33053results about How to "Reduce maintenance costs" patented technology

The invention provides a power distribution network automatic operation and maintenance method and system based on artificial intelligence, and relates to the technical field of power operation and maintenance, and the method comprises the steps: collecting the operation data of a power distribution network in real time through a distributed sensor network, transmitting the collected operation data to a cloud data center, and designing a multi-agent reinforcement learning framework, a power distribution network is divided into a plurality of sub-regions, and an intelligent operation and maintenance agent makes a personalized fault processing scheme by monitoring and analyzing a digital twin model in real time, and makes an optimal execution plan based on the fault processing scheme in combination with real-time road condition information and the position of maintenance personnel; and issuing the optimal execution plan to the automation equipment and a mobile terminal of a maintainer through a security encryption channel, extracting preventive maintenance rules from historical operation and maintenance records, and making a preventive maintenance plan based on the extracted preventive maintenance rules in combination with a pre-trained equipment life prediction model.

The invention provides a drive control circuit with a fault detection function, and relates to the technical field of fault detection, and the drive control circuit comprises the steps: obtaining historical state parameters, carrying out the time-frequency domain analysis through wavelet transformation, and constructing a heterogeneous neural network for fault judgment; operating parameters are collected in real time, multi-domain feature extraction is carried out, and a fault type is determined based on a dynamic time warping algorithm and fuzzy logic reasoning; and constructing a time sequence prediction network of a multi-layer attention mechanism, outputting a multi-dimensional early warning index in combination with the integrated learning model, and generating hierarchical early warning information. According to the invention, the fault of the driving control circuit can be accurately detected and predicted.

The invention relates to power distribution network fault processing equipment applied to the technical field of power distribution, and aims to improve the fault prediction, diagnosis and processing capabilities of a power system. The equipment comprises a pre-training module, a neural network architecture module, a data acquisition layer, an edge calculation layer, a core processing layer, a context learning module, a reasoning processing module, an output and interaction layer and a continuous learning and improvement layer, and is characterized in that a large-scale pre-trained Transform model is utilized, and professional knowledge of a power system is combined; according to the method, deep understanding and analysis of a complex power grid state are realized, through multi-source data fusion, edge calculation and central cooperative processing, the equipment can monitor the power grid state in real time, accurately predict potential faults and provide intelligent diagnosis and repair suggestions, and model pre-training and optimization are performed by adopting self-supervised learning and federated learning technologies; using reinforcement learning to optimize a fault response strategy; the interpretability of the model is realized, and the decision transparency is improved.

The invention belongs to but not limited to the technical field of fault diagnosis and maintenance of power equipment, and discloses an intelligent diagnosis and maintenance system and method for power equipment based on a knowledge graph. A data acquisition and preprocessing module acquires real-time operation data and historical data of the power equipment; the knowledge graph construction module associates various kinds of information of the power equipment to construct a knowledge graph; the intelligent diagnosis module performs association analysis on power equipment faults based on a knowledge graph and a machine learning algorithm, identifies fault modes and potential faults, and predicts possible faults of the equipment; the maintenance suggestion module automatically generates a detailed maintenance scheme according to the diagnosis result; the operation and maintenance visualization module provides visual fault diagnosis results and maintenance suggestions, supports multi-level data display, and helps operation and maintenance personnel to clearly understand the equipment state. The operation safety of the power equipment is improved, the operation and maintenance cost is remarkably reduced, and the overall efficiency of equipment management in an intelligent power grid environment is improved.

The invention relates to a business credit evaluation and supervision method based on a multi-modal coevolution algorithm, and the method comprises the steps: collecting and cleaning multi-modal data, guaranteeing the data quality, uniformly storing the multi-modal data as a structural database, extracting features through cross-modal comparative learning, and enabling the features to be embedded into a dynamic multi-modal knowledge graph, a modal sharing network is used to extract and fuse modal sharing and specific features, consistency of modal feature distribution is optimized through adversarial learning, a global co-evolution credit scoring model is initialized, a dynamic multi-objective algorithm is used to optimize accuracy, robustness and interpretability of the model, and a credit scoring result is obtained. A causal reasoning model is introduced to analyze driving factors of enterprise credit changes, a causal relationship is established, a closed-loop feedback system is designed, a credit evaluation model is updated, adaptability is enhanced, an interpretable credit evaluation report is generated based on a generative adversarial network, and transparency and credibility are enhanced. According to the method, the accuracy and adaptability of credit assessment are improved, and the robustness and interpretability of an assessment result are ensured.

