265 results about "Fault prevention" patented technology

Disclosed in the present invention are a fault identification method and system based on the operating condition of a continuous system in an open-pit mine. The method comprises: establishing a simulation model for a continuous system in an open-pit mine, and monitoring device data in real time; pre-processing the data, and performing potential fault identification; on the basis of a system pressure change rate and an adaptive adjustment mechanism of the continuous system in the open-pit mine, optimizing fault identification output; and designing a fault response and real-time adjustment mechanism to prevent fault occurrence. The fault identification method and system based on the operating condition of a continuous system in an open-pit mine provided in the present invention improve the speed and accuracy of fault diagnosis, particularly the rapid processing capability for complex data relationships. A breakthrough is achieved in fault prevention, thus enabling early warning and adaptive adjustment to be implemented before faults occur. Thus, the stability and safety of continuous systems in open-pit mines are significantly improved, and a more efficient technical solution is provided for operation management of modern open-pit mines.

The invention provides an intelligent control method and system for fault positioning and rapid reliability recovery of a power distribution network, and relates to the technical field of power grids, and the method comprises the steps: collecting real-time data of each node of the power distribution network, inputting the real-time data into a pre-trained deep learning model, rapidly recognizing the fault type, and accurately positioning a fault point. Generating a preliminary fault assessment report; based on the generated preliminary fault assessment report, starting a multi-objective optimization algorithm, simulating various possible fault isolation and power grid reconstruction schemes by using a reinforcement learning technology, and selecting an optimal fault isolation and power grid reconstruction scheme; after fault isolation and power grid reconstruction are completed, the system recovery state is continuously monitored, detailed event reports and optimization suggestions are generated, and data support is provided for future fault prevention and system improvement.

The invention discloses an intelligent linkage control management system based on air conditioner application, and relates to the technical field of intelligent control and energy management, and the system comprises a load prediction module which obtains an air conditioner load demand prediction value through an intelligent load prediction model based on preprocessed environment energy consumption data; the optimization scheduling module is used for optimizing the current air conditioner operation mode through the building energy management system based on the air conditioner load demand prediction value, and obtaining an optimized air conditioner operation scheduling instruction; the intelligent adjusting module is used for intelligently adjusting the operation parameters of the air conditioner system based on the optimized air conditioner operation scheduling instruction, adaptively adjusting the energy efficiency of the air conditioner and obtaining operation feedback data; and the fault management module is used for identifying potential fault risks of the air conditioner and formulating fault risk prevention measures through a time sequence analysis method based on the operation feedback data. According to the invention, through innovation of an intelligent load prediction and fault prevention mechanism, the energy utilization efficiency and the system stability are effectively improved.

The invention relates to the technical field of storage equipment control, and discloses a dynamic cooperative control method and system for digital twin-driven storage equipment. The method comprises the following steps: firstly, constructing a storage equipment digital twin model which covers a storage space three-dimensional model, equipment motion parameters and a real-time operation state; and then collecting sensor data in real time, and generating a collaborative operation path through a dynamic path planning algorithm containing conflict detection and priority allocation. And correcting the equipment motion track and updating the model state by using the space-time consistency verification model, finally inputting the corrected track into an equipment control instruction generation module, and outputting a driving signal to control the storage equipment. The system comprises a digital twinborn model construction module, a sensor data acquisition module, a collaborative operation path generation module, a motion trail correction module, a control instruction generation module, a driving module and the like. The storage space utilization efficiency and the equipment collaborative operation capability are improved, real-time monitoring and fault prevention are achieved, and intelligent upgrading of a storage system is promoted.

