56 results about "Maintenance actions" patented technology

The invention provides a GPU (Graphics Processing Unit) hardware health prediction and active maintenance method and system, and belongs to the technical field of computing hardware maintenance and fault prediction. Based on the GPU health state data, a health degree comprehensive score of the GPU is calculated by using a preset health degree evaluation model, and a key health index in a future set time period is predicted by using a time sequence model. Calculating the fault probability of a preset type of fault risk by using a preset fault risk model based on the health degree comprehensive score of the GPU and / or the predicted key health index; and comparing the calculated fault risk probability with a preset threshold value, and executing a main preset maintenance action when the fault risk probability exceeds the preset threshold value. Through multi-source data acquisition and time sequence prediction, the potential fault risk of the GPU is found in advance, the maintenance action is actively executed, task interruption and data loss are reduced, and the reliability and availability of a GPU cluster are improved.

The invention provides a training method of an aircraft maintenance simulation model and an aircraft maintenance simulation method, relates to the technical field of aircraft maintenance simulation, and aims to predict future evolution of aircraft maintenance so as to improve authenticity of aircraft maintenance simulation. The method comprises the following steps: acquiring training data related to maintenance simulation; training a maintenance simulation model based on the training data; the aircraft maintenance simulation model comprises a video word segmentation device, a multi-modal input encoder, a multi-modal token sequence and a multi-modal output encoder, wherein the video word segmentation device is used for encoding videos related to aircraft maintenance simulation into a video token sequence; the multi-modal input encoder is used for encoding multi-modal input data related to aircraft maintenance into a multi-modal token sequence; the potential action model is used for determining a potential action representation of a maintenance action based on the video token sequence and the multi-modal token sequence, and the dynamic prediction model is used for predicting a prediction token at the next moment based on the video token sequence, the multi-modal token sequence and the potential action representation so as to simulate a maintenance scene at the next moment.

The invention relates to the technical field of sensing, and discloses an equipment operation maintenance state intelligent monitoring system and method.The system comprises a multi-mode sensing acquisition module, an edge calculation analysis module, a digital twinborn decision module and a maintenance execution control module; when the operation state of the equipment is monitored, static parameters and dynamic operation data of the equipment are fused, multi-dimensional feature extraction and abnormal coupling analysis of the health state of the equipment are realized, early potential faults of the equipment can be accurately identified, the problems of false alarm and missing alarm caused by monitoring of a traditional single sensor are avoided, the accuracy and reliability of state evaluation are improved, and the reliability of state evaluation is improved. The method is advantaged in that continuous and safe operation of equipment is guaranteed, long-term benefits of different maintenance actions are simulated in a virtual environment based on equipment degradation state quantitative indexes during equipment maintenance strategy optimization, and a problem of non-planned shutdown caused by dependence on artificial experience and maintenance lag of a traditional threshold alarm mechanism is solved.

The invention discloses a network operation and maintenance automatic management method based on deep learning, and the method comprises the following steps: collecting operation and maintenance data, and carrying out the preprocessing of the operation and maintenance data, and obtaining a standardized data set; adopting a Mama selective state space network to generate an operation and maintenance state vector; inputting the operation and maintenance state vector into the improved CORAL model to generate an anomaly detection result; generating a root cause analysis result by adopting a causal inference method; generating candidate operation and maintenance actions by adopting a constraint modeling method in combination with a service level agreement rule; obtaining an optimal operation and maintenance action by adopting an action evaluation method and a risk analysis method, issuing and executing the optimal operation and maintenance action, and collecting execution feedback data; and generating an operation and maintenance audit report based on the anomaly detection result, the root cause analysis result and the execution feedback data. Intelligent, automatic and auditing management of the whole operation and maintenance process is realized, and the method can be widely applied to intelligent operation and maintenance scenes of a large-scale complex network.

The invention discloses a civil aviation fleet maintenance decision-making method based on multi-body distributed hierarchical reinforcement learning, and the method comprises the steps: constructing a civil aviation fleet maintenance decision-making environment through fleet parametric modeling, and simulating a multi-aircraft multi-task scheduling and maintenance process in real operation; and key factors such as equipment degradation, maintenance resource constraint and task execution priority are fully considered. In the environment, a distributed hierarchical reinforcement learning algorithm is adopted: an upper-layer strategy is used for global maintenance resource allocation and task coordination, and a lower-layer strategy is used for local maintenance actions of a specific body. According to the method, a shared communication mechanism and a reward structure are introduced among multiple agents, so that the cooperation efficiency is improved, and the training convergence is accelerated. Experimental results show that the method is remarkably superior to a traditional heuristic method and a standard reinforcement learning algorithm in the aspects of task completion rate, maintenance cost control and system robustness, and high efficiency and reliability of civil aviation team maintenance are achieved.

