1326 results about "Fault tolerance" patented technology

The invention relates to the field of distributed machine learning, provides a big data-oriented distributed model training optimization method, and solves the problems of load imbalance, low resource utilization rate, large communication overhead, insufficient fault-tolerant efficiency and the like caused by data fragmentation staticization in the prior art. Load balancing is realized through intelligent clustering and overlapping control; the multi-dimensional heterogeneous resource evaluation model monitors calculation / storage / network indexes in real time, and realizes adaptive scheduling in combination with a task prediction and optimization algorithm; the hierarchical gradient synchronization mechanism adopts a tree-shaped parameter server and a dynamic compression technology, so that the communication traffic is reduced by 50%, and the precision loss is less than 0.8%; the incremental checkpoint system uses erasure code coding and parallel recovery to shorten the fault recovery time from 15 minutes to within 2 minutes, the resource utilization rate reaches 85% or above, the convergence speed is improved by 30%-40% in ResNet, BERT and other model training, and the large-scale training efficiency and the system stability are remarkably optimized.

The invention relates to the technical field of data processing, and particularly provides an operation intention recognition method, system and device based on multi-mode fusion and a medium, and the method comprises the steps: synchronously collecting interaction data of at least two modes of a user, the modes comprising at least two of gestures, voice and eye gaze; carrying out alignment processing on the interaction data, wherein the alignment processing comprises time synchronization and space mapping to a unified coordinate system; recognizing structured semantic information from each piece of aligned modal data, wherein the structured semantic information comprises a gesture type, a voice text and a fixation point coordinate; and based on a preset semantic rule and context memory, performing semantic association and anaphora resolution on the structured semantic information to obtain an operation intention. The method effectively overcomes the inherent defects of unnatural single-mode interaction, easy ambiguity and poor fault tolerance.

The invention discloses a goaf unmanned aerial vehicle inspection system based on monitoring and early warning, and the system comprises an observation data collection module which is used for collecting settlement and fracture main variables to form a time stamp observation set; the data fusion module is used for generating a unified monitoring data set by adopting a fractal small-world network coding consensus algorithm; the initial route planning module is used for constructing a route sequence based on a main variable evolution tensor and a risk map; the flight path execution module is used for collecting a flight path and environment data; the fault-tolerant control module triggers a fractional order sliding mode fault-tolerant and degradation mechanism based on the consistency error; and the closed-loop optimization module is used for dynamically updating the main variable tensor and the path cost function and outputting an optimized track and a structured early warning result. The system has high dynamic responsiveness and multi-source risk adaptability.

The invention relates to the technical field of computing power scheduling, provides a distributed computing power scheduling method and system, breaks through the limitation that a traditional computing power scheduling system depends on a single index or a static strategy by constructing an integrated architecture of data perception, load prediction and intelligent scheduling, and constructs a self-adaptive and closed-loop optimized intelligent scheduling system. The scheduling system obtains multi-source data in real time and fuses the multi-source data into a perception vector to accurately describe the running state of the system, accurate pre-judgment of load changes is achieved through hierarchical load prediction, and resources can be efficiently allocated in different scenes in combination with double scheduling paths and execution feedback optimization; according to the scheduling system, the resource utilization rate of the distributed system is effectively improved, the energy consumption cost is reduced, the task execution delay is reduced, the system stability and the fault-tolerant capability are enhanced, and an innovative computing power scheduling solution is provided for a large-scale distributed computing scene.

The invention discloses a power grid equipment state sensing driving dynamic response method based on the Internet of Things technology, and relates to the technical field of power system automation and informatization, and the method comprises the following steps: S001, collecting original multi-dimensional state signals of a plurality of sensors deployed in a power grid equipment state sensing channel, constructing an electromagnetic disturbance recognition model, and carrying out the recognition of the original multi-dimensional state signals; frequency domain and time domain feature extraction is carried out on the signals, and a feature comparison parameter set used for distinguishing electromagnetic interference and real faults is generated. Frequency domain and time domain features are extracted through an electromagnetic disturbance recognition model, dynamic threshold judgment and response strategy adjustment are achieved in combination with multi-source sensing data, interference and faults can be accurately distinguished, early warning and protection logic can be corrected in real time, protection actions can be accurately triggered, closed-loop control is achieved, the delayed fault tolerance and multi-source verification capacity is achieved, and the method is suitable for large-scale popularization and application. The false operation rate and the false stop risk are effectively reduced, and the intelligence, the safety and the stability of power grid operation are improved.