The invention relates to the technical field of program control systems, in particular to an intelligent daily chemical production line online control system. The system comprises an equipment digital twin modeling module, a process parameter optimization module, a modular production unit adjustment module, an automatic scheduling and Internet of Things transmission module, an equipment monitoring and data analysis module and a fault prediction and remote operation and maintenance module. Acquiring equipment real-time operation state data of the intelligent daily chemical production line, and performing digital twinborn modeling based on the equipment real-time operation state data to obtain an equipment digital twinborn model; and performing simulation optimization calculation on each process parameter based on the digital twin model of the equipment to obtain stable-state process parameter set data of the equipment. Through technological parameter optimization, modular production unit adjustment, equipment monitoring and fault prediction, efficient dynamic optimization of the production process is realized, the flexibility, stability and fault prevention capability of the production line are improved, the production cost is reduced, and the production efficiency is ensured.

The invention relates to the technical field of machine tool calibration, and discloses a calibration method and device for precisely manufacturing a multi-axis linkage machine tool, and the method comprises the steps: collecting machine tool operation data and multi-modal sensor data of the multi-axis linkage machine tool, and carrying out the error prediction, and obtaining the error prediction data; the positions of all shafts of the machine tool are measured, and nanoscale resolution ratio position data are obtained; real-time state representation of the machine tool is obtained through the multi-branch deep neural network; constructing a multi-level hypergraph based on the real-time state representation of the machine tool, and carrying out feature processing to obtain a fault feature vector; reinforcement learning is carried out through a deep Q network to obtain an adaptive compensation strategy; linkage compensation values of all axes are generated on the basis of the self-adaptive compensation strategy, real-time adjustment and fault prevention analysis are conducted on the multi-axis linkage machine tool, a fault prevention control scheme is generated, the precision maintaining capacity and operation reliability of the multi-axis linkage machine tool are improved, and the method can be suitable for different types of multi-axis linkage machine tools.

The invention relates to the technical field of data processing, and discloses a power grid fault early warning system based on machine learning and a related device, and the system is characterized in that an initial monitoring module identifies and identifies abnormal data points through monitoring the voltage, current and load of a power grid, and obtains an abnormal mode identification result; the load fluctuation analysis module positions the abnormal node according to the abnormal mode identification result to obtain abnormal node positioning information; the trend analysis and prediction module evaluates and predicts fault nodes and time according to the abnormal node positioning information to obtain a risk prediction result; the strategy formulation and response module formulates an operation instruction based on the risk prediction result to obtain a fault prevention measure; the fault simulation and test module performs virtual fault simulation by adopting fault prevention measures, and performs iterative optimization on a strategy to obtain a test evaluation result; and the fault early warning and optimization module optimizes a fault prevention strategy according to a test evaluation result, and a more accurate target fault early warning and a faster and more effective target response strategy can be obtained.

Disclosed is a digital twin-based fault diagnosis method for logistics mobile equipment, capable of achieving online monitoring of equipment state and fault diagnosis by means of fusion of digital twin technology and sensor data, thereby allowing for proactive fault prevention and precise maintenance, improving the reliability and operation efficiency of logistics equipment and prolonging the service life thereof. The technical solution of the method comprises: step 1, on the basis of logistics mobile equipment, establishing a corresponding digital twin model; step 2, acquiring real-time data of a battery entity for adaptive model updating; step 3, inputting fault information into a bidirectional GRU model having a multi-head attention mechanism; step 4, performing outlier detection on the basis of an improved fuzzy isolation forest algorithm, comprising introducing a type-2 fuzzy theory into a fuzzy isolation forest algorithm and making improvements; and step 5, using the obtained fault data to implement corrective measures on the basis of risk levels.

The present invention relates to the technical field of low-voltage electrical appliances, and discloses an intelligent safety protection and fault prevention method for low-voltage electrical appliances, including: obtaining the working state parameters of the target low-voltage electrical appliance, performing filtering processing, obtaining accurate working state parameters, inputting the accurate working state parameters into a fault analysis model, obtaining a fault analysis result, and if the fault analysis result is an operating fault, generating a safety protection scheme based on the fault type and the working state parameters. By implementing the present invention, a variety of protection strategies are adopted to comprehensively reduce the risk of functional failure caused by faults of intelligent low-voltage electrical appliances, extend the normal operation time of the equipment, and solve the problem in the related technology that the existing protection methods are difficult to effectively monitor and prevent these emerging faults due to the unique fault modes caused by the integration of multiple advanced modules in intelligent low-voltage electrical appliances.

