions, and performing semantic alignment to ensure data transmission between subtasks. According to the method, the automation degree and the execution efficiency of multi-agent cooperative processing of complex tasks are improved, and the fault-tolerant capability of the system and the reliability of task completion are enhanced.

The invention discloses a fault fallback method and device in a multi-agent cooperative system, equipment and a medium, and the method comprises the steps: recognizing a plurality of target agents used for jointly executing a target transaction in the multi-agent cooperative system according to a target transaction operation request; the target agent is used for executing one atomic operation in the target transaction; after a transaction submission instruction is sent to each target agent which completes preparation in the preparation stage, whether an execution fault occurs in the execution process of the target transaction is monitored in real time in the submission stage; when the target execution fault is detected, at least one fault agent generating the target execution fault is positioned in the target agents; according to the fault agent, the local or complete rollback operation of the target transaction in the submission stage is executed, and according to the technical scheme of the embodiment of the invention, by monitoring multi-agent transaction execution in real time, accurately positioning the fault node and supporting the local or global rollback operation, the fault-tolerant capability of the system is remarkably improved, and the transaction consistency is also ensured.

The present invention discloses an e-commerce management method and system based on big data analysis, which relates to the field of data management technology. The present invention realizes the stable operation and efficient data transmission of the e-commerce platform in a complex network environment by introducing big data analysis and dynamic slicing scheduling mechanism. With the help of real-time evaluation and classification processing of the network status of user terminals, the present invention can intelligently adapt to different types of e-commerce business needs, thereby realizing personalized transmission strategies, and effectively alleviates the network congestion problem during the promotion period through time-series differential encoding and interpolation compensation of high-priority data such as flash sales, thereby improving the response speed and user experience of key businesses. Combined with dynamic delay floating analysis and packet loss rate evolution judgment, the present invention can adaptively adjust the data sending rhythm, ensure the smooth scheduling of slice data when bandwidth fluctuates, and significantly improve business continuity and system fault tolerance.

The invention discloses a weak current intelligent building control management system, which comprises a plurality of groups of different types of sensors, a data collection module, a data storage module, a data comparison and elimination module, an emergency management module and a control center. A plurality of groups of different types of sensors are distributed in each key area of a building, all-directional environment monitoring is ensured, preprocessing steps such as Kalman filtering and mean value filling are introduced to improve data quality, data storage adopts a block chain technology based on Hyperledger Fabric, data cannot be tampered and high security is realized, and the method is suitable for large-scale popularization and application. And the data comparison and elimination module verifies the sensor data in multiple dimensions by using transverse comparison, longitudinal comparison, logic verification and redundancy cross validation, so that the accuracy and consistency are improved, and the fault-tolerant and anti-interference capabilities of the system are enhanced.

The invention provides an automatic fault-tolerant propulsion method and system for task execution abnormity and a storage medium, and relates to the technical field of distributed system fault-tolerant control, and the method comprises the steps: task dynamic arrangement: arranging tasks into a serial, parallel or dependent execution mode according to a preset dynamic rule, and realizing task trigger execution through an asynchronous thread pool; performing process monitoring, adding a monitoring annotation to an execution method of each task by using an AOP tangent, and capturing a task execution state in real time; performing abnormal grading processing, and executing a strategy according to a pre-configured three-level processing channel; spreading a dependency state, and automatically updating an associated task state based on a dependency relationship among the tasks: when an upstream task fails, pausing a downstream dependency task and marking the downstream dependency task as a waiting state, and continuing to execute a non-dependency task; and performing global state statistics, calculating a global state in real time through a task state algorithm, if all tasks are completed, marking the tasks as completed, if abnormal tasks exist, marking the tasks as abnormal, and triggering root cause analysis.