The invention provides a coal mill fault diagnosis and prediction method and system based on big data analysis, and relates to the technical field of big data analysis, and the method comprises the steps: collecting a vibration signal of a coal mill bearing through a vibration sensor, carrying out the multi-scale analysis through wavelet decomposition, extracting features, constructing a deep convolutional neural network model, and carrying out the fault diagnosis and prediction of a coal mill bearing. A maximum mean value difference adaptive layer is introduced to carry out feature alignment on data of a source domain and a target domain, identification of fault types and degrees is realized, a health state evaluation index including a fault severity coefficient and a development trend coefficient is established, and the remaining service life is calculated and early warning is carried out. According to the invention, the accuracy and generalization ability of fault diagnosis are improved, accurate prediction and timely early warning of the bearing fault of the coal mill are realized, and the service life of equipment is prolonged.

The invention relates to the technical field of power systems, in particular to a substation main equipment self-checking system based on a multi-source heterogeneous data fusion technology. The self-inspection system comprises a data acquisition and preprocessing module, a self-adaptive data fusion algorithm module, an equipment fault mode dynamic identification and intelligent repair module, a multi-scale time sequence prediction and emergency plan generation module, an equipment health state evaluation and residual life prediction module, and a fault diagnosis and knowledge base updating module. A user interaction and remote monitoring management module, and an intelligent prediction and optimization scheduling module; according to the substation main equipment self-checking system based on the multi-source heterogeneous data fusion technology, comprehensive monitoring and intelligent management of substation equipment are achieved by integrating various data acquisition and processing technologies.

The invention provides a circuit board fault diagnosis method and system based on artificial intelligence, and relates to the technical field of circuit board fault diagnosis, and the method comprises the steps: constructing a three-dimensional electromagnetic field simulation model of a circuit board, setting a plurality of virtual sensing probes in the three-dimensional electromagnetic field simulation model, and generating an electromagnetic field distortion distribution diagram; dividing a fault sensitive area of the circuit board based on the electromagnetic field distortion distribution diagram, outputting a performance degradation rate of each functional area of the circuit board, and generating a fault risk assessment matrix according to the performance degradation rate; performing function reconstruction on the circuit board according to the fault risk assessment matrix, establishing a dynamic fault-tolerant mechanism of the circuit board, generating a task load distribution strategy based on the dynamic fault-tolerant mechanism, and deploying the task load distribution strategy to a field programmable gate array of the circuit board. And self-repairing the circuit board by adjusting the logic structure of the field programmable gate array, verifying the repairing effect and outputting a diagnosis report.

The invention provides a photovoltaic station intelligent operation and maintenance simulation method and system based on digital twinning, and relates to the technical field of photovoltaic station operation and maintenance, and the method comprises the steps: constructing a digital twinning hierarchical structure through multi-modal detection signals through discrete wavelet packet transformation, tensor decomposition and a graph neural network, and determining structured data; extracting fault features by using a dynamic neural network, predicting a fault probability based on a Markov model, and triggering maintenance early warning; inputting the early warning signal into a maintenance strategy model based on multi-agent reinforcement learning, and generating a candidate maintenance time window set; the maintenance scheduling module optimizes sorting to obtain an optimal time window sequence, and the resource allocation module generates a personnel material allocation scheme by adopting a genetic algorithm; and iteratively optimizing the strategy parameters based on the maintenance cost, and outputting a final operation and maintenance implementation scheme.

The invention relates to the technical field of data processing, and discloses a road condition monitoring recognition method, device and equipment based on machine vision and a medium. The method comprises the following steps: carrying out multi-source data acquisition on a road environment to obtain an original data set; performing dynamic time warping and multi-modal feature fusion processing on the original data set to obtain a spatial-temporal feature tensor; performing multi-scale pyramid decomposition and depth feature extraction on the spatial-temporal feature tensor to obtain a road condition feature vector; semantic analysis is carried out on the road condition feature vectors through feature mapping and knowledge graph enhancement, and a road condition attribute set is obtained; performing multi-level risk quantification and space-time propagation simulation on the road condition attribute set to obtain a graded early warning index and a risk distribution map; and performing multi-target tradeoff and causal chain reasoning on the graded early warning indexes and the risk distribution map to obtain a self-adaptive traffic regulation and control instruction set. According to the invention, the efficiency and accuracy of road condition monitoring and identification based on machine vision are improved.