The invention discloses a cross-regional network fault prevention and repair method, system and device and a medium, and the method specifically comprises the steps: collecting the multi-modal data of a cross-regional network according to distributed monitoring nodes, and generating a multi-modal AI model through the training of the multi-modal data based on a quantum computing platform; deploying the multi-modal AI model to each edge node of a cross-regional network, and performing fault detection on each edge node according to the multi-modal AI model to obtain a fault detection result; according to the fault detection result and the real-time network state of the cross-regional network, dynamically adjusting a traffic scheduling strategy of the cross-regional network through a reinforcement learning algorithm; and determining a fault self-healing strategy according to the fault detection result, and executing a fault self-healing operation on the cross-regional network through the fault self-healing strategy. According to the method, the technical bottleneck faced by a cross-regional network fault prediction and self-healing system is effectively solved, and the real-time performance, the accuracy and the self-adaptive capability of the system are improved.

The invention discloses an efficient and energy-saving type intelligent management and control system and method for an integrated cold station, and particularly relates to the technical field of intelligent management and control of cold stations. A plurality of sensors are arranged in a cold station system, the operation state of each component and the external environment change are monitored in real time, historical data and external environment information are combined, and a load fluctuation prediction model is established by adopting a long-short-term memory network, so that the load demand is accurately predicted, and the operation parameters of each component of the system are adjusted in advance; meanwhile, on the basis of real-time monitoring data and load prediction deviation, dynamic optimization is conducted on the cold station system through fuzzy logic, and therefore the equipment response speed and the energy-saving efficiency are balanced; abnormal detection is carried out on the system through a machine learning model, potential fault risks are found in time, and early warning is given out; the system can quickly and accurately adjust the operation state when the load fluctuates, the stability, the energy efficiency and the fault prevention capability of the cold station system are improved, and the overall operation efficiency and the energy-saving effect are remarkably improved.

The invention relates to the technical field of equipment monitoring and management, and particularly discloses an AI oT-based distributed industrial equipment monitoring and management system and method, and the system comprises a distributed equipment monitoring module which collects operation parameters, performance indexes and environment data in real time through a plurality of types of sensors disposed on industrial equipment, a dynamic hierarchical edge architecture is utilized to process data and evaluate a static health state, and task intelligent layering and cloud collaboration are supported. And the multi-modal perception analysis module generates an equipment association graph based on the collaborative task, extracts multi-modal features by using a cross-node collaborative algorithm, and realizes dynamic health assessment by combining a CNN-LSTM fusion model and equipment implicit dependency relationship analysis. And the distributed equipment management module fuses equipment dynamic and static health data, cooperates with a task association graph and a fault tracing model, and realizes efficient resource configuration and fault prevention. The system guarantees intelligent monitoring and management of the whole life cycle of industrial equipment through a cooperation mechanism of edge intelligence and cloud deep analysis.

The invention relates to a water conservancy facility operation and maintenance method based on multi-source data, and the method comprises the steps: collecting multi-source heterogeneous data of a water conservancy facility, and obtaining a standardized multi-dimensional data matrix; multi-modal features of the standardized multi-dimensional data matrix are extracted through a graph neural network fusion algorithm, and a fusion feature vector is obtained; performing fault diagnosis on the fusion feature vector through a knowledge graph reasoning algorithm automatically constructed by a domain ontology to obtain fault diagnosis information; based on the fault diagnosis information, performing facility state evolution analysis through a space-time coupling degradation prediction model to obtain a space-time degradation prediction result; according to the space-time degradation prediction result, state evaluation of the water conservancy facilities is carried out through a dual-objective optimized dynamic weight distribution algorithm, and an operation and maintenance state evaluation result is obtained; and on the basis of the operation and maintenance state evaluation result, performing cross-basin cascade fault prevention and control on the water conservancy facilities to obtain a cascade fault prevention and control scheme. The method improves the prediction precision of the degradation of the water conservancy facilities, and enhances the anti-risk capability of the water conservancy infrastructure.