The invention discloses a wind power plant data optimization storage management system and method, and relates to the technical field of data storage management. Comprising a data acquisition module used for acquiring and storing operation data of a wind power plant to a storage node, performing copy number evaluation on the operation data, and distributing the copy number according to a copy number evaluation result; the storage evaluation module is used for acquiring storage node information, performing storage space evaluation on the storage node information, performing migration evaluation on the copies according to a storage space evaluation result, and determining the copies required to be migrated according to a migration evaluation result; and the copy optimization module is used for carrying out copy placement evaluation on the storage node information. According to the method, the problems of storage space waste and insufficient system fault-tolerant capability caused by a fixed number of copies in a traditional wind power plant data storage management system are solved, the utilization efficiency of storage resources is improved, the fault-tolerant capability of the system is enhanced, and the storage cost is reduced.

The invention relates to the technical field of industrial robot control, and discloses a single-connecting-rod mechanical arm system fault-tolerant asynchronous control method based on double-variable decision, which comprises the following steps: establishing a semi-Markov jump system dynamic model for describing the operating characteristics of a single-connecting-rod mechanical arm; based on the dynamic model, designing a fault-tolerant asynchronous controller structure based on a double-variable decision and an experience-machine cooperative operation strategy; establishing sufficient conditions which enable the closed-loop system to be randomly stable and meet H infinity performance indexes; and the numerical value of the gain of the machine controller is obtained by solving the linear matrix inequality set, and a final control law is generated to conduct fault-tolerant asynchronous control on the single-connecting-rod mechanical arm. According to the fault-tolerant asynchronous control method for the mechanical arm, fault-tolerant asynchronous control of the mechanical arm is achieved through a double-variable decision and an experience-machine cooperation strategy in combination with modal transfer rate boundary processing, and the random stability and the H-infinity performance of the system are guaranteed under the modal mismatching and fault working conditions.

The invention relates to a remote firmware updating optimization method for an electric bicycle charging pile, relates to the technical field of remote firmware updating of Internet of Things equipment, and solves the problem of transmission reliability in a weak network environment by optimizing a data block size and a transmission protocol according to real-time network quality through a dynamic block transmission strategy and a protocol adaptive switching mechanism. Constructing an end-to-end security protection system by adopting a multi-level security verification mechanism and combining frame verification, file hash and digital signature hierarchical verification; a hardware compatibility precise matching algorithm is designed, and based on structured version number interval comparison and a double-partition firmware updating mechanism, the adaptability of a firmware version and hardware is ensured; and establishing an exception monitoring and closed-loop recovery system, and realizing full-process self-healing after updating failure through an error classification model, a recovery strategy routing table and intelligent state rollback. The method improves the transmission success rate of firmware updating, the safety protection level and the system fault-tolerant capability, and is suitable for a remote operation and maintenance scene of a large-scale charging pile group.

The invention discloses an engineering system fault-tolerant tracking control method based on data driving, and belongs to the technical field of engineering control methods. Comprising the following steps: S1, establishing an engineering system equivalence model under a fault, and defining a performance index function and a corresponding value function so as to convert an optimal control problem into a zero-sum game problem; s2, aiming at the problem that the state of the engineering system is unmeasurable, reconstructing the state by utilizing historical input and output data so as to design and solve an optimal control strategy; and S3, introducing and defining a Q function, designing a controller gain and a disturbance gain # imgabs0 #, and optimizing the # imgabs1 # in combination with a designed model-free non-strategy Q-learning output feedback algorithm to obtain an optimal controller. Through an injection molding industrial process and a depth control simulation experiment of an autonomous underwater vehicle (AUV), convergence of the algorithm and an excellent tracking control effect of a designed controller are verified.

