1229 results about "Maintenance system" patented technology

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 discloses a power distribution cabinet maintenance system based on artificial intelligence, and belongs to the technical field of intelligent power distribution cabinet diagnosis. Normalization, time sequence feature extraction and denoising are carried out on the collected data, an electrical topological graph is automatically constructed, node states are vectorized, text semantics are coded by utilizing a BERT class model, and a maintenance knowledge graph is generated; through fusion of multi-source data, a multi-branch neural network is constructed, state perception and risk determination are realized, and a fault trend and structure degradation are identified. Calculating a potential fault risk coefficient, and triggering a structure health monitoring mechanism; analyzing the structural health based on a topological graph and a graph neural network, and starting semantic strategy retrieval when the structure is abnormal; comparing the semantic conformity between the state and the historical strategy, and assisting in generating a precise maintenance strategy; according to the system, dynamic monitoring, intelligent early warning and strategy recommendation of the operation state of the power distribution cabinet are realized, and the operation and maintenance efficiency and the equipment reliability are improved.

The invention discloses a gas pipe network maintenance method and system based on a large language model, and electronic equipment, and aims to solve the ubiquitous problems that data fusion is difficult, fault analysis depends on manpower, the operation and maintenance automation degree is low and the like in a gas pipe network. The method comprises the following steps: firstly, acquiring and processing multi-source heterogeneous data of the gas pipe network, then vectorizing the multi-source heterogeneous data to construct a unified multi-modal database, when monitoring that the data is abnormal, adopting retrieval enhancement to generate a large language model, retrieving associated information from the database, performing fault root cause intelligent reasoning, and generating an analysis report, and finally, automatically generating a structured maintenance instruction according to the report, and docking with an operation and maintenance system to form a whole-process automatic closed loop from monitoring to disposal. The system and the electronic equipment are used for executing the method. The method has the advantages that a data island is broken through, accurate situation awareness is achieved, the efficiency and accuracy of fault diagnosis are remarkably improved, and the effect of reducing the operation cost and the safety risk is remarkable.

The invention relates to the technical field of engineering management and intelligent operation and maintenance, and discloses a digital twinning-based engineering consumable management and operation and maintenance system, which collects construction data in real time through multi-modal perception, identifies operation behaviors through a behavior twinning module, verifies behavior compliance through a grammar verification module, and sends the construction data to the engineering consumable management and operation and maintenance system after verification is passed. The dynamic consumption cancel-after-verification module is combined with behaviors, working conditions and tool data to predict consumable consumption and automatically cancel-after-verification inventory, the self-adaptive correction module continuously optimizes the prediction model, and meanwhile, the predictive maintenance module monitors the tool state, generates a maintenance work order and binds related consumables when discovering abnormity. According to the method, through multi-source data fusion, intelligent behavior identification, process logic verification, dynamic accurate verification, model adaptive optimization and tool predictive maintenance, fine management of engineering consumable consumption and intelligent operation and maintenance of operation tools are realized, and the aims of improving construction efficiency, reducing consumable consumption and prolonging the service life of the tools are achieved.

The invention relates to the technical field of power system automation, in particular to a monitoring and maintenance system and method for a transformer substation. The system comprises a quantitative classification module, a difference analysis module and a grading adjustment module. Equipment in a transformer substation is classified through the quantitative classification module, correction parameters such as aging and load loss are introduced for single-equipment monitoring through the difference analysis module, misjudgment and missed judgment caused by equipment state changes are reduced by combining multi-level threshold values, associated equipment is modeled through a GNN model, neighbor states are aggregated, the equipment health degree is output, and the equipment quality is improved. The method comprises the following steps of: quantifying a fault propagation probability, pre-warning cascading fault risks in advance, and adjusting a threshold value of a single device in a linkage manner while a hierarchical adjustment module takes risk response measures, so that single device monitoring is adaptively matched with an associated device risk state, full-coverage accurate monitoring is realized, and local and global risks are considered.