The invention discloses a distributed photovoltaic equipment detection method and system based on big data analysis, through multi-source data acquisition including equipment operation data, state data, electric energy quality data and environment data, comprehensive working information of a photovoltaic power station can be obtained, rich data support is provided for subsequent analysis, and the detection efficiency is improved. Data cleaning and standardization technologies are adopted, the quality and consistency of data are ensured, the accuracy of subsequent model training and fault diagnosis is improved, fault types are given through a multi-model fusion diagnosis method in combination with feature indexes and a detection method, and a fault knowledge base is constructed, so that the accuracy of fault diagnosis is improved, and the fault diagnosis efficiency is improved. And valuable experience and data support are provided for future fault prevention and maintenance.

The invention provides a reliability test evaluation method and system for an integrated circuit chip, and relates to the technical field of integrated circuit chip test.The method comprises the steps that a test target and a test index are defined; constructing a multi-level test environment; performing a dynamic stress test in the constructed multi-level test environment; based on the historical test data and the monitoring data, establishing a prediction model by using a machine learning algorithm to predict performance changes and potential faults of the integrated circuit chip under different conditions; performing intelligent diagnosis on faults occurring in the test process by using the established prediction model, and positioning a fault source; and continuously monitoring the test process through the online remote monitoring platform, and dynamically adjusting the test parameters and the test strategy according to the monitoring data and the prediction result of the prediction model. The reliability of the integrated circuit chip is comprehensively and accurately evaluated, and the test efficiency and the fault prevention capability are improved through a multi-level test environment, machine learning prediction and intelligent diagnosis.

The invention discloses a communication base station electric power safety monitoring alarm system based on the Internet of Things technology, and relates to the technical field of Internet of Things communication base station electric power safety monitoring. A multi-mode sensing fusion and self-calibration sensor is used for data collection, electric power data are accurately collected, transmission depends on terahertz communication in combination with hybrid encryption and self-adaptive dormancy, and the communication base station electric power safety monitoring alarm system is high in reliability. Safety and low consumption are ensured, edge intelligence and federated learning are analyzed, knowledge graph is used for auxiliary diagnosis, alarm is used for setting a dynamic threshold value, intelligent linkage is achieved, equipment management depends on digital twinning and a block chain, and stable operation of the equipment in the whole life cycle is guaranteed. According to the invention, accurate power data acquisition, high-speed safe transmission, intelligent analysis and processing, multi-level linkage alarm, remote safety control of equipment, linkage environment monitoring and fault prevention can be realized, the power safety of the communication base station can be comprehensively guaranteed, and the communication stability can be improved.

The invention discloses an AGV fault diagnosis and prediction system and method based on data analysis, and relates to the technical field of automated guided vehicle management, and the AGV fault diagnosis and prediction system based on data analysis comprises a data management module, a fault diagnosis module, a fault prediction module and an adjustment control module. The method can quickly respond to possible operation problems by recognizing fault data in time, reduce downtime and predict possible faults in the future, so that an enterprise can turn from reactive fault processing to active fault prevention, thereby greatly improving the continuity and stability of production and reducing the production cost. By analyzing the key factors influencing the operation of the automatic guided vehicle, the operation parameters can be adjusted and optimized to deal with the predicted potential faults, so that the overall maintenance cost is reduced, and the service life of the automatic guided vehicle and related equipment can be effectively prolonged.