The invention relates to the technical field of artificial intelligence, in particular to a cloud edge collaborative production manufacturing management system based on artificial intelligence, which comprises a task scheduling optimization module, a resource scheduling and load balancing module, a production efficiency evaluation module, a dynamic load adjustment module and a fault tracing analysis module. According to the invention, by accurately analyzing the task dependency relationship, optimizing the task scheduling strategy and improving the response sequence and delay control of task execution, the processing bottleneck problem caused by data transmission delay is avoided, the task execution period is flexibly adjusted, the high-frequency tasks in the production process are effectively managed, and the load unevenness caused by fluctuation is reduced; the method can accurately identify and regulate the production bottleneck, improve the adaptive capability of the production process, accurately identify the potential fault source, shorten the fault diagnosis time, reduce the complexity, more rapidly position the problem, improve the stability, efficiency and fault tolerance of the production system, and integrally enhance the self-optimization capability of the production system.

The invention provides an agent collaborative question-answering method and system based on context awareness and authority control, and belongs to the technical field of artificial intelligence. The method comprises the following steps: acquiring user query, and triggering a user context manager to generate a session state user context object containing a permission label and preference; the central scheduler performs task decomposition based on the query intention and the context, dynamically selects an intelligent agent through a multi-dimensional scoring model and binds a knowledge source; the authority-aware agent execution engine executes triple authority verification and intermediate result desensitization processing in the whole process, and supports agent fault tolerance and audit log recording. The method realizes unification of knowledge security and access flexibility, supports multi-scene adaptive question and answer, and has good expandability and fault-tolerant capability.

The invention belongs to the field of high-performance computing, and relates to an integer parallel computing method and device based on distributed storage and computer equipment, and the method comprises the steps of collecting real-time resource indexes, dynamically identifying fault nodes, triggering task migration, and performing data verification and hard disk fault detection. The weight value of each node is calculated, the nodes are arranged according to the descending order of the weight values, and the nodes with high load capacity are selected to distribute tasks; dynamically distributing a data generation task to a computing node, executing parallel computing, and performing distributed storage on a result; obtaining an operand, converting the operand into a first-order tensor form of a basic operand, serializing tensor data, and sending the serialized tensor data to a parallel computing layer; distributing a search task to a computing node, retrieving storage data in parallel, reading effective data from a storage layer, and combining search results into a partial sum; and summarizing and then outputting. The system has dynamic resource management and fault-tolerant capabilities, and can realize efficient task allocation and load balancing.

The invention relates to the technical field of intelligent control, in particular to a multi-mode-based intelligent home control method and system, and the method comprises the following steps: S1, synchronously collecting a voice stream and an action video stream of a user, and respectively extracting a semantic anchor point of a voice instruction and a spatial anchor point of an action track; s2, constructing a virtual time axis, mapping the semantic anchor points and the spatial anchor points to a unified space-time coordinate system, and performing anchor point deviation correction on a mapping result based on an environment semantic field to generate an alignment instruction set; and S3, performing intention fusion on the alignment instruction set according to the spatial topological relation of the equipment, generating an equipment control instruction, and triggering execution. According to the method, the fault tolerance and execution rationality of fuzzy instructions or wrong finger behaviors are improved, the method is particularly suitable for family scenes with dense multiple devices and frequent environment dynamic changes, and the practicability and the intelligent level of the system are remarkably improved.

The invention relates to an unmanned system cluster distributed task cooperative scheduling method and system. The system comprises a ground station general control scheduling module, an unmanned aerial vehicle cluster and a distributed monitoring and scheduling agent module. The method comprises the following steps: calculating the static priority of each task in a DAG task graph; tasks with high priorities are scheduled firstly; sequentially selecting a node with the minimum power consumption perception completion time EA-EFT from all the unmanned aerial vehicles, and allocating a task to the node; each monitoring scheduling node monitors the task execution progress in real time; and after the node fails, task allocation is stopped, the tasks are rescheduled according to the priorities of the tasks, the optimal allocation nodes of the tasks are recalculated based on an EA-EFT model, and a recommendation mechanism is triggered to take over the tasks of the failed nodes after each scheduling fails. The problems that an unmanned aerial vehicle cluster scheduling scheme is poor in adaptability, low in efficiency and incomplete in fault-tolerant mechanism are solved, and the scheduling efficiency, the system robustness and the energy efficiency of an unmanned aerial vehicle cluster in a complex task scene are improved.

