1542 results about "Maintenance system" patented technology

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

The invention discloses a digital twin enabling intelligent pump station preventive operation and maintenance system. Comprising a dynamic twin construction module, a multi-source heterogeneous multi-modal data acquisition module, an edge computing and cloud collaboration module, an equipment health degree evaluation module, a predictive maintenance decision module, a cross-system data fusion module, a self-evolution knowledge graph module, an intelligent diagnosis and early warning module, a self-adaptive maintenance decision module and a man-machine collaboration interaction module. And the dynamic twin construction module comprises a physical-virtual synchronous calibration mechanism and an equipment degradation parameter dynamic updating mechanism. According to the method, the limitation problem of a traditional static model is solved, the method can adapt to nonlinear changes under complex working conditions, the comprehensive judgment and prediction capability of the system on the equipment state can be enhanced, the energy utilization efficiency is improved, the energy consumption is reduced, the decision and verification mechanism is perfected, and the data acquisition and processing problem is improved; the problems of timeliness and flexibility of the model are solved, and the computing architecture and the response capability are optimized.

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 provides an intelligent factory fault diagnosis method and system based on an AI prediction model, and the method comprises the steps: obtaining an equipment monitoring data flow of a target production line of an intelligent factory, carrying out the diagnosis feature construction processing of the equipment monitoring data flow, generating a state evolution feature and a component correlation feature, and carrying out the fault diagnosis of the target production line of the intelligent factory; and inputting the state evolution characteristics and the component association characteristics into a pre-trained fault prediction model for fault prediction, and generating diagnosis result data containing fault risk levels. And the potential fault type and the propagation characteristic information are identified according to the diagnosis result data, and finally the maintenance guidance data containing the fault positioning identifier are generated based on the potential fault type and the propagation characteristic information and are transmitted to the factory operation and maintenance system to trigger the fault intervention operation, so that the accuracy and the maintenance efficiency of intelligent factory fault diagnosis are effectively improved.

The invention relates to the field of industrial robots, and discloses an industrial robot real-time maintenance system and method in combination with edge computing, and the method comprises the steps: obtaining the multi-source operation state data of an industrial robot, and constructing an operation feature data set of key parts of the robot in combination with a state coding mechanism of an edge end and a feature coupling analysis method; carrying out rapid distributed processing on the operation characteristic data set, constructing an equipment health state model based on a lightweight time sequence modeling algorithm, and introducing a multi-dimensional correlation analysis mechanism to carry out incremental learning on the model; judging whether the edge side state recognition result is stable or not based on the change trend of the trigger frequency; according to the corrected state mapping relation, performing response level division on the potential fault trend by applying a multi-scale fault prediction mechanism, and extracting matched maintenance plan parameters; and based on the maintenance scheduling plan, in combination with a preset fault handling knowledge base, performing automatic evaluation and optimization on the current maintenance strategy. The method has the advantage of improving the response speed.

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 relates to the technical field of aircraft intelligent detection and maintenance systems, and discloses an aircraft defect intelligent evaluation system and method based on multi-modal fusion, and the system comprises a multi-modal data collection module, a tensor construction and decomposition module, a meta-prototype relation network module, a multi-target game optimization module, and a closed-loop feedback module. The method comprises the steps of constructing a five-order feature tensor through multi-modal data synchronous acquisition and space-time alignment, extracting low-rank features through hypergraph block item decomposition, dynamically generating a defect prototype set in combination with meta-learning, generating a maintenance decision by adopting a Nash equilibrium strategy and fusing multi-constraint conditions, and optimizing system parameters through closed-loop feedback. The whole-process intelligentization of aircraft defect detection and maintenance is realized; according to the method, high-precision defect detection is realized through multi-modal data fusion and hypergraph modeling, an intelligent decision is generated in combination with dynamic prototype learning and multi-target game optimization, and continuous self-optimization is performed by means of a closed-loop feedback mechanism, so that the operation and maintenance efficiency and safety of the aircraft are improved automatically in the whole process.