The invention discloses a power Internet of Things fault prevention method and device and a storage medium, and the method comprises the steps: collecting the system state, system operation data and historical fault data of the power Internet of Things; inputting the system operation data and the historical fault data into a preset fault detection model to predict a fault event and a fault part when the electric power Internet of Things has a potential fault in the future, generating a preventive strategy report according to the fault event and the fault part, and optimizing the preventive strategy report according to the system state, the fault event and the fault part. And executing a repair operation according to the fault-tolerant strategy to obtain a repair result, if the repair result is repair failure, starting a standby system for the fault part, and optimizing the fault-tolerant strategy according to the repair result. According to the embodiment of the invention, the system operation data and the historical fault data are monitored and analyzed in real time through the electric power Internet of Things acquisition system, so that potential faults can be accurately predicted, a more comprehensive fault-tolerant strategy can be generated, and the accuracy of fault prediction is improved.

The invention provides a railway equipment fault report analysis method and device, and the method comprises the steps: collecting fault report data of railway equipment, and carrying out the data cleaning and preprocessing; a pre-trained BERT model is used for processing a text sequence of the fault report, the BERT model adopts a 12-layer encoder in a Transform architecture as a basic component, context information is extracted from a front text and a back text, a word vector in each sentence is generated, and named entity recognition is carried out; establishing a railway fault knowledge graph according to the identified named entities; and displaying the constructed railway equipment fault knowledge graph through visual representation, performing statistical analysis on fault data in combination with a visual form, and revealing distribution and trend of faults. The method can provide a scientific basis and a favorable tool for maintenance and fault prevention of the railway equipment, effectively improves the efficiency and accuracy of fault analysis of the railway equipment, and improves the safety and operation efficiency of a railway system.

The invention relates to the technical field of electric energy meter burning prevention, in particular to an electric energy meter burning fault prevention system fusing multi-modal data. The system comprises a data acquisition module used for acquiring an environment temperature sequence and a voltage sequence, a current sequence and an internal temperature sequence of each phase of an electric energy meter; the data normality analysis module is used for acquiring the data normality probability of each phase at each moment; the data anomaly judgment module is used for fitting the voltage sequence and the current sequence of each phase to obtain a fitting curve of each phase, and then judging whether the current moment is a suspected anomaly moment or not; the time segmentation module is used for acquiring different time periods by using the environment temperature sequence; the temperature trend analysis module is used for acquiring the current stability degree, the current approaching degree and the temperature slope of each time period; and acquiring a predicted temperature slope based on the current stability degree, the current approaching degree and the temperature slope of each phase in each time period, and further performing early warning. The electric energy meter can be effectively prevented from being burnt.

The invention discloses a fault monitoring method and system using AI intelligent image recognition, and the method achieves the continuous monitoring of the subtle change of equipment through collecting the time-space information image data of the operation state of the equipment and extracting the dynamic characteristics of motion trail, deformation rate, spectrum change and the like. The dynamic features are associated with the historical state of the device, and a dynamic incidence matrix is constructed to analyze potential trend deviation points in a time sequence, so that possible faults are predicted, and not only occurred problems are identified. An abnormal mode matching rule is generated by calculating a prediction window and combining the prediction window with equipment operation environment parameters, meanwhile, a response threshold value is adjusted to adapt to equipment performance attenuation characteristics, and finally an early warning signal is generated through cross validation of the abnormal mode matching rule and the prediction window. According to the method, the abnormal development trend can be identified before the equipment has an obvious fault, early warning is given out in advance, the transformation from post-event identification to pre-event early warning is realized, and the fault prevention capability is remarkably enhanced.