The invention relates to the technical field of aerocar mode switching, and discloses a distributed driving aerocar self-adaptive fault-tolerant control method for actuator multimode failure, which comprises the following steps: constructing a unified six-degree-of-freedom dual-mode state space model; residual signals are generated based on extended Kalman filtering and a sliding-mode observer, and fault types and positions are positioned in real time through a lightweight classifier; the method comprises the following steps: extracting residual time-frequency features, identifying hard faults by using a lightweight convolutional neural network, quantifying soft fault degrees through an incremental support vector machine, fusing multi-source information based on a Bayesian network to output fault types, levels and confidence coefficients, and introducing an incremental learning mechanism to realize self-evolution of a diagnosis model; a virtual control instruction is generated by adopting hierarchical sliding mode control, thrust and torque distribution of remaining actuators is optimized based on a dynamic quadratic programming algorithm, control parameters are adjusted online in combination with a Lyapunov adaptive law, aerodynamic interference and model uncertainty are inhibited, attitude stability and trajectory tracking in air-ground mode switching are guaranteed, and the method has the advantages of being high in reliability and high in reliability. And the fault-tolerant performance and the operation safety of the hovercar in the air-ground mode switching process are obviously enhanced.

The invention discloses a fault simulation method and equipment in a complex flight scene, belongs to the technical field of flight fault simulation, and aims to improve the fault-tolerant capability of a system and realize full-coverage simulation of the complex flight scene through state injection and fault simulation. The problems of insufficient fault scene coverage, real-time control and data reproducibility in the prior art can be solved; moreover, the method can inject the fault states of a plurality of devices in parallel in the same simulation process, and compared with a traditional method which only supports a single-device single-fault simulation mode, the method has the advantages that the capability of restoring a composite fault scene is greatly enhanced, and the method is suitable for limiting condition verification of a high-reliability aircraft system.

The invention discloses an access control equipment data management method and system based on multi-source fusion. The method comprises the following steps: collecting a multi-source access control data stream in real time; based on a preset feature extraction rule set, extracting a multi-modal biological feature vector, a voucher legality identifier, an abnormal behavior probability value and an equipment health degree index, and inputting the multi-modal biological feature vector, the voucher legality identifier, the abnormal behavior probability value and the equipment health degree index into a dynamic security assessment matrix generation model to generate a real-time security assessment matrix; matching the dimension safety score of the real-time safety evaluation matrix with a preset threshold strategy library, and dynamically generating an access control strategy instruction set; and issuing the access control strategy instruction set to the target access control equipment execution terminal. The method has the following advantages and effects: the fault tolerance bottleneck of a single-dimensional decision chain is broken through, and the system misjudgment rate is reduced by at least one order of magnitude on the premise of ensuring the security by establishing a dynamic coupling mechanism of the multi-source data stream.

The invention discloses a six-winding high-voltage variable-frequency speed regulation control method and system, and belongs to the technical field of high-voltage variable-frequency speed regulation, and the method comprises the steps: building an electric-thermal-magnetic coupled six-dimensional nonlinear system model through collecting the structural parameters, thermosensitive data and an initial state of a motor; establishing a frequency domain regulation and control matrix among the six windings in combination with the operation condition and the load type; predicting a future disturbance power trend by using a weighted short-time Fourier analysis method, dynamically correcting the reference current of each winding, and generating six groups of PWM control signals; when the performance of the winding is degraded, fault-tolerant topology reconstruction and driving task redistribution are executed; periodically reconstructing a control matrix through a thermoelectric feedback mechanism, and executing parameter adaptive optimization according to a current balance coefficient and hot redundancy distribution; according to the method, high-precision current coordination control, frequency domain energy decoupling and winding thermal load balancing can be realized, and the speed regulation response speed, the stability and the fault-tolerant capability of the system are remarkably improved.

The invention discloses a smart power grid fault-tolerant communication system and method based on multi-operator channel switching, and the method comprises the steps: collecting heterogeneous network and equipment state data through an intelligent sensing layer, achieving the network quality prediction and multi-target optimization through an AI decision engine, integrating spectrum resources and network slices through a resource cooperative control layer, and achieving the fault-tolerant communication of a smart power grid. The edge execution layer executes hardware-level fast switching, the emergency disaster recovery layer provides multi-dimensional fault recovery, and the security trusted layer guarantees data security. The method breaks through the bottleneck of traditional network switching, realizes high-reliability and low-delay transmission of power business, is suitable for scenes of intelligent substations, distribution network automation and the like, and remarkably improves the fault-tolerant capability and resource utilization efficiency of intelligent power grid communication.