The invention provides a semiconductor equipment fault prediction and maintenance method and system based on data fusion, and relates to the technical field of semiconductor equipment fault prediction and maintenance, and the method comprises the steps: collecting the data of a multi-source sensor, constructing a process-stress-material multi-dimensional state model, and calculating the failure probability of a key part. And when the failure probability exceeds a threshold value, a fault diagnosis process is triggered, fault mode identification is performed based on a digital twin model, and a fault development trend is predicted in combination with historical data. And finally, constructing a maintenance objective function, determining an optimal maintenance scheme by using an adaptive optimization algorithm and a maintenance decision rule base, and carrying out maintenance guidance. According to the method, the maintenance knowledge graph is constructed, so that continuous optimization of fault prediction and maintenance capability is realized, and the reliability and production efficiency of semiconductor equipment are improved.

The invention relates to the technical field of ore conveying, and discloses an intelligent mine ore conveying management system, which comprises a deep reinforcement learning scheduling module used for realizing optimal scheduling through a deep reinforcement learning algorithm based on an ore conveying equipment state, a conveying path condition and ore queue information; the digital twinning system is used for creating a digital twinning model of the conveying equipment and carrying out multi-physics field simulation, real-time data synchronization and state prediction so as to manage the full life cycle of the equipment; and the edge calculation module is used for carrying out data acquisition, real-time processing and lightweight AI reasoning on a mine site so as to realize equipment anomaly detection and early warning. By introducing deep reinforcement learning scheduling, digital twinning, edge computing and block chain data management technologies, adaptive optimization scheduling, accurate state monitoring and fault prediction of the ore conveying system are realized, an efficient safety protection and intelligent user interface is provided, and the operation efficiency, reliability and safety of the system are improved.

The invention discloses an intelligent adjusting system of an adaptive multi-environment ship electric control valve actuator, which relates to the technical field of control valve adjustment and comprises an environment judgment module, a real-time monitoring module, an adaptive adjusting module and a feedback optimization module. The environment judgment module collects environment data through various sensors distributed on an electric control valve and around the electric control valve, and the environment data are processed through a signal conditioning and microprocessor application algorithm. According to the intelligent adjusting system of the self-adaptive multi-environment ship electric control valve actuator, the adaptability and the stability of the ship electric control valve actuator in variable environments are remarkably improved; the electric control valve actuator can monitor and respond to environment changes such as temperature, humidity, vibration and electromagnetic interference in real time, it is ensured that the electric control valve actuator can be accurately regulated and controlled under the extreme working condition, and therefore the reliability and safety of a ship system are improved.

The invention provides a prediction model construction method and system for personalized equipment operation and maintenance, and relates to the technical field of data processing, and the method comprises the steps: collecting equipment operation data and environment data, extracting the change characteristics of the equipment operation state, carrying out the correlation analysis of the environment data based on historical equipment fault records, and obtaining a prediction model. Performing normalization processing, performing time sequence analysis, generating a joint feature matrix, segmenting the joint feature matrix into time slices of a fixed window according to a time sequence, classifying the time slices into a normal state and an abnormal state, performing feature contribution degree analysis, generating a feature weight matrix, and performing feature extraction; re-combining the equipment features in the time slice feature data set with the environment factors according to feature weights, generating fusion feature data layer by layer, performing supervised learning training on the fusion feature data, constructing an equipment prediction model, and outputting the equipment prediction model as a prediction model of personalized equipment operation and maintenance; according to the invention, the operation and maintenance effect of personalized equipment is improved.

The invention provides a predictive maintenance management method and system for an industrial robot, and relates to the technical field of equipment maintenance management, and the method comprises the steps: obtaining multi-modal heterogeneous data of the industrial robot, carrying out the intelligent preprocessing, obtaining first preprocessing data, carrying out the cross-modal correlation fusion of the first preprocessing data, generating fusion data, and carrying out the prediction of the industrial robot; extracting complementary redundant information and constructing a semantic feature map; constructing a health state prediction model, adding the semantic feature map into the health state prediction model, mapping the semantic feature map to an embedding space through linear transformation, extracting time sequence information, carrying out feature interaction to obtain interaction features, and carrying out transformation and feature enhancement through a feedforward neural network layer to obtain health state probability distribution; comprehensive health state distribution is generated through Bayesian reasoning fusion, an optimal maintenance strategy is generated in combination with a detection model, an abnormal mode and a key turning point, collaborative maintenance is conducted on the industrial robot, a maintenance result is uploaded to a server, and the current state is stored.