The present invention discloses an intelligent scheduling method for a comprehensive virtual power plant, which relates to the technical field of intelligent scheduling of power plants. By deploying a high-concurrency acquisition module on the edge side, redundant filtering and unified timing calibration are performed on multi-node sensor data such as cold and hot energy stations and energy storage equipment to generate high-quality timing fusion sequences and screen schedulable resources. An error dynamic evaluation mechanism is used to achieve online model correction. Aiming at economic, reliability, and environmental protection goals, an improved particle swarm algorithm is used to generate a multi-stage scheduling strategy, and the optimal instructions are output in combination with equipment constraints. Deviations are evaluated through priority control and real-time monitoring indicators to trigger rolling corrections and backup strategies, achieve equipment-level fault risk quantification and self-healing control, quickly isolate anomalies, and activate redundant resources. Through data-prediction-scheduling-execution closed-loop collaboration, resource utilization, scheduling accuracy, and system fault tolerance are significantly improved, and the scheduling capability of heterogeneous resources is enhanced.

The invention discloses a group scheduling control method and system for a PCS energy storage system, relates to the technical field of group scheduling of energy storage systems, and solves the technical problems of insufficient SOC balance regulation and control accuracy and weak capacity proportion distribution fault-tolerant capability. The SOH correction coefficient is accurately calculated through the linear relation, it is ensured that the PCS with the high health degree and the large capacity preferentially undertakes the balancing task, the weak health battery is effectively protected, the cycle life of the battery is prolonged, multiple screening constraints of the available PCS are defined, and according to the size relation between the exceeding power and the total redundant power, a capacity proportion or redundant power proportion distribution strategy is adopted, so that the capacity of the PCS is effectively protected. Reasonable power distribution and no equipment overload risk are ensured, the power distribution deviation after the fault of a single PCS can be reduced, and the fault-tolerant response speed of the system is improved.

The invention discloses a large-stroke aircraft airfoil static force loading system for aircraft strength testing, and particularly relates to the technical field of aviation structure testing, and the large-stroke aircraft airfoil static force loading system comprises a multi-region loading execution module for realizing distributed autonomous loading and state sensing; the boundary coordination control module realizes force field continuity between loading areas through flexible fusion; the asynchronous self-synchronization adjustment module adjusts a loading step pitch based on local feedback and a loading offset prediction function; the redundant closed-loop feedback control module adopts a force value and displacement dual-channel path; the loading path modeling and reproduction module realizes historical path playback and loading track reproduction by constructing a track data set; according to the invention, continuous coordination of multi-region loading force fields is realized, the adaptive adjustment capability of loading step pitch and frequency is improved, and the loading behavior is dynamically optimized through a path difference feedback mechanism; a redundancy feedback and path reproduction mechanism is configured, and the fault tolerance and loading consistency of the system are enhanced; and intelligent scheduling is realized based on the shared table, so that the loading precision, stability and efficiency are remarkably improved.

The invention provides a triple redundant data synchronization method and system based on dynamic priority weighting. The method comprises the following steps: collecting multi-source data; calculating a health degree index of each processor module according to the multi-source data; calculating the dynamic weight of each processor module according to the health degree index of each processor module, and normalizing the dynamic weight; calculating the weighted support degree of the input value according to the input value of the processor module and the normalized dynamic weight; according to the weighted support degree, voting and selecting an input value as a voting output of a three-system processor module; and if the normalized dynamic weight of a certain processor module is continuously lower than a predefined threshold value for a preset number of cycles, automatically isolating the processor module. According to the scheme provided by the invention, the voting weight can be dynamically adjusted by evaluating the health degree of the module in real time, and the system fault-tolerant capability and the resource utilization rate are improved by combining weighted voting and a fault pre-isolation mechanism.

The invention provides a path optimization method for cleaning an unmanned aerial vehicle swarm and application. The method comprises the steps that position initialization is conducted on multiple unmanned aerial vehicles in a task area, a fitness function is set, and a variable Trials is recorded; the stages of employed bees, observation bees and reconnaissance bees are executed in sequence through an artificial bee colony algorithm, and path searching, probability selection, boundary processing and position updating are carried out; a reverse learning strategy, disturbance control frequency and a global factor are introduced into track initialization to improve search performance, and meanwhile, a nectar source regeneration mechanism in a scout bee stage is set. Through dynamic task allocation optimization, global collaborative path planning and a distributed elastic control architecture, the cleaning resource utilization rate, the operation efficiency and the energy consumption balance are improved, the fault-tolerant capability of the system is enhanced, and the cleaning requirement of the complex building facade is met.