According to various embodiment, a method for performing maintenance of a communication network is described comprising receiving a specification of one or more maintenance goals for one or more network entities of the communication network, determining a set of maintenance actions to achieve the one or more maintenance goals, determining at least one impact of the set of maintenance actions on one or more further network entities if they are applied to the one or more network entities, determining one or more further maintenance actions which prevent negative impact of the set of maintenance actions on the one or more further network entities, determining a maintenance action schedule for at least a subset of the maintenance actions and the determined one or more further maintenance actions and carrying out the maintenance actions of the subset of the maintenance actions and the determined one or more further maintenance actions according to the maintenance action schedule.

The present disclosure relates to a method of providing for maintenance of a separator (1), and a separator (1) performing the method. The method comprises determining (S101) operational modes in which the separator (1) is controlled to operate upon performing separation processes, storing (S102) information as to which operational modes are performed by the separator, evaluating (S103) the stored information for scheduling a maintenance action to be performed on the separator (1) based on the performed operational modes, and alerting (S104) an operator (41) of the separator (1) of the scheduled maintenance action to be performed on the separator (1).

A method for maintaining a test table (2) using a maintenance unit (4) is described, wherein the test table (2) has a surface (6) with at least one test module (8) for connecting and testing a cable set, wherein the maintenance unit (4) has a sensor unit (10) positioned above the surface (6), wherein the maintenance unit (4) uses the sensor unit (10) to detect the test module (8) and determine its current state, wherein the maintenance unit (10) determines an ID of the test module (8), wherein the maintenance unit (4) uses the ID to retrieve a target state for the test module (8) from a database (14), wherein the maintenance unit (4) compares the current state with the target state and automatically performs a maintenance action for the test module (8) based on the comparison. A corresponding maintenance unit (4) is further described.

The invention discloses an equipment maintenance action digital processing method and device, equipment and a storage medium, and relates to the technical field of industrial production and equipment management, and the method comprises the steps: obtaining the maintenance video data of target equipment in a maintenance scene; dividing the maintenance video data into a plurality of independent action segments, and synchronously identifying an action category and operation tool information corresponding to each action segment; on the basis of the action category and the operation tool information of each action section, generating a special semantic descriptor for maintenance of the target equipment in the maintenance scene; and generating a standardized maintenance log based on the maintenance special semantic descriptor of the target equipment. Through the above mode, maintenance operation and operation tools are directly and accurately extracted from the video data, the accuracy and adaptability of extraction are ensured, and the generated standardized maintenance log can be used as a digital carrier of experience and skill of workers for a maintenance scene, thereby providing reliable data support for intelligent maintenance decision and skill training.

The present application relates to the technical field of bridge resilience operation and maintenance management, and discloses a near-shore bridge operation and maintenance action optimization and scheduling management system based on resilience management, which comprises: a state and data modeling module, which defines the deterioration state and exposure state of the near-shore bridge components and obtains passive agent observation data; a likelihood and tail distribution module, which constructs an observation likelihood model and establishes a heavy-tailed joint distribution; a shock identification and updating module, which identifies shock events and updates the posterior distribution of the deterioration state and exposure state; a safety index calculation module, which calculates the resistance distribution and demand distribution, evaluates the failure probability and reliability index; an operation optimization and scheduling module, which optimizes the operation action scheme, resource allocation and scheduling; a resilience target configuration module, which calculates the resilience gap score and generates a set of resilience strategy parameters; and a monitoring and auditing module, which splits the task list and calculates the cost deviation, progress deviation and resilience back-off amount. The present application realizes the operation action optimization and efficient resource allocation of the near-shore bridge.

The invention discloses an operation and maintenance analysis method, device and equipment for a virtual power plant and a medium, and the method comprises the steps: obtaining structured time series data, text data and image data generated in the operation and maintenance process of the virtual power plant, and carrying out the feature extraction of the multi-modal data, and obtaining a multi-modal fusion feature; and then, inputting the multi-modal fusion features into the constructed causal enhanced multi-agent fault diagnosis framework for reasoning so as to determine the fault root cause and the propagation path thereof of the virtual power plant. And meanwhile, an equipment health state evaluation model based on meta reinforcement learning is constructed, the model is combined with the multi-modal fusion features and the dynamic knowledge graph, the health state of the equipment in the virtual power plant is evaluated, and corresponding maintenance actions are output. The accuracy and practicability of operation and maintenance analysis of the virtual power plant can be improved, and the operation reliability, economy and flexibility of the virtual power plant in a complex power grid environment can be enhanced. The method can be widely applied to the technical field of electric power.