The invention discloses an intelligent predictive maintenance system for an audio equipment fault, and the system comprises a data collection module which collects the internal sensor data during the operation of audio equipment, and outputs an audio signal feature parameter, an external environment parameter, and a historical operation log; the feature preprocessing module is used for carrying out standardization and noise reduction processing on the multi-source data based on a multi-modal feature fusion algorithm; the health state evaluation module outputs a health state evaluation result of the audio equipment in real time through a hybrid analysis model combining a convolutional neural network, a long and short-term memory network and an attention mechanism; the fault risk prediction module is used for performing real-time fault risk prediction according to the evaluation result and generating predictive maintenance decision parameters; and the maintenance decision and early warning module is used for outputting fault early warning information according to the prediction parameters and automatically generating maintenance operation suggestions when the early warning level reaches a preset condition. According to the invention, the operation reliability of the audio equipment can be effectively improved, and intelligent prediction and advanced maintenance of faults are realized.

The invention discloses a heterogeneous system integration and fault diagnosis operation and maintenance system based on big data analysis, and the system is characterized in that the system comprises an acquisition cleaning module which is used for collecting structured data, semi-structured data and non-structured data, and carrying out the data cleaning; the mapping calculation module is used for dynamically mapping the cleaned data, and storing the data into a database after federal calculation; the feature extraction module is used for performing multi-modal extraction on the data in the database, constructing a knowledge graph and generating features for fault diagnosis; the model training module is used for constructing a fault diagnosis model and performing fault prediction and root cause analysis by using fault diagnosis features; the collaborative decision-making module is used for carrying out collaborative decision-making on the edge and the cloud according to the analysis result; and the feedback optimization module is used for feeding back the response processing result to the data center and carrying out updating iteration on the diagnosis model.

The invention relates to the technical field of electric power intelligent judgment, in particular to an AI-based intelligent electric energy meter fault diagnosis system, which comprises an operation data acquisition module, a jump correlation judgment module, an operation deviation judgment module, a current waveform diagnosis module and an abnormity alarm module. According to the invention, through synchronous acquisition of the current data and the operation data of the refrigeration storage, whether the current jump is related to the gating action is judged, and the refrigeration performance abnormity of the refrigeration storage and the fault type of the intelligent electric meter are effectively identified by further combining multiple cross validation of baseline energy consumption and temperature change; an ideal current waveform is constructed through an AI algorithm and is compared with an actually measured waveform, so that intelligent judgment and classification of fault types are realized; meanwhile, the diagnosis result is linked with the operation and maintenance system, the response efficiency is improved, and the method is particularly suitable for scenes with high requirements on the stability of the electric meter, such as cold chain storage, and has good practical value and popularization prospect.

The invention relates to the field of intelligent operation and maintenance, and discloses a multi-source network data operation and maintenance system based on micro-service architecture and AI collaboration, comprising the steps of collecting multi-source data of a micro-service system, and performing cleaning, format unification and time alignment on the collected data; based on a data result of the data acquisition module, constructing a micro-service call chain and a dependency graph, embedding a real-time performance index and a log feature in each node, and dynamically updating a service relation graph; carrying out real-time anomaly detection on the multi-source data, and judging the alarm effectiveness in combination with a dynamic threshold and an AI alarm confidence self-learning mechanism; the AI conducts reasoning along the call chain anomaly map, causal relationship reasoning is added, and the anomaly propagation influence range is predicted; and feeding back a root cause positioning result and the optimized alarm information to an operation and maintenance system, optimizing an alarm threshold and decision parameters in combination with historical records, and outputting an updated operation and maintenance decision scheme. The method has the advantage of improving the operation stability of the system.