The invention relates to an intelligent vending machine and a remote management system. The intelligent vending machine and the remote management system comprise the intelligent vending machine and a remote management platform. The intelligent vending machine is provided with a movable goods shelf or a mechanical arm used for storing and dynamically adjusting the positions of commodities. The sensor is used for monitoring commodity inventory, mechanical part states and environmental parameters, and is in bidirectional communication with the remote management platform through the communication module. The remote management platform comprises a commodity configuration module, generates a commodity position adjustment instruction based on real-time sales data, sales promotion activities or customer behavior analysis, and sends the instruction to the intelligent vending machine to realize real-time commodity layout adjustment. The system further comprises a fault prevention and self-repair module which analyzes sensor data through machine learning, predicts potential faults of the mechanical part and executes self-diagnosis. And the customer service module provides a video call function and realizes purchase suggestion, fault processing and refunding and changing services. According to the invention, intelligentization and adaptive optimization of commodity management are realized, and the operation efficiency and user experience of the vending machine are improved.

The invention discloses a base station alarm early warning management method and system. The method comprises the following steps: acquiring a first operation state sequence of each of a plurality of first base stations in a target area in a first time period; for each first base station, analyzing the first operation state sequence corresponding to the first base station by using a pre-trained base station alarm type prediction model to obtain a first predicted base station alarm type of the first base station at the first moment; and determining the troubleshooting priority of each first base station according to the first predicted base station alarm type of each of the plurality of first base stations in the target area at the first moment, and managing the plurality of first base stations in the target area according to the troubleshooting priorities. According to the invention, the technical problem that the operation and maintenance efficiency and the fault defense capability of the base station are relatively low because the related base station operation and maintenance technology cannot carry out early warning on the base station alarm in advance is solved.

The invention discloses a handcart type high-voltage circuit breaker operating characteristic evaluation method based on big data analysis, and particularly relates to the field of data analysis, which comprises the steps of monitoring time division, data entry, data analysis and evaluation. All-time multi-dimensional monitoring is achieved through big data analysis, monitoring sub-areas are divided, dynamic data are collected at high frequency, transient changes are captured, the limitation of traditional single-point monitoring is broken through, and comprehensive data support is provided; quantitative indexes are constructed based on a multi-parameter fusion model, the electrical and mechanical system collaboration is comprehensively evaluated, the multi-dimensional performance is reflected, and the evaluation precision is improved; and a health interval judgment rule is established, so that real-time comprehensive evaluation can be realized, faults can be accurately positioned, anomalies can be recognized in advance, a basis is provided for maintenance, and the intelligent level of power operation and maintenance and the fault prevention capability are improved.

The invention discloses an automatic conversion analysis method and system based on an implicit common cause fault model, and relates to the technical field of power system fault analysis, and the method comprises the steps: obtaining and marking power equipment, forming a plurality of equipment sets, setting a monitoring period for operation monitoring, and storing data into an operation record library; extracting feature data of the equipment, calculating a correlation index between the equipment according to the category of the equipment, and calculating the correlation between the same category of equipment and different categories of equipment by adopting different strategies; utilizing an implicit common cause fault model to calculate the probability that the reference equipment and other equipment are abnormal at the same time under each abnormal cause, including an independent failure probability, a common cause failure probability and a comprehensive probability; and according to the comparison of the comprehensive probability and a preset threshold value, the monitoring period of the equipment exceeding the threshold value is adjusted, and according to the abnormal reason, the monitoring period of all the associated equipment is also adjusted, so that the effective monitoring and fault prevention of the power system are realized.

According to the converter valve waste heat digital comprehensive analysis model and method, working parameters of a converter valve are monitored in real time, a physical mechanism and a data driving method are combined, and a hybrid modeling system is constructed for waste heat model building and dynamic correction. A multi-scale data fusion framework is adopted to carry out data processing and mode recognition, the accuracy of model prediction is evaluated through a residual analysis mechanism, and future temperature rise prediction is carried out. According to the method, a dynamic knowledge graph is constructed based on a digital twin platform, multi-dimensional report contents including a cooling system upgrading scheme, a valve tower inspection period adjustment strategy and the like are generated, and data traceability is ensured through block chain evidence storage. According to the method, the requirements of a current converter valve waste heat analysis method for real-time monitoring and accurate prediction are improved, a feasible mode for real-time monitoring and accurate prediction of converter valve waste heat is provided, the accuracy of equipment working state feedback and fault prevention measures is ensured, and the stability of a high-voltage direct-current power transmission system is further ensured.