A structural-health-monitoring system is disclosed for real-time detection and autonomous maintenance of physical structures. The system includes a sensor network comprising at least one strain gauge and one tri-axial accelerometer mounted on the structure to generate real-time sensor signals. A perception module filters and normalizes the signals and extracts numerical features such as peak amplitude and dominant frequency. A digital-twin module maintains a finite-element model updated in response to the extracted features. A data-driven surrogate model predicts sensor behavior and refines itself using machine-learning techniques. An anomaly-detection module computes an anomaly score from model residuals or classifier outputs. Upon exceeding a threshold, a maintenance module initiates a maintenance action, including generating an inspection schedule or issuing a control signal to an autonomous inspection or repair device. A learning module continuously improves system performance using reinforcement learning based on historical outcomes. The system supports predictive diagnostics, robotic repair, and automated optimization for long-term structural integrity.

The invention relates to the technical field of equipment management, in particular to a food processing equipment management system based on cloud edge collaboration, which comprises a production disturbance quantification module, a micro-shutdown time window identification module, a maintenance task dynamic matching module and a maintenance state closed-loop feedback module. According to the method, disturbance of maintenance on production takt is quantified by constructing a discrete event simulation model, complex maintenance is decomposed into an atomization action sequence, non-fault shutdown time windows such as production switching are actively recognized, the duration time is accurately predicted through a K-nearest neighbor algorithm, atomization maintenance actions are dynamically matched and combined, and the production takt time is accurately predicted. The method comprises the steps of generating a micro task package which can just fill the downtime and pushing and executing the micro task package, then forming closed-loop feedback according to a completion signal, updating the global task progress in real time, changing passive planned maintenance into active opportunity maintenance, and completing maintenance without interrupting the production process in a seaming manner, so that the comprehensive efficiency and the production continuity of equipment are remarkably improved.

The invention discloses a refrigerator which comprises a storage chamber and a refrigerator body. A cooling unit; an air door; the processing unit comprises a scene diagnosis part, a fault diagnosis part and an execution part; the scene diagnosis part is configured to receive a time sequence of the operation data from the detection unit, diagnose an abnormal scene by using a scene diagnosis model, and collect a scene diagnosis result of temperature abnormity of the storage chamber; the fault diagnosis part is configured to receive a time sequence of the operation data from the detection unit, diagnose fault classification causing temperature abnormity of the storage chamber by using a fault diagnosis model, and collect a fault diagnosis result of an air door fault when a collected scene diagnosis result meets a preset scene evaluation condition; and the execution part is configured to respond to an evaluation result to execute one or more maintenance actions when the collected fault diagnosis result of the air door fault meets a preset fault evaluation condition. The refrigerator provided by the invention can accurately detect the fault of the air door.

The application relates to a device deployment and long-term self-maintenance method, device, computer equipment, computer readable storage medium and computer program product. The method comprises the following steps: obtaining a device running state, an installation relationship state, a calibration reference state and a reference archive state of a measuring device; determining an installation relationship parameter of the measuring device according to the installation relationship state, comparing the installation relationship parameter with a standard installation state of the measuring device, and obtaining a deployment health state of the measuring device; establishing a calibration reference archive of the measuring device according to the calibration reference state and the reference archive state; analyzing the device running state to obtain a component health state of the measuring device; performing device health self-maintenance or calibration maintenance on the measuring device according to the component health state, the deployment health state and the calibration reference archive, and outputting a maintenance action result. The application can improve the efficiency and accuracy of device maintenance.

This invention discloses a machine learning-based method for user operation and maintenance decision-making in an embedded tag e-commerce platform, comprising: collecting multi-source behavioral and operation and maintenance interaction data to construct a current session state set; constructing an intent impulse sequence and generating a tag reachability tensor, constructing and updating a tag debt state set; constructing a multi-layer hypergraph of tag position and action, and constructing a corresponding multi-scale input feature set; inputting the multi-scale features into an improved deep factorization machine model to generate a counter-evidence net effect value; selecting target operation and maintenance solutions based on the net effect value, and constructing a tag commitment execution chain across page positions; controlling the embedding position and timing of embedded tags and the execution of operation and maintenance actions, and updating the model and the tag commitment execution chain. This invention achieves accurate reach and continuous operation and maintenance decision-making for embedded tags in multi-page, multi-stage sessions by constructing a tag debt memory mechanism and an improved deep factorization machine dual-mirror recognition model.