The invention discloses a wind driven generator state remote monitoring system based on the Internet of Things, and relates to the technical field of wind power generation equipment monitoring, and the system comprises a sensing module, a transmission module, a remote processing module and a user terminal module. The sensing module is used for accurately collecting various physical parameters such as vibration, temperature, pressure and rotating speed during operation of the wind driven generator by using various sensors, so that the operation condition of equipment is comprehensively mastered, and a data basis is provided for subsequent analysis and processing; according to the method, intelligent prediction of the future operation state of the wind driven generator is realized through deep learning and time sequence analysis, the fault type and time possibly occurring to the equipment can be predicted based on historical data and real-time data, powerful support is provided for operation and maintenance decision making, an operation and maintenance team can prepare in advance, and the operation and maintenance efficiency is improved. And targeted maintenance measures are taken, so that the maintenance cost is reduced, and the operation efficiency and the safety of the equipment are improved.

The invention discloses an intelligent management method and system for IT equipment asset maintenance, and relates to the technical field of intelligent management. During system operation, multi-dimensional parameters of IT equipment are collected in real time and transmitted to a data processing module, the collected data are preprocessed, the current state of the equipment is comprehensively analyzed, and the health state coefficient Hstate of the equipment is calculated; the method comprises the following steps: acquiring a current equipment operation state, performing baseline establishment on historical operation data of the equipment, comparing the current state with the historical data, analyzing the performance trend and change of the equipment, calculating to obtain a trend analysis coefficient Ttend, and identifying potential problems and performance reduction trend of the equipment through long-term data accumulation. And calculating a final fault prediction probability index Pfault, comparing the final fault prediction probability index Pfault with a set threshold value, obtaining a grade prediction scheme, generating early warning information, prompting a system administrator to sort and distribute according to the urgency and priority of tasks, automatically generating a comprehensive report by the system, and providing decision support for the administrator.

The invention discloses a multi-working-condition-oriented equipment fault diagnosis method based on unbalanced learning, and the method comprises the following steps: collecting the operation data of equipment under different working conditions through a plurality of sensors, carrying out the preprocessing, feature extraction and feature selection of the collected original operation data, and obtaining a feature set; processing the feature set by adopting an unbalanced learning technology and a cost-sensitive learning algorithm to obtain a balanced data set; constructing a multi-working-condition fault diagnosis model based on learning, and optimizing model parameters by adopting a method of combining cross validation and grid search according to a balanced data set in a model training process; deploying a multi-working-condition fault diagnosis model obtained through training, carrying out the online diagnosis of equipment faults, and continuously updating and optimizing the model through a continuous learning mechanism. According to the method, the unbalanced learning technology is applied to multi-working-condition fault diagnosis, the accuracy and robustness of diagnosis can be remarkably improved, and real-time monitoring and predictive maintenance of the equipment health state are achieved.

The invention discloses a smart home energy management optimization method in an Internet of Things environment. According to the method, the Internet of Things technology is utilized, intelligent identification and classified management are carried out on equipment, multiple energy management strategies are preset, and a user can conveniently and autonomously adjust; energy data is monitored in real time through a sensor and an intelligent electric meter, an energy consumption rule is mined by using a big data analysis technology, and a demand trend is predicted; constructing an intelligent energy dispatching center, and formulating a dynamic energy distribution scheme by using an optimization algorithm; a mobile phone application and an intelligent control panel are developed, and a user is supported to check data, control equipment and carry out personalized customization; and meanwhile, equipment firmware upgrading and remote management are realized. According to the invention, energy consumption can be reduced, cost can be controlled, equipment collaboration and energy distribution efficiency can be improved, user experience can be enhanced, system maintenance and upgrading are facilitated, and intelligent household energy management is more efficient, intelligent and flexible so as to adapt to diversified household requirements.

The invention discloses an industrial equipment health monitoring and fault early warning system based on edge computing, belongs to the technical field of edge computing, and aims to solve the technical problem of how to efficiently realize health monitoring and fault early warning of equipment in real time and identify different types of faults. Each edge computing node is used for taking local data as input, predicting a fault category and a probability value corresponding to the fault category through a local model, outputting the fault category and the probability value as a prediction result, encrypting the local data and the prediction result through the cloud edge collaboration module and then sending the local data and the prediction result to the central server layer, the fusion model comprises three fault diagnosis models, namely a first fault model constructed based on LSTM, a second fault analysis model constructed based on a rule expert model and a third fault analysis model constructed based on a physical model; and the central server layer is used for fusing prediction results based on a self-adaptive weight mechanism.