The invention discloses a power equipment dynamic health degree assessment and predictive maintenance method, and belongs to the technical field of railway power system operation and maintenance. The method comprises the following steps: constructing a parameterized digital twinborn body of power equipment, and collecting real-time operation data, resume data and environment data; based on the parameterized digital twins and the collected data, equipment health degree components are calculated through a multi-model cooperation method, and a comprehensive health index is generated through fusion; performing equipment life prediction and maintenance decision generation according to the comprehensive health index, and outputting an optimal maintenance strategy; and performing visual virtual rehearsal and augmented reality auxiliary execution on the optimal maintenance strategy to form a closed-loop maintenance system. According to the method, the problems of data and model separation, model static stiffness and health assessment deficiency in the prior art are solved, dynamic perception, accurate assessment and predictive maintenance of the equipment state are realized, and the operation and maintenance efficiency and the system reliability are improved.

The invention discloses a new energy equipment intelligent operation and maintenance system and method based on digital twinning, and relates to the technical field of new energy equipment operation and maintenance management. The system comprises a data acquisition module, a digital twin construction module, a model adaptive module, an intelligent analysis module, a decision optimization module and a knowledge closed-loop module. The data acquisition module realizes multi-source heterogeneous data fusion and standardization; the digital twin construction module generates geometric, physical and behavior models and is linked with real-time data; the model adaptive module dynamically calibrates the key parameters; the intelligent analysis module completes anomaly detection, fault diagnosis and life prediction; the decision optimization module formulates a maintenance scheduling and operation strategy and realizes a control closed loop; and the knowledge closed-loop module constructs a structured fault graph through text analysis to continuously optimize the model. The method can be widely applied to wind power, photovoltaic, energy storage and other scenes, and efficient and intelligent operation and maintenance of new energy equipment are achieved.

The invention discloses an active prediction type modular battery thermal management system, which comprises the following steps: a liquid path and electrical connection module, a data acquisition module, a communication transmission module, a working condition identification module, a feedback control module, a cooperative control module, an execution adjustment module, a state feedback module, an exception handling module, a redundancy switching module and the like. A prediction control instruction is generated by combining a working condition recognition result, a fusion control quantity is formed by the prediction control instruction and temperature feedback, amplitude limiting control is conducted according to thermal power evaluation and a set threshold value, coordinated adjustment of a pump, a valve and a bypass assembly is achieved, and if recognition fails or data is abnormal, the system automatically freezes prediction control and executes closed-loop adjustment only depending on feedback. And the standby device takes over the fault after fault judgment, so that continuous operation and safety maintenance of the system are guaranteed. The system has a modular structure and complete maintenance process support.

The invention discloses an abnormal operation state maintenance system and method for an internet platform. The system comprises a data acquisition module, an intelligent analysis module, a strategy generation module, an execution control module and a knowledge base module. The data acquisition module generates monitoring data stream signals in a unified format; the intelligent analysis module receives the monitoring data stream signal and generates an abnormal state judgment signal and a root cause positioning signal through multi-source data fusion analysis; the strategy generation module generates an operation instruction signal containing a disposal type and an execution parameter; the execution control module receives the operation instruction signal and generates an operation result feedback signal; and the knowledge base module continuously receives and stores associated data of the root cause positioning signal and the operation result feedback signal, and provides a historical case matching signal for the intelligent analysis engine module. According to the operation abnormal state maintenance system and method for the internet platform, the problem of alarm failure caused by high false report and missing report rate of abnormal detection can be solved.

The invention relates to the technical field of concrete curing, in particular to a concrete crack risk prediction and curing system based on thermo-acoustic coupling indexes. According to the scheme, a temperature field and an acoustic emission signal are synchronously acquired through the multi-modal data acquisition module; a thermal diffusion shear index and an acoustic emission energy density are calculated by a multi-modal data fusion module, and are fused into a unified thermo-acoustic coupling factor to quantify a cross-scale risk; the structural constraint compensation module corrects the factor by using pre-stored constraint information to generate a final risk index; the self-adaptive maintenance decision-making module maps a dynamic maintenance strategy according to the indexes; and finally, the maintenance robot executes precise maintenance operation. The technical problems that macroscopic thermal stress and microcosmic damage signals cannot be evaluated in a unified mode, different-source data are difficult to fuse, and high-constraint area risk identification is inaccurate are solved, and continuous prediction and self-adaptive cooperative control of cracks from a potential stage to an initiation stage are achieved. And the accuracy and the intelligent level of early-age crack prevention and control of the concrete are remarkably improved.