This invention discloses a deep learning-based method for predicting storage device failures, comprising: collecting operational monitoring data of the storage device; preprocessing to construct a time-series health representation sequence; performing adaptive window construction to extract the modeling sequence; establishing a topology graph and aggregating the node-level health representation sequences; constructing an improved SCINet model to obtain a prediction sequence; calculating the prediction residual sequence and generating a standardized residual sequence; outputting a failure risk judgment result based on the sequential probability ratio test; generating alarm information and triggering coordinated operation and maintenance actions. This invention, by combining the improved SCINet model and the sequential probability ratio test, achieves online prediction and early warning-based coordinated handling of storage device failure risks.

A diffusion strategy method and system for guiding multi-level safety constraint fields in embodied intelligent operation and maintenance robots is disclosed. First, a multi-level safety constraint field is constructed for equipment operation and maintenance scenarios, quantifying the safety risks of the safe operation zone, equipment sensitive access zone, personnel activity interference zone, and equipment protection prohibition zone in the form of a continuous differentiable scalar field. Then, a multi-modal state observation module for the operation and maintenance scenario is designed, integrating robot body state, target equipment geometry, and safety topology distance information. Next, the gradient of the multi-level constraint field is injected into each step of the diffusion strategy's denoising calculation using an embedded denoising guidance method, causing the denoising trajectory to deviate along the direction of decreasing constraint field value. Simultaneously, a learnable adaptive guidance intensity scheduling function is employed, outputting a larger guidance intensity in the macro-path planning stage and a minimal guidance intensity in the fine-grained control stage. Finally, an operation and maintenance action sequence that satisfies the multi-level safety constraints is output. This invention achieves an optimal balance between safety constraints and fine-grained control.

The invention relates to a distribution line air switch operation and maintenance method and device and a medium, and belongs to the technical field of power system operation and maintenance, and the method comprises the following steps: obtaining power data of an air switch in a distribution line, and calculating the thermal power loss of the air switch based on the power data; obtaining the temperature of the air switch, and calculating the to-be-predicted time temperature of the air switch based on the thermal power loss and the temperature; calculating a thermal stress index of the air switch based on the temperature and the to-be-predicted time temperature; determining historical maintenance times of the air switch, and determining an operation and maintenance action threshold value based on the thermal stress index and the historical maintenance times; and generating an operation and maintenance instruction based on the operation and maintenance action threshold value and the temperature, and overhauling the air switch according to the operation and maintenance instruction. According to the invention, by constructing the electric power data and temperature prediction model, operation and maintenance mode transformation from passive response to active prevention is realized.

The invention discloses a dynamic optimization method and system for a maintenance strategy of a digital twin-driven pressure vessel, and relates to the technical field of industrial equipment health management and intelligent operation and maintenance. The method comprises the following steps: constructing a digital twinborn model integrated with a physical failure mechanism; performing dynamic calibration on model state variables and parameters by using real-time operation data and a data assimilation algorithm; integrating data from a manufacturing execution system, an enterprise resource planning system and a computerized maintenance management system to form a dynamic constraint set; performing multi-dimensional simulation deduction based on the calibrated model and the dynamic constraint set, generating a candidate maintenance action set, and calculating an expected comprehensive cost to select an optimal strategy; and acquiring actual equipment state and resource consumption data after strategy execution, and performing bidirectional self-adaptive correction on the digital twin model and maintenance cost parameters to realize closed-loop optimization. The technical problem that an existing maintenance strategy is difficult to cooperate with the equipment health state, residual life prediction and external multi-dimensional resource constraint in real time for dynamic decision making is solved.