The invention discloses an intelligent switch fault monitoring system which comprises a multi-modal data acquisition module, an intelligent feature extraction module, a fault prediction root cause positioning module, an intelligent decision module, a digital twin operation and maintenance module, a security situation awareness module, a model optimization and self-learning module and an enhanced display visualization interaction module. The fault prediction root cause positioning module uses a lightweight hybrid encoder model to efficiently encode the fused features, learns spatial-temporal feature representation of the switch operation state, uses a gradient SHAP algorithm to calculate the contribution degree of each feature to fault occurrence, generates a fault root cause thermodynamic diagram, and sends the fault root cause thermodynamic diagram to the switch operation state; through deep integration of artificial intelligence, digital twinning and augmented reality technologies, a passive mode of traditional switch fault monitoring is thoroughly changed, an intelligent operation and maintenance system with self-sensing, self-decision and self-recovery capabilities is constructed, and all-around guarantee is provided for reliable operation of a data center and network infrastructures.

The invention discloses an intelligent power grid operation and maintenance system based on federated learning and edge calculation, and the system comprises an intelligent electric meter enhancement module which is disposed at a power grid monitoring node and is used for collecting electric energy quality parameters and environment data in real time; the edge calculation layer is used for carrying out data preprocessing, power quality parameter anomaly detection and building a federated learning model; the secure communication framework is used for establishing a hybrid communication network and multi-level security protection, carrying out routing decision and carrying out encrypted transmission on interactive data among the modules; and the end analysis platform is used for integrating the multi-source heterogeneous data of the power grid, predicting the state of the power grid and generating a maintenance plan. According to the invention, comprehensive monitoring and predictive maintenance of the operation state of the power grid are realized.

The invention discloses an intelligent power distribution network fault analysis and maintenance system based on the Internet of Things, which belongs to the technical field of power distribution network fault analysis and maintenance and comprises a multi-source data acquisition preprocessing module, a dynamic digital twin modeling module, an edge cloud fusion analysis module, a transient fault topology positioning module, a self-healing control module and an intelligent maintenance scheduling module. According to the method, fault features are extracted through edge-side AI reasoning, global anomaly detection is realized in combination with cloud big data analysis, anomaly modes under different working conditions are adapted through a dynamic balance coefficient, and fault type tags are quickly generated through weighted fusion of feature vectors in edge-side real-time reasoning, so that the fault detection accuracy is improved. The global anomaly scoring mechanism greatly improves the fault classification accuracy, updates the node state and the probability density function by injecting a dynamic digital twin model, quantifies the distribution characteristics of the faults in space coordinates in combination with the topological weight, the fault influence function and the space attenuation coefficient, and accurately recognizes the fault high-incidence area.

The invention discloses an intelligent operation and maintenance system, method and equipment for steam turbine lubricating oil and a storage medium, and relates to the technical field of industrial digital intelligent operation and maintenance. Zero loss and low delay of data are guaranteed through the data transmission module, and the data fault problem of traditional manual meter reading is solved; static parameters are converted into dynamic trend, residual life and abnormal early warning through a hybrid model of a visual cloud operation and maintenance module, and intelligent conversion from data to decision is realized; and the oil filtering execution module automatically responds to the instruction to complete closed-loop treatment from polluted oil liquid to clean oil liquid without manual intervention, so that real-time monitoring and intelligent analysis of the running state of the steam turbine are realized, an oil filtering system is automatically started when a set threshold value is reached, the oil liquid is ensured to be in a normal state, the maintenance cost is reduced, and the operation and maintenance efficiency is 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 field of computing power center operation and maintenance, in particular to a computing power center multi-robot collaborative operation and maintenance system and method based on a multi-modal large model, and the method comprises the steps: collecting equipment operation data in real time; carrying out data preprocessing on the acquired multi-modal data; the preprocessed single-mode features are input into a multi-mode large model, a unified multi-mode feature vector is generated through an adaptive feature fusion algorithm, the equipment fault type, position and occurrence probability are predicted based on the fused features, and a multi-robot cooperative operation and maintenance strategy is generated; the cooperative control center distributes tasks to the optimal robot combination through a Hungary algorithm according to an operation and maintenance strategy; a reinforcement learning algorithm is adopted to plan a collision-free path for each robot, and real-time dynamic adjustment is carried out to cope with environmental changes; and the operation and maintenance robot executes inspection, fault positioning and basic maintenance tasks, and uploads execution data to the cooperative control center in real time. The complex operation and maintenance requirements of the computing power center are effectively met, and the operation and maintenance efficiency is improved.