The invention discloses a root cause analysis method based on an IT operation and maintenance system, and the method comprises the steps: obtaining the physical position data and real-time environment data of IT equipment, carrying out the matrix construction through a dual topological relation based on the physical position data and the real-time environment data, and obtaining an IT equipment spatial topological matrix containing environmental impact factors. And obtaining operation state data of the IT equipment, performing spatio-temporal conjoint analysis and dimension reduction based on the operation state data and the IT equipment spatial topology matrix to obtain IT equipment dynamic prediction data, and obtaining an IT equipment state dynamic prediction curve according to the IT equipment dynamic prediction data. If the IT equipment state dynamic prediction curve monitors abnormity, a fault propagation path is generated through a reinforcement learning algorithm, and IT equipment fault root causes are determined in combination with a multivariate fault knowledge graph and a causal graph model. According to the method, the problem of early warning lag of the IT operation and maintenance system can be solved, and the problem that root cause positioning has high dependence on artificial experience is solved.

The invention belongs to the technical field of aero-engine maintenance, and discloses an aero-engine intelligent fault diagnosis and maintenance system based on a domain knowledge graph and a large language model. The system comprises four core modules, wherein a data processing module adopts an AemCasRel model to extract a fault entity and relation triple from maintenance data; the knowledge graph management module stores the triple into a Neo4j database, constructs a structured knowledge graph and provides a management interface; the knowledge visualization module realizes visual display and interactive retrieval of the atlas based on D3. Js; the intelligent question and answer module depends on LangChain and a stellar fire big model and combines a multi-hop path reasoning technology to generate an interpretable diagnosis report. According to the system, the whole process integration of fault knowledge from extraction and management to intelligent diagnosis is realized, the precision, efficiency and intelligent level of maintenance diagnosis are remarkably improved, and the system has a wide application prospect in the fields of aviation, aerospace, machinery and the like.

The invention relates to the technical field of equipment dust hazard maintenance, in particular to a hardware tool production equipment intelligent prediction maintenance system which comprises a dust cooperative sensing unit, a dust feature library construction unit, a dust weighted decision unit and a maintenance execution unit. The dust cooperative sensing unit collects dust concentration, a vibration energy peak value and a current fluctuation rate through a three-dimensional gridding sensor group, generates a dust-vibration-current baseline comparison table through self-calibration, associates a mapping relation with the dust cumulative gradient and dust feature library construction unit, and distinguishes an equipment operation stage optimization baseline. The dust weighted decision-making unit divides three levels of dust hazard levels, dynamically adjusts a threshold value and calculates a comprehensive influence index in combination with an equipment type weight, and the maintenance execution unit receives a grading instruction and outputs a maintenance scheme containing process compensation parameters, so that the problems of insufficient correlation of traditional maintenance parameters and one-sided risk assessment are solved; the fault early warning accuracy and the maintenance pertinence are improved, and the production continuity is guaranteed.