The invention discloses an encoder and Hall dual-mode redundancy closed-loop driving method and system, and relates to the technical field of motion control and fault tolerance, and the method comprises the steps: collecting the dual-channel signal data of an encoder and a Hall sensor in parallel in real time, and monitoring the redundancy through consistency detection; carrying out abnormity screening based on a preset rule on the collected double-path signals, carrying out data health assessment by using a machine learning model, and carrying out abnormity identification on the screened double-path signals; and fusing screening and identification results, dividing risk levels, and when an abnormal value exceeds a risk level threshold value, performing intelligent fault-tolerant switching. The dynamic threshold rule, the online incremental learning decision tree and the multi-level fusion grading strategy adopted by the method have sensitive capture capability on progressive and hidden faults. In a high interference scene, the accuracy and continuity of fault detection are effectively ensured through concept drift monitoring and a streaming decision tree online updating mechanism.

The invention relates to a railway material purchasing asynchronous task processing method, device and system, and the method comprises the steps: constructing a six-layer message channel architecture of a purchasing business process, and executing an instruction of deploying a message agent node in an active-active mode for each channel based on the core message middleware of a Kafka cluster; the front-end gateway is instructed to package the purchase event into a binary message body with a transaction ID and a timestamp, and the binary message body is written into a persistent queue after priority labeling is carried out according to a task type label; processing the concurrent tasks by adopting a multi-stage feedback queue scheduling algorithm, and dynamically allocating computing resources based on a thread pool; a maximum retry threshold value is set according to an exponential backoff retry strategy, so that the failed task is automatically transferred into a dead message queue; and dynamically calculating an optimal message routing path based on the supplier portrait and the material classification model, and extracting the order to an exclusive processing queue of the optimal supplier according to Topic dynamic switching of the message queue. According to the invention, the coupling degree is reduced, the system performance is improved, and the fault tolerance is enhanced.

The invention discloses an energy storage scheduling method and system in combination with dynamic load prediction, and particularly relates to the field of risk prediction of energy storage scheduling, comprising the steps of constructing a fusion structure comprising a perception input set, a disturbance trend set and a system variable set, and forming a perception input structural body in a scheduling process; comparing the perception input structural body with the behavior track sample set, generating a residual structural body and marking an abnormal behavior candidate section; and processing the abnormal behavior candidate section, and generating an abnormal confirmation result through a freezing mechanism and a trend consistency judgment process. An abnormal behavior candidate section caused by false load change is identified by constructing a sensing input structural body and introducing a behavior trajectory comparison mechanism, so that a misjudgment instruction is frozen before response, trend consistency verification is implemented, frequent start and stop and response drift of an energy storage system caused by sensing errors are avoided, and the fault-tolerant capability of the system is improved.

The invention relates to the technical field of distributed data storage, and discloses a database cluster partition operation method and device under a network partition scene and a medium, and the method comprises the steps that a multi-master distributed database cluster is constructed, and each node continuously captures a database native transaction log; obtaining change event data, and performing unified data storage; analyzing the original log into structured data, and executing uniform fragmentation and parallelization processing; processing the block log change record, and arranging change events of the primary key according to a rule; executing conflict processing and data synchronization, and synchronizing all nodes after the conflict is solved; through exception handling and fault tolerance, self-healing is realized and data consistency is maintained. According to the method, millisecond conflict identification and data consistency convergence after network disconnection can be realized, continuous and stable operation of a cluster in a partition state is ensured, and self-healing and continuous availability of a system are realized.

The invention discloses a heterogeneous database structure dynamic synchronization method and device and a medium, belongs to the technical field of database migration, and is used for solving the defects of the existing database migration technology in the aspects of environment without native change log support, structure synchronization and fault tolerance. The technical problems of log dependence, low structure repairing efficiency and weak migration fault-tolerant capability exist in the prior art. The method comprises the following steps: performing data structure differentiation comparison between a source library and a target library to generate a structure difference atlas; traversing and converting the abstract syntax tree after syntactic analysis in the source library to generate a DDL statement conforming to the target library; performing incremental synchronization processing on the migration task between the source library and the target library to obtain incremental synchronization information; if structural difference information is detected in the incremental synchronization information, carrying out structural repair processing on the target library; and if the structure repair result information is repair operation failure information, performing data fusing processing on the migration task.