The invention discloses an operation and maintenance method and system for a credential and credential heterogeneous system, and belongs to the technical field of operation and maintenance. The method comprises the following steps: fusing multi-modal operation data through an attention mechanism to obtain a multi-modal joint feature vector; intention classification is carried out through the multi-modal joint feature vector, so that an operation and maintenance intention classification result is more comprehensive and accurate; and then operation and maintenance keyword extraction and operation and maintenance intention classification result analysis are carried out, so that the operation and maintenance intention is more accurate and comprehensive. Then, through the running trend prediction model and the running trend analysis window scale, running trend prediction of the credential heterogeneous system is realized, and then potential operation and maintenance requirements can be found in advance; and then an operation and maintenance service scheme is generated through the operation and maintenance intention and the operation trend prediction result, executable operation and maintenance actions and target operation and maintenance nodes are determined and converted into operation and maintenance instructions, comprehensive and accurate operation and maintenance of the information and creative heterogeneous system are achieved, and the accuracy and comprehensiveness of operation and maintenance of the information and creative heterogeneous system are improved.

In one scenario, an information processing method, device, equipment, storage medium and program product are provided, the method comprising: obtaining first information, the first information comprising alarm information of a task; inputting the first information into an intelligent system to obtain second information; wherein the intelligent system is configured to call at least one troubleshooting tool in a first tool set to perform root cause analysis on the task, the troubleshooting tool is configured to execute an analysis step for the task, and the second information represents a root cause of the problem corresponding to the alarm information; presenting the second information and a first operation control in a first interface, the first operation control being configured to trigger an operation and maintenance action corresponding to the root cause of the problem. The processing efficiency of information can be improved.

The invention provides an operation and maintenance processing method and system of a tunnel intelligent fire-fighting system, and relates to the technical field of tunnel fire-fighting system operation and maintenance, firstly, an operation and maintenance trigger signal is received to start an interactive response process, and the trigger signal is derived from equipment abnormity feedback or environment change preset conditions; then collecting equipment operation situation and environment influence situation information to construct a comprehensive situation data set; based on a dynamic situation mapping model, establishing interaction association characteristics of the two, wherein the dynamic situation mapping model is formed by training historical interaction data; a targeted operation and maintenance action sequence is generated according to the interaction association characteristics, and equipment adjustment, component maintenance and environment adaptation operation are included and are sequenced according to priorities; and after executing the sequence, collecting and updating the situation information optimization model. The method can actively find hidden dangers, comprehensively and accurately analyze situations, generate targeted operation and maintenance actions, improve operation and maintenance efficiency and guarantee stable operation of a tunnel fire fighting system.

The invention discloses an electric power material storage maintenance method and device based on the Internet of Things, and relates to the technical field of storage maintenance methods, a plurality of sub-storage abnormal items are determined according to the recognition of a storage abnormal event, and a storage maintenance event of an electric power material is determined according to the plurality of sub-storage abnormal items and a material use plan. And the corresponding storage maintenance action is determined according to the storage maintenance event of the electric power material, the corresponding material position and the mobile storage equipment, so that the accuracy of the storage maintenance action is improved. Therefore, the storage maintenance schedule of the mobile storage equipment is determined according to the storage maintenance mode of the mobile storage equipment and the area form of each electric power material area; and according to the storage maintenance schedule, the work task process of the mobile storage equipment and the material arrangement plan of the electric power material warehouse, a corresponding material autonomous management and control system is determined, and the accuracy of the material autonomous management and control system is improved.

The application discloses a kind of laparoscope smoke removal equipment multi-agent collaborative operation and maintenance system and method, the system includes standardization operation and maintenance knowledge base, single equipment history library, message communication module and multiple agents: data acquisition agent obtains real-time characteristic parameter;Knowledge retrieval agent matches rule item from knowledge base;Health diagnosis agent is built-in rule guide working condition self-adaptive graph timing residual error network, pass through extraction timing degradation characteristics, dynamically adjust the fault propagation edge weight of equipment topology graph, combine historical memory characteristics, fusion output health status grade, root cause label and recommended operation and maintenance action;Operation and maintenance decision agent generates and executes operation and maintenance strategy.The system and method of the application improve the real-time, accuracy and automation level of equipment operation and maintenance.

The embodiment of the invention provides an autonomous collaborative operation and maintenance decision-making method and device for a rail transit turnout group, and the method comprises the steps: constructing digital twin bodies of the turnout group, and mapping the physical world state of turnout group equipment; respectively modeling into heterogeneous independent agents, storing a global state through a state space, and storing different action sets through an action space; a proposal action is output through the heterogeneous independent intelligent body, the global state and the proposal action are analyzed in real time through a large language model LLM subjected to domain knowledge fine adjustment, the proposal action of the heterogeneous independent intelligent body is evaluated, and a reward signal is generated; a multi-agent near-end strategy optimization MAPPO algorithm is adopted to train each agent strategy network, the trained agent strategy networks are deployed and operated online, a parameterized action space is designed for maintenance actions, and evaluation and reward shaping are carried out on the rationality of operation parameters by utilizing reward signals of LLM, so that the reward performance of the maintenance actions is improved. And finally, outputting an optimal collaborative decision scheme under multiple constraints.