The invention relates to the technical field of charging pile operation and maintenance systems, and particularly discloses a charging pile operation unmanned inspection and automatic operation and maintenance system which comprises a data acquisition module, a state initial judgment module, a fault diagnosis module and an automatic operation and maintenance module. An unmanned three-dimensional inspection network is constructed through an unmanned aerial vehicle and an Internet of Things sensor, accurate fault diagnosis is realized in combination with multi-modal feature fusion, a dynamic operation and maintenance priority is generated and a differential strategy is formulated based on a machine learning model, and a complete operation and maintenance closed loop of acquisition, diagnosis, execution, feedback and optimization is formed at the same time. According to the method, the inspection real-time performance and the fault recognition accuracy are greatly improved, the operation and maintenance resource configuration efficiency is optimized, the operation and maintenance cost is reduced, the equipment downtime can be shortened, the charging experience of a user is improved, the operation and maintenance capability can be continuously iteratively optimized through data accumulation, and powerful support is provided for stable and reliable operation of a charging pile network.

The invention discloses an industrial robot predictive maintenance system based on machine learning, and relates to the technical field of industrial robot maintenance. Comprising a data acquisition module, a preprocessing module, a feature engineering module, a hybrid prediction model unit, a meta-learning and domain adaptive module, a federated learning coordination module, a digital twin sample generation module and a dynamic decision engine unit. A model agnostic meta-learning algorithm is utilized to train a cross-brand universal feature extractor, distribution of different brand data is confused in combination with an adversarial field adaptive network, a'data gap 'caused by sensor parameter definition and sampling frequency difference in traditional modeling is broken through, model cooperative training is achieved on the premise that data of all brands are not out of the local, and the modeling efficiency is improved. A global model containing cross-brand fault generality is generated, and the problem of data islands caused by commercial secrecy requirements in an industrial scene is solved.

The invention relates to the technical field of power station operation and maintenance, and discloses a new energy power station intelligent operation and maintenance system based on big data analysis. The system comprises a data acquisition module used for acquiring and preprocessing operation data and maintenance logs of power station equipment; the diagnosis evidence generation module is used for generating diagnosis evidences of three dimensions of performance degradation, mechanical abnormity and repeated fault risk through parallel analysis; the data analysis module maps the diagnosis evidence to a state space to construct an equipment health track, calculates a health index based on a mahalanobis distance and predicts a change trend; the operation and maintenance decision module is used for performing significance verification on the health trend, generating a dynamic risk score, automatically matching a maintenance strategy and establishing a feedback optimization mechanism; according to the invention, accurate evaluation and predictive maintenance of the equipment health state are realized, and the operation and maintenance efficiency and the equipment reliability of the new energy power station are effectively improved.

The invention provides a network information operation and maintenance system based on AI multi-modality, and belongs to the technical field of network information operation and maintenance. Heterogeneous operation and maintenance data such as network flow, equipment state, audio alarm and thermal imaging are acquired through an acquisition unit, and various features are extracted in parallel by using a multi-modality feature extraction module to form a unified multi-dimensional feature vector; a hierarchical anomaly detection architecture is established, lightweight and deep anomaly detection models are deployed on an edge side and a cloud end respectively, different modal features are intelligently fused through an attention mechanism by adopting a multi-modal feature fusion model, and a detection threshold is optimized in real time according to a network state by using an adaptive threshold dynamic adjustment Bayesian algorithm. And a multi-modal fusion decision engine is constructed to perform weighted fusion on the anomaly detection result and dynamically adjust the modal weight, so that the technical problem of insufficient accuracy of multi-modal operation and maintenance data fusion processing is solved.

The invention provides an intelligent fault diagnosis method and system for electromagnetic valve test data and a computer program product, and relates to the technical field of electromagnetic valve fault diagnos.The intelligent fault diagnosis method comprises the steps that firstly, multi-dimensional test data of an electromagnetic valve under multiple working condition combinations are collected, and multiple types of data including action response and the like are contained; a data behavior collaboration model is constructed based on the behavior collaboration relation between the data, and laws such as synchronous response are embodied; an abnormal collaboration chain deviating from a normal collaboration mode is mined from the model, and abnormal performance of each link is determined; reversely deducing a fault core cause through the abnormal collaborative chain, and determining information such as cause types; and finally, generating an executable instruction sequence based on the fault core inducement and the abnormal collaborative chain conduction characteristic, and controlling the maintenance system to execute fault handling operation. Therefore, faults can be comprehensively and accurately diagnosed, and the operation reliability and the system stability of the electromagnetic valve are improved.