The invention relates to the technical field of industrial fault prediction, in particular to a pomegranate extraction production line fault self-diagnosis cooperative control system based on digital twinning. An electric inspection type monitoring method is mainly adopted to simulate the fault situation of a production line, a high-fidelity digital twin model is constructed to achieve dynamic mapping of a control system, the operation state of the industrial control system is continuously monitored, and when a fault is detected, an optimal maintenance regulation and control suggestion is given in time according to the fault level. The fault self-diagnosis cooperative control system comprises four core modules and is provided with an online verification unit, the system is periodically checked and tested, and closed-loop verification and evaluation of the performance of the control system are completed. Besides, the system also has the ability of autonomous learning, and the current fault can be recorded in the fault matching library after analysis and regulation, so that the analysis and diagnosis ability can be continuously optimized, the fault tolerance rate of the industrial production monitoring system is enhanced, and a solution and suggestions are provided for intelligent diagnosis of the industrial process.

The invention discloses a fault-tolerant reconstruction method for sensing faults of a spatial dexterous hand, and relates to the field of robot control. In order to solve the defects that in the prior art, a dexterous hand on-orbit sensor is weak in fault-tolerant capability, insufficient in model generalization capability and incapable of being adaptive to complex space environment changes, the technical scheme provided by the invention comprises the following steps: constructing a conditional variation auto-encoder model, inputting a joint position and a rope length sequence, and constructing a model; time sequence features are extracted, and a reconstructed joint position mean value and a prediction variance are output; the reconstructed joint position mean value is compared with actual joint data, a dynamic confidence interval is generated, and whether the data breaks down or not is judged; for the data judged to be normal, information entropy is calculated based on the prediction variance, and data samples with the information entropy exceeding a set threshold value are screened and written into a dynamic buffer area; and when the buffer data is fully loaded, incremental learning is triggered, and model parameters are updated by utilizing the buffer data. The method is suitable for real-time detection and fault-tolerant reconstruction of sensor faults in the long-term on-orbit operation process of the space dexterous hand.

The invention discloses a multi-turn memory optomagnetic encoder hybrid system, belongs to the technical field of position and angle measurement encoders, and aims at solving the problems that an existing optomagnetic hybrid encoder is complex in signal processing, long in axial length, insufficient in precision stability and prone to losing the number of turns and the position. The system comprises an optical encoder, a gallium arsenide magnetic signal acquisition sensor, a multi-antipode unequal magnetic ring, a processor, a hardware circuit, an ultra-low power consumption circuit, a signal acquisition module, a signal processing module and the like, integrates self-developed magnetic angle analysis, turn number memory correction and other algorithms, and adopts dual-redundancy design and intelligent signal mode switching. The method simplifies the signal processing flow, shortens the axial length, improves the measurement precision and stability, enhances the environmental adaptability and fault-tolerant capability, prolongs the endurance, and is suitable for the position angle measurement in a high-precision and severe environment.

The invention provides a distributed intelligent multi-machine cooperative operation system and method, a deployment device and equipment, belongs to the technical field of industrial automation and intelligent operation system crossing, and aims to solve the problems of low reliability, rigid static task distribution, poor compatibility of heterogeneous equipment and insufficient fault-tolerant capability caused by a centralized architecture in the prior art. The operating system comprises a communication module, a task scheduling module, a heterogeneous equipment adaptation module, a fault-tolerant and self-healing module and a human-computer interaction module. The operating method comprises the following steps: S1, establishing an equipment communication network; s2, equipment state visualization and task instruction issuing; s3, dynamically allocating tasks and scheduling the tasks; and S4, carrying out equipment fault monitoring and self-healing recovery strategies.

The invention discloses a computing power resource dynamic scheduling system integrating environmental perception and power self-balancing, and relates to the technical field of computing power resource scheduling. According to the system, multi-dimensional data inside and outside a specified computing power facility operated by a server cluster are collected in real time through a data collection and environment perception fusion module, and dynamic / static fusion processing is carried out on the multi-dimensional data and current environment perception parameters, so that comprehensive perception of the environment and the operation state is realized; then, resource configuration is optimized by combining a combined scheduling and self-power balance control module with a fusion processing result; finally, a hierarchical scheduling scheme is generated through a hierarchical scheduling and multi-scene adaptation verification module, and whether the generated hierarchical scheduling scheme adapts to the current scene or not is judged by combining progressive derating judgment and error-tolerant rate verification, so that dynamic linkage regulation and control of the load, the environment and the power are realized, the problem of unbalanced power distribution in computing power scheduling is effectively solved, and the service life of the computing power scheduling system is prolonged. And the energy efficiency ratio and the environment adaptive capability of the server cluster are improved.