The invention relates to the technical field of electrochemical energy storage, in particular to an intelligent management system for electrochemical energy storage application, which comprises a state real-time monitoring module, a dynamic regulation control module, a fault diagnosis and analysis module, an emergency response module, a health monitoring and analysis module and a charging and discharging strategy module. According to the invention, by monitoring key performance parameters such as voltage, current and temperature of the electrochemical energy storage equipment in real time and combining data of the environment monitoring sensor, external and internal changes can be responded in real time, the response speed and preventive maintenance capability of the equipment are improved, and upcoming load requirements are met by adjusting discharge parameters in time; according to the invention, the device can be used for preventing overdischarge and potential equipment damage, effectively prolonging the service life of the battery, optimizing the energy consumption, adjusting the maintenance period according to real-time data, reducing the risk of sudden failure, improving the stability and safety of the electrochemical energy storage equipment in a complex environment, and supporting the stable operation of the equipment.

The invention belongs to the technical field of power system monitoring, and particularly relates to a wireless cable temperature measurement system and method, and the system comprises an operation information collection module, a layout planning analysis module, a data standardization module, a change trend analysis module, an early warning threshold adjustment module, and a fault positioning analysis module. According to the invention, cable operation data and environment information are analyzed, the coverage range, data transmission delay and signal attenuation are considered, the layout of temperature sensors is optimized, the response speed and data accuracy of a temperature sensor network are enhanced, temperature data are formatted and processed, time sequence analysis is carried out, and the temperature change trend is evaluated. Abnormal temperature risk recognition is achieved in the early stage, the accuracy of fault prediction is improved, the cable temperature risk monitoring threshold value is adjusted in combination with cable specifications and meteorological information, the timeliness and effectiveness of risk management are improved, the fault response speed and the system stability are improved, the maintenance cost is reduced, and the reliability and safety of a power grid are improved.

The invention discloses a coal mining intelligent scheduling method based on an artificial intelligence platform. The method comprises the following steps: S1, constructing a data set; s2, preprocessing the data set; s3, analyzing the preprocessed data set to generate an analysis result; s4, based on an analysis result, constructing and training a prediction model by adopting a variational auto-encoder and a graph neural network; s5, multi-agent deep reinforcement learning is adopted, a hybrid expert model and a distributed depth deterministic strategy gradient are combined, and an optimization model is constructed and trained; s6, predicting the fault of the equipment by using the prediction model, generating a prediction result, performing early warning in advance, and taking measures; s7, dynamically adjusting a production plan by using the optimization model according to the prediction result and the real-time data, performing resource scheduling, and optimizing resource allocation; and S8, establishing a feedback mechanism, collecting feedback information, and optimizing and improving a resource scheduling strategy. According to the invention, efficient intelligent scheduling is realized by using big data analysis, deep learning and a multi-agent system.

The invention discloses a data acquisition and processing method based on an SF6 pressure gauge of a transformer substation, and relates to the technical field of data processing, and the method comprises the steps: collecting the environment data of the transformer substation in real time through an SF6 pressure sensor, a temperature sensor and a humidity sensor; inputting the substation environment data into a multi-sensor fusion algorithm, performing temperature compensation and humidity correction, and generating a corrected multi-dimensional data set; inputting the extracted key features into an artificial intelligence model for fault prediction, and outputting a fault early warning signal; and according to the fault early warning signal, an alarm mechanism is triggered and operation and maintenance personnel are notified, and an actual fault processing result is fed back to the central processing platform. Through a multi-sensor fusion technology and a self-adaptive filtering algorithm, the problem that SF6 pressure data in the prior art is easily influenced by environment temperature and humidity is effectively solved, and the pressure monitoring precision is remarkably improved.

The invention provides a Docker Registry management optimization mode. The Docker Registry management optimization mode comprises the steps that based on Docker Registry management requirements, a Docker Registry native management mode corresponding to the management requirements is determined; based on the management requirements, a corresponding optimization management mode is determined; optimization is conducted on the native management mode through the optimization management mode, and corresponding Docker Registry management operation is conducted through an optimized operational order. According to the embodiment, the problems that the native Docker Registry control operational mode is unfriendly, management operation is complex, and the network operation and maintenance cost is too high are solved. For example, storage catalogs of mirrored documents are changed to cluster-type share mirrored storage equipment from a local storage area, namely, single-machine mirrored storage is changed to cluster-type mirrored storage, and on the condition of large-scale cluster-type machinery equipment, the mirrored document maintenance cost can be greatly lowered. For another example, by means of the mode that multiple Registry service operation is simplified, the operating difficulty of Docker Registry management is lowered, and the network maintenance operational experience is improved.