The invention relates to the technical field of low-altitude operation and maintenance management, and discloses a digital twinning-based low-altitude takeoff and landing field infrastructure full-process operation and maintenance system, which comprises a real-time digital twinning construction module, a digital twinning construction module, a low-altitude takeoff and landing field infrastructure full-process operation and maintenance module and a digital twinning construction module, an airspace conflict detection module; an intelligent route planning module; a control instruction generation and execution module; and a data driving optimization module. By integrating multi-source data, updating the digital twin model in real time, carrying out airspace conflict detection and optimizing flight path planning, the path and scheduling time slot of the aircraft can be dynamically adjusted, the aircraft efficiency and the resource utilization rate are maximized, meanwhile, real-time feedback and continuous optimization are provided, and the flight path planning efficiency is improved. And the operation and maintenance intelligence and automation level of the low-altitude take-off and landing field is remarkably improved.

The invention belongs to the technical field of new energy intelligent operation and maintenance, and discloses a new energy station unattended intelligent operation and maintenance system based on multi-source data fusion, which integrates various types of sensing terminals such as visible light, infrared, voiceprint and the like through an acquisition module, comprehensively covers scenes such as a wind turbine generator, a booster station, a power transmission line and the like, and improves the operation efficiency. A space-time synchronous calibration and grading preprocessing mechanism is matched, so that the limitation of a single data source of a traditional system is broken; the fusion module adopts an edge and cloud collaboration architecture, fusion strategies can be dynamically switched according to actual requirements, cross-modal semantic alignment can also be realized, and then collection parameters are optimized through a bidirectional feedback mechanism, so that multi-source data form effective complementation; the recognition module constructs a small sample learning algorithm system, and a multi-mode collaborative sample enhancement technology and meta-learning driving adaptation capability are utilized, so that novel defects can be quickly recognized only by a small number of samples.

The invention discloses a hardware equipment anomaly detection and fault prediction method and system, and relates to the technical field of server room operation and maintenance, and the system comprises a data collection module which is used for collecting CPU utilization rate, memory utilization rate, fan rotation speed and mainboard temperature indexes in real time from baseboard management controller (BMC) equipment; the graph causal modeling module is used for modeling the correlation and causal relationship among multiple equipment indexes to form a causal graph structure; the time sequence prediction module is used for predicting the equipment operation state in a period of time in the future through graph convolution and a causal attention mechanism; the anomaly detection module is used for generating an anomaly score and identifying a potential fault based on the deviation between a predicted value and a real value; and the alarm module is used for feeding an abnormal detection result back to the operation and maintenance system to realize early warning and decision support. The abnormal detection accuracy is remarkably improved, faults are predicted in advance, the interpretability of the causal relationship between equipment is provided, and the operation and maintenance risk of a machine room is reduced.

The invention provides a grounding grid intelligent operation and maintenance system based on digital twinning and computer equipment. A multi-factor coupled corrosion kinetic equation is generated based on a laboratory grounding grid steel sheet accelerated corrosion test, and the corrosion kinetic equation is used for predicting the instantaneous corrosion rate of a galvanized steel sheet and estimating the mass corrosion loss of the steel sheet within specified time; combining the corrosion kinetic equation with the simulation model to establish a steel sheet corrosion data set, and training a physical information neural network to construct a corrosion prediction physical model; training the corrosion prediction physical model by adopting a neural network regression algorithm; constructing a grounding grid digital twin model by taking the corrosion prediction physical model as a base and combining field environment monitoring data; defining a Markov decision process in the digital twin environment of the grounding grid; and based on the feedback and reward function of the digital twin model of the grounding grid, training by adopting a near-end strategy optimization algorithm to obtain an intelligent decision-making body, and continuously optimizing the decision-making of the intelligent decision-making body through an online fine tuning function.