The invention relates to the field of chemiluminescence immunity analyzers, and discloses a high-speed full-automatic chemiluminescence immunity analyzer multi-module parallel motion control method, which comprises the following steps: in a current execution period, pre-calculating all secondary function module action instruction sets of a next period based on real-time position information of a reaction cup and test sample parameters; according to a pre-generated instruction set, coordinating parallel actions of multiple modules through a preposition and subsequent dependency relationship, and driving a hardware execution unit to complete sample processing; and synchronizing the reaction cup position data and the test step state in real time, and switching to a next period instruction set when the period is ended. According to the method, the problems of time sequence conflict and resource contention of multi-module cooperative control are solved, the parallel efficiency of the detection process and the fault-tolerant capability of the system are improved, and the method is suitable for a high-throughput chemiluminescence immunoassay scene.

The invention discloses a service application unified data exchange mechanism and dynamic scheduling fault tolerance method and system, and relates to the technical field of service scheduling. The service application unified data exchange mechanism and dynamic scheduling fault tolerance method comprises a starting service scheduling module, a task management module, a configuration center module, a communication management module, a state monitoring module and an exception management module. The task management module receives the task configuration file, analyzes a service dependency relationship in the task configuration file, generates an execution node of the task, and promotes execution of the task in a data stream mode; and the task management module distributes a binary executable file required by the task according to the analyzed task configuration. According to the method, the problems that a current task scheduling system generally lacks a real-time monitoring and response mechanism for a service state, and service abnormity is difficult to process in time, so that the system stability is reduced; and in addition, the system fault-tolerant capability is insufficient, a rapid migration and recovery mechanism is lacked when a service fault occurs, and the system availability is low.

The invention provides a multi-scene composite scheduling method and system for heterogeneous unmanned aerial vehicles, and the method comprises the steps: collecting the flight demand data and flight resource data of a heterogeneous unmanned aerial vehicle cluster in real time, and constructing an unmanned aerial vehicle global resource pool; calculating a matching degree between each flight demand and each flight resource in the unmanned aerial vehicle global resource pool; and constructing a multi-target composite optimization model based on the matching degree, and solving the multi-target composite optimization model to obtain an optimal scheduling result of the unmanned aerial vehicle. According to the invention, for the demands of multi-task concurrence, dynamic environment change and resource collaborative allocation in logistics distribution, forest and equipment inspection, emergency disaster rescue, agricultural plant protection and wide-area monitoring scenes, the matching degree of a flight task and an idle unmanned aerial vehicle is calculated in a multi-dimensional manner, and the tasks are dynamically allocated; the method is suitable for efficient matching of heterogeneous computing resources and diversified tasks of a large-scale unmanned aerial vehicle cluster so as to realize collaborative improvement of global scheduling efficiency and system fault tolerance.

The invention discloses an employee assessment management method and system based on a cloud platform, and particularly relates to the technical field of human resource management.The method comprises the steps that attendance coordinate data, customer evaluation star-level scores and sales order completion quantity are collected in real time through a real-name account system, and a multi-source work data set is generated through abnormal value filtering and sliding window mean value processing; dynamically adjusting the ratio of the customer evaluation weight to the attendance weight, and constructing a five-level achievement step based on extreme value difference equal-ratio division; extracting historical threshold data according to the number of times of achievement unlocking to generate a calibration evaluation benchmark, and verifying and triggering a cross-end game reward instruction in combination with a double-end timestamp difference value; and aligning a re-judgment achievement condition through a timestamp, and sending a virtual asset increment update packet to realize cross-end state synchronization. According to the method, the dynamic adaptive capacity of an evaluation system to service fluctuation is improved, the association accuracy of achievement excitation and work performance is enhanced, the problem of misjudgment caused by asynchronous transmission of data is solved, and the objectivity of an evaluation result and the fault tolerance of the system are ensured.