The invention relates to a deep ground network dynamic self-adaptive operation and maintenance method and system based on a Stackelberg game. The method comprises the steps that edge nodes are deployed on a deep ground working face, partition gateways are deployed in underground partitions, a cloud data warehouse is deployed on the ground, a three-level information integration framework is formed, and pressure, gas, actuators, communication and personnel positioning data are collected and stored in a layered mode; setting a ground control center and a partition gateway as leader nodes, setting deep ground terminal equipment as follower nodes, constructing a Stackelberg master-slave game model, determining a state and operation and maintenance action set, and establishing a revenue function; and mapping the state and the action into a reinforcement learning space, solving and updating an operation and maintenance strategy table by adopting a DQN algorithm under bandwidth and energy consumption constraints, generating a control instruction by an operation and maintenance platform, issuing the control instruction through a partition gateway, calling a local emergency strategy in a high-risk scene, and realizing dynamic self-adaptive operation and maintenance of the deep ground network. According to the invention, the real-time performance and reliability of deep ground network operation and maintenance can be improved.

The invention discloses an intelligent digital management method and system for equipment assets, and relates to the technical field of equipment management.The method comprises the steps that a sensor cluster is used for collecting a multi-modal time sequence data stream sent by physical equipment, and the multi-modal time sequence data stream is converted into a high-dimensional feature vector; deducing internal state variables of the equipment at the current moment by using the high-dimensional feature vector, and generating an equipment health state vector after combination; analyzing the equipment health state vector under the time sequence arrangement, and generating a health degree degradation track; inputting the health degree degradation track to a decision maker, and generating an optimal maintenance action sequence for the equipment; and executing the generated maintenance action sequence, and continuously optimizing the decision maker according to the executed equipment state. According to the method, a traditional equipment management information island is broken, non-planned shutdown can be reduced, the maintenance cost is reduced, the equipment utilization rate and the management efficiency are improved, and the transformation from passive maintenance to predictive management is realized.

A progressive diffusion strategy method for guiding hierarchical reasoning in embodied intelligent operation and maintenance robots is proposed. First, a demonstration trajectory dataset of operation and maintenance tasks of the robot is collected, and three-level operation and maintenance supervision labels are extracted, including global path, operation strategy, and fine-grained execution labels. Then, encoders for global path reasoning, operation strategy reasoning, and fine-grained execution reasoning are trained based on these labels. The demonstration trajectory is reasoned frame by frame, generating three-way reasoning features, which are then concatenated with the original observations to form an enhanced observation sequence. Next, using the enhanced observation sequence as conditional input, a progressive operation and maintenance strategy based on a diffusion model is trained in conjunction with a stage-aware gating network. Finally, during the robot deployment phase, the three-level reasoning features are calculated in real-time from the original environmental observations using an augmentation wrapper during reasoning and concatenated. The enhanced observations are then input into the trained progressive diffusion strategy to generate a sequence of operation and maintenance actions. This invention improves the success rate of fine-grained operation tasks of operation and maintenance robots.

The invention relates to the technical field of bridge toughness operation and maintenance management, and discloses an offshore bridge operation and maintenance action optimization and scheduling management system based on toughness management, and the system comprises a state and data modeling module which defines the degradation state and the exposure state of an offshore bridge member, and obtains passive agent observation data. And the likelihood and tail distribution module is used for constructing an observation likelihood model and establishing heavy tail joint distribution. And the impact identification updating module is used for identifying the impact event and updating the posterior distribution of the degraded state and the exposed state. And the safety index calculation module is used for calculating resistance distribution and demand distribution and evaluating a failure probability and a reliable index. And the operation and maintenance optimization scheduling module is used for optimizing an operation and maintenance action scheme and resource allocation and scheduling. And the toughness target configuration module is used for calculating a toughness gap score and generating a toughness strategy parameter set. And the monitoring and auditing module is used for splitting the task list and calculating the cost deviation, the progress deviation and the toughness fallback amount. According to the invention, operation and maintenance action optimization and efficient resource allocation of offshore bridges are realized.