The invention discloses an operation and maintenance prediction and self-repairing method, system and device and a storage medium, and the method comprises the following steps: collecting structured, semi-structured and non-structured data, and carrying out the analysis and format conversion of the collected data, and obtaining the preprocessed data; based on the preprocessed data, constructing a dynamic knowledge graph containing entities, relationships, attributes and timestamps; a depth map neural network model is applied to analyze the dynamic knowledge graph, node features are aggregated, key fault nodes are identified, and potential faults are predicted; when the potential fault is predicted, a reinforcement learning algorithm is applied, and a repair strategy is generated according to the operation and maintenance cost, the service influence and the repair duration; and executing the restoration strategy, and feeding back an execution result and performance index data in the restoration process to update the dynamic knowledge graph and depth graph neural network model. Panoramic digital mapping of the operation and maintenance system is realized, and the fault prediction accuracy and the self-repairing success rate are improved.

The invention relates to the technical field of geotechnical engineering, and discloses a soft soil foundation full-life-cycle intelligent electro-osmosis maintenance system which comprises a field control system. And the intelligent monitoring regulation and control system transmits a feedback signal to the power supply system, and the field control system and the spraying humidification system perform corresponding actions, so that active prevention and control of foundation diseases are realized. Dynamic management is carried out on the anode boundary through real-time monitoring data, regular or on-demand starting is carried out through intelligent decision, the physical and mechanical properties of a foundation soil body are dynamically improved through the electroosmosis effect of a field control system, and the durability and long-term stability of the foundation soil body are improved; according to the intelligent electroosmosis maintenance system, the efficiency of an electroosmosis reinforcement system can be improved, the environmental adaptability is enhanced, and long-term stable operation is achieved.

The invention discloses a laser equipment fault prediction and maintenance method based on digital twinning, and the method comprises the following steps: S1, collecting structure parameters, control logic and historical data, and constructing a unified neural network digital twinning model; s2, collecting multi-source operation data in real time in an operation process, and constructing a standardized state data set; s3, mapping the data to a unified neural network digital twin model to realize state synchronization; s4, predicting a future key parameter trend by using an LSTM model; s5, a health score is calculated through an entropy weight method, and the equipment state is quantitatively evaluated; s6, triggering early warning based on the health threshold value, identifying abnormity and generating a maintenance strategy; s7, pushing the strategy to an operation and maintenance system for closed-loop execution; and S8, maintaining feedback for retraining and self-optimizing the unified neural network digital twin model. According to the method, high-fidelity digital twin modeling is realized, so that a prediction-maintenance-feedback-updating intelligent closed-loop system is formed, and the fault prediction accuracy and the maintenance response efficiency of the laser equipment are remarkably improved.

The invention discloses an information operation dimension intelligent management and control platform system and method, electronic equipment and a storage medium, and belongs to the field of intelligent operation and maintenance of an information communication technology. The system comprises an anomaly detection module, a multi-modal feature fusion module and the like, multi-source data is processed through an unstructured data preprocessing unit and a time sequence data alignment unit, model parameters are dynamically adjusted by utilizing a self-adaptive online learning strategy, and dynamic resource scheduling is realized by combining a reinforcement learning algorithm. The method comprises the steps of data receiving preprocessing, feature fusion alignment, model training analysis and the like. The electronic equipment adopts a heterogeneous computing architecture, and the storage medium stores corresponding program codes. According to the method, the problems of weak generalization ability, insufficient unstructured data processing and the like of a traditional platform model are solved, the anomaly detection precision and the response speed are improved, a closed-loop operation and maintenance system is formed, the operation and maintenance decision intelligent level is improved, and the generalization recognition ability and the environmental adaptability of the model to an unknown abnormal mode are remarkably enhanced.