The invention discloses a multi-modal stability identification and evaluation method and system for a transformer substation monitoring system. The method comprises the following steps: evaluating static model vulnerabilities in a system configuration file based on the transformer substation monitoring system; dynamically linking a transformer substation monitoring system through an application system, performing application function test on the monitoring system by utilizing a dynamic linkage test case, and performing dynamic vulnerability evaluation on the model; and monitoring system application function module faults through verification function logic, memory leakage, alarm coverage rate and system fault tolerance verification, and carrying out positioning inversion. Technical difficulties existing in application scenes such as equipment research and development, network access detection, transformer substation design, factory joint debugging, field debugging, operation and maintenance and the like of an intelligent transformer substation monitoring system can be taken as entry points, and model vulnerability detection, data flow section detection and function and performance defect testing can be carried out; and a defect panoramic evaluation means suitable for a novel power system intelligent substation monitoring system is formed.

This invention provides a multi-agent cooperative underwater logistics system and method based on reinforcement learning, applicable to the fields of underwater robot cooperative control and intelligent logistics technology. The system integrates six modules: task management, multimodal communication, cooperative localization, path planning, energy management, and fault handling. Through a dynamic switching mechanism in multimodal communication, it adaptively selects between acoustic, optical, and magnetic induction modes to ensure communication reliability in underwater environments. A distributed cooperative localization method based on dynamic weights is employed, optimizing weight allocation by considering communication quality, battery power, and computing power to achieve cooperative localization. A multi-agent reinforcement learning controller with a multi-branch attention mechanism is introduced to achieve real-time obstacle avoidance, formation maintenance, and task-oriented cooperative path planning. An intelligent charging strategy based on multi-level battery thresholds and task urgency supports continuous system operation. A sliding window fault detection and adaptive task reassignment mechanism enhance the system's fault tolerance and robustness.

The application provides a SINS and GNSS combined navigation system fault-tolerant method based on an LSTM neural network, and a set of fault detection and system fault-tolerant method based on an LSTM neural network auxiliary is provided for sudden faults and slowly changing faults in a SINS / GNSS loosely coupled navigation system. A new information Mahalanobis distance auxiliary chi-square test algorithm is used to detect the sudden faults existing in the system in real time, and a sequential probability ratio algorithm is used to detect the slowly changing faults existing in the system. Robust Kalman filtering based on M estimation is used to complete the robustness enhancement of the system, thereby effectively inhibiting the influence of sudden faults on the system. The LSTM neural network is used in the form of replacement to effectively inhibit the influence of slowly changing faults on the system, and signal repair is realized. Through detection and fault tolerance of the two kinds of faults existing in the GNSS signal, the precision of the combined navigation positioning is improved.

The invention discloses a driving circuit with IGBT (Insulated Gate Bipolar Translator) fault diagnosis and protection, which can realize synchronous and independent control driving according to driving requirements and module faults through a switchable driving circuit. The fault detection circuit detects the operation state information of the power module to judge whether the module has a fault and judge the fault type at the same time, and executes a protection strategy. When a plurality of IGBTs connected in parallel work at the same time, a plurality of power modules are driven to work at the same time through the push-pull amplification circuit post-stage. When a certain module breaks down but still needs small current output in a short time, single power module output can be switched, meanwhile, the three-phase driving mode is switched to single module output, and output consistency is guaranteed. The method solves the problems that a parallel IGBT system is poor in fault tolerance, high in fault misjudgment rate, insufficient in long-term reliability and the like, is suitable for the fields with high reliability requirements, such as new energy automobiles, aerospace power supplies and the like, and has industrialization popularization potential.