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 relates to the field of wind power plants, in particular to a wind power cluster preventive maintenance strategy intelligent generation method and system. The method comprises the following steps: collecting multi-source heterogeneous data of a wind power cluster in real time, including SCADA system operation data, CMS monitoring data and environmental sensor data; performing standardization processing and abnormal value elimination on the collected data, and constructing a wind power equipment operation state database; a wind power equipment health state evaluation model is established based on a deep learning algorithm, the equipment operation state is analyzed in real time, and the potential fault risk is predicted; generating a preventive maintenance strategy by adopting a multi-objective optimization algorithm according to a fault risk prediction result in combination with historical maintenance records of the equipment and operation and maintenance resource scheduling information; and issuing the generated maintenance strategy to a wind power cluster operation and maintenance system to realize intelligent execution of preventive maintenance. The problems of high data fusion difficulty and low processing efficiency in the prior art are solved.

The invention discloses a charging pile remote operation and maintenance system based on the Internet of Things, and the system comprises a data collection and edge calculation module which is used for collecting multi-mode operation data of a charging pile, and generating standardized operation data; the health degree dynamic evaluation module is used for executing multi-modal feature coupling analysis on the standardized operation data; the fault root cause analysis module is used for starting a time-space coupled fault root cause analysis process to obtain a root cause confidence score and a high-risk component; the self-healing strategy execution module is used for generating a self-healing strategy of the charging pile; the resource dynamic scheduling module is used for realizing dynamic optimal distribution of operation and maintenance resources according to the health degree score and the geographic position; the intelligent operation and maintenance management module is used for updating the weight of the dynamic pruning random forest model and generating a hierarchical maintenance report; and the data full-process control module is used for completing automatic closed-loop management from data acquisition to operation and maintenance decision. According to the invention, remote intelligent diagnosis and self-healing operation and maintenance of the charging pile can be realized.

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 provides a core network operation and maintenance system construction method based on a large model and a core network operation and maintenance system. The method comprises the following steps: creating sub-graph structures of a plurality of task execution agents and a graph structure of a main agent; establishing a calling relationship between the graph structure and / or the sub-graph structure and a large language model LLM; compiling the graph structure and the sub-graph structure to obtain a graph instance; constructing a core network operation and maintenance system based on the graph instance; wherein the graph instance is used for defining an operation process of the core network operation and maintenance system, and the operation process at least comprises the following steps: responding to a user instruction by a main agent, calling an LLM to analyze the user instruction so as to identify a task intention, and scheduling a corresponding task execution agent based on the task intention; and the task execution agent responds to the scheduling and calls the LLM to generate a task execution process, and executes the operation and maintenance task based on the task execution process. Therefore, a core network operation and maintenance system can be constructed, intelligentization and automation of core network operation and maintenance are realized, and operation and maintenance efficiency and accuracy are improved.

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 technical field of intelligent power grid management, in particular to a typhoon prediction and power grid maintenance system based on digital twinning and AI cooperation, which comprises a typhoon path evolution module, a power grid vulnerability mapping module, a risk link aggregation module, a maintenance strategy calibration module and a multi-stage maintenance tracking module. According to the method, through dynamic environment parameter extraction and path deviation trend analysis, continuous evolution judgment of a typhoon influence area can be realized, the problem of insufficient change capture of static prediction is solved, fine description and dynamic identification of a vulnerable part are realized, and through node connection relation induction and load intensity partitioning, a typhoon influence area can be identified. A risk link is identified and a potential fault chain is structurally presented; resource configuration and risk priority dynamic comparative analysis are maintained; a deployment logic can be calibrated; a task sequence is ensured to be consistent with a risk level; and the response efficiency and the scheduling accuracy are improved.