The invention discloses a charging pile liquid cooling module intelligent cooperative control system based on an industrial Ethernet, and relates to the technical field of liquid cooling control. The charging pile liquid cooling module intelligent cooperative control system based on the industrial Ethernet comprises a liquid cooling data acquisition and preprocessing unit used for acquiring and preprocessing liquid cooling cooperative operation data; the thermal load sensing and intelligent liquid cooling regulation and control unit is used for dividing independent cooling branches, evaluating the thermal load degree of each cooling branch, judging the real-time thermal load state and generating and executing a control instruction; the anomaly detection and cooling redundancy control unit is used for evaluating the thermal response deviation condition of a cooling branch and judging whether a double-ring redundancy pipeline is started or not; and the fault root cause judgment and recovery enhancement unit is used for evaluating the component fault risk degree in the abnormal branch and executing fault-tolerant control and branch recovery strategies. The problem that a whole set of liquid cooling system is shut down due to the fact that local fault tolerance cannot be achieved when a local branch breaks down in a traditional pipeline structure is solved.

Systems and methods for decentralized data storage and retrieval within a distributed computing system include partitioning datasets into encrypted data shards that are each associated with a unique identifier. A processing system operating within the network may distribute these shards across storage nodes selected based on performance metrics such as latency, storage utilization, and reliability. Metadata for each shard, including its identifier, unique cryptographic key, and node location, may be recorded in a tamper-evident distributed ledger. Upon receiving a retrieval request, the system may query the ledger to identify shard locations, retrieve the shards in parallel, validate their integrity by comparing ledger metadata with metadata from storage nodes, decrypt the shards using their respective cryptographic keys, and reconstruct the dataset. These systems and methods enhance data security, integrity, and fault tolerance while allowing efficient and verifiable decentralized storage and retrieval in dynamic, distributed environments.

The invention relates to the field of computer big data, and discloses a self-adaptive parallel computing high-dimensional data dimension reduction and classification optimization system which comprises a quantum tensor decomposition module, a dynamic routing module, a heterogeneous computing classification module and a feedback regulation and control module. The quantum tensor decomposition module realizes nonlinear feature extraction of high-dimensional data through super-adjacency tensor modeling and entanglement entropy constraint; the dynamic routing module generates an adaptive communication path based on pulse time sequence weight optimization and quantum key verification; the heterogeneous calculation classification module dynamically schedules a quantum processor according to data characteristics, and FPGA and GPU resources execute mixed gradient aggregation; and the feedback regulation and control module guarantees the robustness of the system through multi-dimensional parameter closed-loop correction and a multi-stage fault-tolerant mechanism. All the modules cooperate to form a closed loop of dimension reduction, transmission, classification and regulation, the problems of feature extraction distortion, low resource utilization rate and poor dynamic environment adaptability in the prior art are solved, and the real-time performance and reliability of high-dimensional data processing are remarkably improved.

The invention relates to the technical field of optical communication, in particular to an algorithm service system and method for an optical transport network, and the system comprises an optical network sensing module which is used for collecting optical layer performance parameters, service flow data and node equipment operation states of the optical transport network; the deep algorithm decision center predicts service traffic based on a space-time diagram convolutional network, and optimizes routing wavelength allocation through reinforcement learning; the strategy adaptation module is used for dynamically generating an operation and maintenance strategy and an energy-saving scheduling instruction according to the prediction result and the optimization scheme; the risk assessment unit is used for assessing a link fault risk, a service interruption risk and a network congestion risk in real time, quantifying a risk weight through an analytic hierarchy process, and optimizing an operation and maintenance strategy in combination with historical fault data; and the intelligent resource reconstruction unit is used for performing optical channel reconstruction according to the optimized operation and maintenance strategy, dynamically allocating optical layer resources or switching redundant transmission paths. Therefore, the problems of poor strategy adaptability, low resource scheduling efficiency, insufficient fault-tolerant capability and the like in the prior art are solved.