The invention relates to the technical field of miniature circuit breakers, in particular to a method for recording events with time marks of a miniature circuit breaker and a miniature circuit breaker system, and the miniature circuit breaker is provided with an internal real-time clock module and a storage medium. The method comprises the following steps: when the miniature circuit breaker is powered off, the miniature circuit breaker obtains power-down time from an internal real-time clock module, generates a power-down event record containing the power-down time, and stores the power-down time into a storage medium; after the electricity meter and the miniature circuit breaker are powered on again, the electricity meter issues an instruction with a time mark to the miniature circuit breaker; and the miniature circuit breaker carries out time calibration based on the instruction with the time mark, and generates a power failure recovery event record containing calibrated time. The miniature circuit breaker can record the occurrence and recovery time of the fault, valuable time data support is provided for the formulation of fault prevention and maintenance strategies, and the reliability and maintenance efficiency of a power system are remarkably enhanced.

The invention discloses a distributed household photovoltaic work order processing system and a use method thereof, and belongs to the technical field of photovoltaic work order processing. The system is provided with a work order management module, and through an intelligent distribution algorithm of a task scheduling module, the system can perform intelligent distribution according to factors such as skills, positions and task loads of engineers; and the work order is automatically allocated to the most suitable engineer. The intelligent distribution not only improves the work order processing efficiency, but also ensures that the engineers can efficiently process the work orders distributed to the engineers. And the engineer operation module allows an engineer to update a work order state in real time, records used parts and equipment, and ensures transparency and traceability of a work order processing process. The data analysis and report module is an important part of the system, generates various statistical reports by analyzing work order data, and provides decision support for a management layer. And common fault reasons can be identified by analyzing the fault type distribution system, so that engineers are guided to perform more effective fault prevention and processing.

The invention discloses a converter transformer winding stress analysis method, which is applied to a single-phase four-column converter transformer, and comprises the following steps: obtaining the magnetic flux of a magnetic circuit adjacent to a main column of the converter transformer; determining a two-dimensional model of the main column according to the magnetic flux; inputting excitation current into the two-dimensional model, and determining a magnetic field model of the main column; the excitation current comprises a fundamental current and a harmonic current; characteristic parameters of the converter transformer are obtained, a vibration equation of the converter transformer is determined according to the characteristic parameters and the magnetic field model, and the vibration equation is used for expressing stress of a winding of the converter transformer. According to the method, the fundamental current and the harmonic current are combined to analyze the stress of the winding of the converter transformer, the vibration problem of the converter transformer in actual operation is deeply and systematically known, and sufficient theoretical basis and technical support are better provided for design optimization, performance improvement and fault prevention of the transformer.

The invention discloses an intelligent ditch gate dam self-adaptive adjustment method based on the Internet of Things, relates to the technical field of drainage control, and provides a set of integrated method of multi-source data fusion, predictive diagnosis and automatic fault tolerance for fault prevention and disposal of a gate dam execution mechanism in farmland irrigation. High-quality sensing data is obtained in real time through edge calculation and a wavelet noise reduction technology, and potential anomalies are marked in time; secondly, analyzing the state of an execution mechanism by combining a historical fault record and a health model, and sending out a maintenance suggestion in advance; if a serious fault occurs, the local controller automatically starts secondary adjustment and emergency linkage to temporarily compensate downstream water supply or flood diversion; and finally, the central dispatching center iteratively updates a dispatching strategy by using a global collaborative optimization and self-learning mechanism, and the system reliability and the resource utilization efficiency are enhanced. Therefore, closed-loop management of the gate dam execution mechanism is realized, and stable operation of large-scale farmland irrigation under load and outdoor environments is ensured.