The invention relates to the technical field of intelligent monitoring and operation and maintenance of power equipment, and discloses a remote operation and maintenance system and method for power equipment based on a digital twinborn technology, and the system comprises a data collection module, a digital twinborn modeling module, a state monitoring and diagnosis module, and an operation and maintenance decision module. The data acquisition module adopts a distributed grid architecture and optimizes sensor sampling by using a PPO algorithm; the digital twin modeling module fuses RLC voltage and a simplified heat dissipation equation, and model parameters are corrected through scaled Kalman filtering and aging factors; the state monitoring and diagnosis module realizes fault early warning based on multi-threshold detection and a Bayesian network; the operation and maintenance decision module adopts a genetic algorithm to generate an optimization operation and maintenance instruction; the method comprises the steps of adjusting parameters, updating a model, triggering calculation, sending a dimensional instruction and correcting model parameters. According to the invention, full-process intelligent management of power equipment is realized, the operation and maintenance cost is optimized, and stable operation of a power system is guaranteed.

The invention discloses a multi-mode intelligent linkage 3D visual data center operation and maintenance system and method, and relates to the technical field of data center operation and maintenance. The system comprises a multi-modal data fusion module used for mapping physical sensor data and video monitoring and operation and maintenance logs to a unified three-dimensional coordinate system and constructing a multi-modal fusion feature tensor; the three-dimensional particle modeling module is used for constructing a particle space structure based on a Voronoi diagram and Delaunay triangulation and adaptively adjusting the particle resolution; the multi-modal score analysis module is used for calculating a particle multi-dimensional score and generating a semantic heat map to recognize an abnormal region; the structured noise prediction module is used for simulating future responses under different instructions based on a diffusion model; the instruction generation and regulation module is used for realizing causal analysis and instruction optimization in combination with a knowledge graph; and the three-dimensional visual interaction module supports real-time rendering and interaction operation at a client. According to the method, the intelligence, the real-time performance and the visualization level of data center operation and maintenance can be improved.

The invention relates to the technical field of data processing, and provides an intelligent operation and maintenance management method and system based on a cloud platform. Obtaining multi-source heterogeneous production data based on an Internet of Things architecture; based on the timestamp matrix corresponding to the production data, performing calibration processing on the production data through the attention weight matrix to generate a data tensor; extracting a first feature in an entangled state in the data tensor and a second feature representing a single state of the data tensor; performing fault detection on a fusion feature generated by the first feature and the second feature to generate fault probability distribution based on a space attribute corresponding to a data acquisition node and a time attribute corresponding to production data in the Internet of Things architecture; and generating a fault maintenance strategy and a resource allocation strategy according to the fault probability distribution. The fault positioning precision is improved through space-time modeling based on the Internet of Things, the operation and maintenance cost and risk are balanced through dynamic strategy optimization, full-link intelligence from data collection to strategy generation is achieved, and an efficient and accurate intelligent operation and maintenance system is formed.

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 relates to the field of photovoltaic power station operation and maintenance, and discloses an operation and maintenance system for a photovoltaic power station, and the system comprises a data collection module which is used for collecting the equipment state data, environment parameters and power grid information of the photovoltaic power station, and forming multi-source information; the preprocessing module is used for performing space-time alignment, abnormal value filtering and feature extraction on the multi-source information, generating equipment-level health features and constructing a standardized data set based on the equipment-level health features; and the coupling modeling module is used for establishing a multi-equipment collaborative degradation model and outputting equipment collaborative health indexes and residual life prediction based on the standardized data set. Multi-source information is formed by collecting equipment state data, environmental parameters and power grid information of a photovoltaic power station, after space-time alignment, abnormal value filtering and feature extraction, a standardized data set containing equipment-level health features is constructed, and then a coupling modeling module is used for modeling the equipment-level health features on the basis of the standardized data set. And outputting an equipment collaborative health index capable of reflecting the overall degradation state and residual life prediction.

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.