Disclosed are a method for initializing a controller area network interface, an electronic device, and a robot. The method for initializing a controller area network interface provided by the present disclosure is suitable for interface initialization in the CAN bus communication scenario of an industrial robot. By adopting a multi-source hierarchical configuration analysis mechanism, the priority of configuration sources such as environment variables and command line parameters is determined, CAN interface zero-configuration deployment is achieved, and the configuration flexibility and environment adaptation capability are greatly improved. By introducing a state-aware redundant configuration skipping mechanism, the current state of the interface is queried during initialization, unnecessary repeated operations are skipped, hardware loss and initialization delay are reduced, and system startup efficiency is improved. The method has a graceful degradation strategy, and in a multi-interface scenario, the failure of partial interface initialization does not block the overall service startup, thereby improving the system fault tolerance and operation stability. Meanwhile, by adopting an initialization strategy with fixed interval retries, the method avoids the endless loop caused by the failure of initialization due to temporary faults.

The present invention discloses a fault-tolerant iterative learning control method for a robotic arm system based on a balance factor. Based on the derivative relationship between the parameters of the robotic arm system, the system is reconstructed into a second-order system, a backstepping error and a virtual controller are constructed. At the same time, to avoid the imperfect reset of the system state at the beginning of each iteration, a parameter update law based on the balance factor is constructed, and an iterative learning controller is designed to control the robotic arm system and compensate for actuator faults, parameter uncertainties and disturbances. Compared with traditional control methods that require an accurate mathematical model, the control algorithm proposed in the present invention is more novel and has simple application conditions. When this method is used for repetitive task operations, it effectively improves the control accuracy and has good engineering application value.

The invention relates to a dual three-phase permanent magnet motor fault-tolerant prediction control method based on virtual vectors and common-mode rejection, and belongs to the technical field of permanent magnet synchronous motor control. In order to solve the problems of fundamental frequency offset and high harmonic content of common-mode voltage after a T-type three-level dual three-phase electric drive system has a single-phase open-circuit fault, a symmetric prediction current model is established by constructing a reduced-order decoupling matrix, a zero-harmonic voltage virtual vector set is generated, and the vector set is alternately input in combination with real-time common-mode voltage offset detection, so that the zero-harmonic voltage is obtained. And a weight-free series-parallel multi-objective optimization cost function is adopted to carry out vector preferential optimization. According to the method, the abc phase common-mode voltage is reduced to Udc / 6 in a high-speed interval, the full-speed domain of the def phase is lower than Udc / 8, the current distortion rate is smaller than 10%, meanwhile, neutral-point potential balance and low switching frequency are considered, and the fault-tolerant capability and the operation efficiency of the system are remarkably improved.

The invention relates to a system fault-tolerant degree estimation method considering professional performance, belongs to the technical field of fault-tolerant analysis, and solves the problem that the actual fault-tolerant capability of a system under different degradation levels is difficult to accurately evaluate due to the lack of comprehensive consideration on the degradation degree of professional performance of the system in the prior art. The system fault tolerance estimation method comprises the following specific steps: step 1, constructing a professional performance margin model of a system according to a subject crossover equation, a degradation equation and a margin equation; step 2, acquiring first wearing time according to professional performance allowance of the system; step 3, obtaining professional performance fault tolerance of the system based on the reliable degradation time range of the system determined during first wearing; and 4, carrying out approximate solution on the professional performance fault tolerance of the system to obtain the professional performance fault tolerance concerning the degradation time under the discrete condition.

The invention relates to a system fault tolerance estimation method based on a system component structure, belongs to the technical field of fault tolerance analysis, and solves the problems that in the prior art, a systematic evaluation method for fault tolerance is lacked, and an evaluation result is inaccurate. The system fault tolerance estimation method comprises the following specific steps: step 1, constructing a component structure-oriented system directed graph; and 2, based on the component structure-oriented system directed graph, the fault tolerance of the system structure is obtained, the system fault tolerance estimation method quantifies the influence of component errors on the fault tolerance of the system from the perspective of functional logic, and the fault tolerance of the system can be evaluated more accurately.