The invention discloses a mobile energy storage power supply health state intelligent assessment method and system, and the method comprises the steps: inputting the multi-parameter monitoring data of a power supply into a battery physical model for preliminary assessment, and obtaining a first assessment value; performing feature extraction and feature selection on the obtained monitoring data and environmental data, and inputting an obtained principal component matrix into a health state evaluation model for prediction to obtain a second evaluation value; comparing the first evaluation value with the second evaluation value, and adjusting parameters of the battery physical model by using the health state evaluation model to obtain an optimized battery physical model; obtaining a third evaluation value based on the optimized battery physical model; and obtaining a final battery health state evaluation result based on the third evaluation value and the second evaluation value. By collecting various monitoring data and environmental data in real time, the health state of the battery is comprehensively known, so that the evaluation accuracy is improved; and the established physical model and the health state evaluation model are combined, so that the precision and robustness of the evaluation result are improved.

The invention provides an electric power multi-modal data perception and digital twinborn simulation cooperative interaction method and system, and relates to the field of electric power equipment management and maintenance, and the method comprises the steps: carrying out the electric power multi-modal data perception of data collected by edge equipment, and obtaining the state characteristics of single equipment; constructing a digital twinborn model based on the state features of all devices in the to-be-monitored power system; constructing a power equipment graph knowledge base by using a digital twinborn model, and extracting global state features of the equipment from the power equipment graph knowledge base through a graph neural network so as to construct a global state feature time sequence of the equipment; inputting the global state feature time sequence into the trained time sequence prediction model, and predicting the equipment state at the future moment; according to the multi-modal data, parameters of the digital twin model are adjusted in real time, environmental changes are flexibly adapted, time sequence modeling and fault trend analysis functions are combined, the running state of the equipment is predicted, and the comprehensiveness and foresight of equipment monitoring are further improved.

The invention discloses an intelligent textile production line control method and system, and relates to the technical field of industrial automation, and the method comprises the steps: collecting operation data in a textile production process in real time, and carrying out the data preprocessing; dynamically adjusting process parameters of the textile production line based on the preprocessed data; detecting the quality state in the textile production process in real time, and adjusting the operation of the production line based on the detection result; constructing a multi-feature fusion fault prediction model, and predicting the operation state of the production line equipment; potential faults are recognized in advance according to prediction results, and the operation mode of the production line is adjusted. The method combines multi-feature fusion analysis, dynamically optimizes the equipment operation state, identifies and adjusts potential faults in advance, improves the production quality and efficiency, reduces the maintenance cost and the shutdown risk, is significantly superior to a traditional method, and provides powerful technical support for intelligent upgrading of the textile industry.

The invention discloses a method and a system for realizing data synchronization between heterogeneous databases based on logs. The method comprises the following steps: acquiring connection information of a source database and a target database; querying a table structure of the source end database and generating an SQL statement to establish a data mapping rule; matching a corresponding rule model from a mapping rule model library; if the matching is not successful, the system abnormally quits and prompts the user to create a new mapping model and add the new mapping model to the mapping rule model base; converting a table structure of the source end database into a JSON character string based on a standard field type; an SQL statement required by the target database is output through the data mapping engine module; the message monitoring program reads the data in the temporary storage area and converts the data into data meeting the requirements of the target database; formatting the converted data into SQL statements supported by a target database; and sending the generated SQL statement to a target database to complete data migration. Accurate and controllable synchronization of the data and the model in a heterogeneous environment is realized.

The invention relates to the technical field of power system monitoring, in particular to an intelligent direct-current power supply whole-course fault recording analysis system which comprises a dynamic threshold self-adaption module, a multi-dimensional data fusion module, an event triggering module, a long-period data accumulation module, a complex boundary condition monitoring module and a fault mode recognition early warning module. Wherein the dynamic threshold adaptive module is used for collecting multi-dimensional data of a direct current power supply system in real time and dynamically adjusting a threshold range of fault detection; the multi-dimensional data fusion module is used for generating a comprehensive system operation state evaluation result; and the event triggering module is used for triggering a fault recording process when an abnormal condition meeting a threshold range condition is detected. According to the invention, through real-time data monitoring, deep learning and fault mode identification and long-term data accumulation, the fault early warning capability and the operation and maintenance efficiency of the DC power supply system are significantly improved, and stable and reliable operation of the system is ensured.