776 results about "SCADA" patented technology

The invention relates to the field of fault early warning, in particular to a wind power fault dynamic early warning method and system based on multi-source heterogeneous data fusion. According to the method, multi-source data such as SCADA operation data, CMS vibration monitoring data and meteorological environment data of a wind turbine generator are collected in real time, standardization processing is carried out, and a multi-dimensional feature vector is constructed. And generating a fusion data set by using an adaptive weighted fusion algorithm, constructing a fault prediction model based on a deep convolutional neural network, and outputting a health state assessment value and a fault risk level in real time after historical fault sample supervised training. And when the risk level exceeds a threshold value, generating an early warning signal containing a fault type and a positioning and repairing suggestion, dynamically adjusting a monitoring parameter weight, iteratively updating a model, and realizing adaptive optimization of an early warning strategy. The problem that an existing method depends on single data source and multi-source data fusion is solved, and accurate dynamic early warning is achieved.

The invention discloses an intelligent power distribution network line state real-time monitoring, analysis and evaluation system, and relates to the technical field of power system monitoring and control, the intelligent power distribution network line state real-time monitoring, analysis and evaluation system is constructed, data is collected in multiple dimensions, features are extracted through edge calculation, data is screened, a dynamic threshold model is constructed at a cloud end, and abnormity is identified. The method has the advantages that a three-dimensional digital twin network is constructed based on GIS data and an SCADA system through the power grid topology modeling module, and a unique'topology fingerprint 'containing electrical connection, physical position and logic control relation is generated for each piece of equipment; an upstream and downstream equipment list, a load influence range and a power flow transfer path are marked, and an association state evaluation module adopts a graph convolutional neural network and can accurately capture electrical coupling, physical proximity and historical fault association effects among equipment.

The invention discloses a power grid abnormal flow detection method based on multi-modal data fusion, and the method comprises the steps: collecting the multi-source heterogeneous data of a power grid through an edge calculation node, including the current waveform of an intelligent electric meter, the state variable of an SCADA system, network protocol metadata and an equipment log event; performing space-time alignment preprocessing on the original data, converting an unstructured log into a time sequence by adopting a sliding window mechanism, and eliminating sensor noise through an LSTM auto-encoder; constructing a multi-dimensional feature space which comprises a frequency domain feature, a spatial-temporal feature and a protocol feature, and obtaining a feature vector; the feature vectors are input into a hybrid detection model, the model is composed of an isolated forest algorithm, an improved CNN-LSTM classifier and an information entropy-based rule engine which are connected in parallel, and a dynamic weight fusion strategy is adopted to output an abnormal probability; and when the abnormal probability exceeds a dynamic threshold, triggering a multi-level response mechanism: sending a traffic shaping instruction to the edge device, generating a device fingerprint portrait on the cloud platform, and storing abnormal event features through a block chain.

The invention provides a fan system multi-physics field coupling simulation and modeling method and device, and the method comprises the steps: building a coupling simulation frame of an electromagnetic field, a temperature field, a flow field and a structural mechanical field through multi-physics field collaborative modeling, employing a finite volume method FVM and multi-body dynamics MBS for collaborative solving, and precisely simulating the dynamic response of a fan under a complex working condition. And real-time simulation and verification: combining an S CADA system and a machine learning algorithm to realize online calibration and dynamic verification of a multi-physics field model, and supporting fan structure stability prediction under extreme conditions of typhoon, turbulence and the like. A multi-physics field coupling simulation software environment is developed, the functions of wake effect analysis, fan array layout optimization and the like are provided, the construction cost of an offshore wind field is reduced, and the power generation efficiency is improved.

The present invention relates to a method for constructing a digital twin system for an additive manufacturing process with lifecycle data management. The digital twin system for an additive manufacturing process constructed by the method includes a data acquisition and supervisory control system, a data management and storage system, a manufacturing executing system, a configuration model, a simulation system, and a multi-physics simulation system. The present invention can fully and accurately characterize the molding quality of parts, thereby saving a lot of time required for offline data processing, and achieving real-time acquisition and updating of data.

The invention discloses a wind power fault diagnosis operation and maintenance method based on multi-source data and knowledge retrieval enhancement. The method comprises the following steps: S1, collecting and preprocessing multi-modal data; s2, knowledge base construction: performing knowledge extraction, and constructing a corresponding wind turbine generator operation and maintenance knowledge graph database; s3, multi-granularity problem decomposition and retrieval optimization are carried out, and a sub-problem list is generated by deeply grading and splitting problems in combination with doubt degree and dependency analysis; s4, knowledge retrieval enhancement generation: combining knowledge graph sub-graph retrieval and SCADA real-time data dynamic weight adjustment to generate a final answer; and S5, result generation and feedback optimization are carried out, and the validity of the diagnosis result is verified through a real-time verification mechanism. According to the method, the accuracy, the reliability and the real-time performance of fault diagnosis can be remarkably improved in a limited fault data environment.

The invention relates to the field of wind power plants, in particular to a wind power plant edge calculation data cleaning and real-time transmission optimization method and system. The method comprises the following steps: forming a multi-source heterogeneous data stream by collecting SCADA operation data, CMS vibration monitoring data and meteorological environment data of a wind turbine generator in real time; performing standardization processing on the data at an edge computing node, constructing a multi-dimensional feature vector, and generating a fusion data set by adopting a self-adaptive fusion algorithm based on a dynamic weight; and constructing a fault prediction model based on the deep dual-channel convolutional neural network, and outputting a health state evaluation value and a fault risk level in real time. And when the risk level exceeds a threshold value, generating an early warning signal. The system also performs data compression transmission optimization according to the real-time bandwidth state, and dynamically adjusts the weight of the monitoring parameter to realize the closed-loop optimization of the preventive maintenance strategy.

The invention discloses a health degree evaluation system and method based on a photovoltaic string, and belongs to the technical field of photovoltaic operation and maintenance intellectualization, and the method comprises the steps: firstly collecting SCADA operation data of a plurality of strings of a photovoltaic power station, including voltage, current, assembly temperature, environment irradiance and power factors; secondly, constructing a whole-station feature reference model, and generating a state vector fusing environment and load characteristics through a nonlinear projection algorithm; aiming at the target group string, extracting a health reference track, calculating a disturbance propagation factor, and identifying a health abnormal state; when the deviation degree exceeds a threshold value, dynamically screening heterogeneous reference group strings, constructing a health score distribution model, and further calculating a confidence score and a prediction residual error; if the score is low and the residual error is unstable, determining that the string is in a sub-health or hidden fault state, and generating an intervention instruction; the method has high accuracy, strong adaptability and good interpretability, and can be widely applied to intelligent diagnosis and refined operation and maintenance management of the photovoltaic power station.

The invention relates to the technical field of power management, in particular to a multi-source heterogeneous data three-dimensional fusion analysis method based on a power grid unified information model, which comprises the following steps of: performing timestamp alignment and space coordinate normalization processing on SCADA system real-time data, GIS geographic information data, equipment vibration spectrum data and infrared thermal imaging data; and constructing a unified three-dimensional attribute matrix, and fusing the multi-source data based on a power grid unified information model. A three-dimensional space interpolation algorithm is adopted to carry out topological reconstruction on the model, a three-dimensional space-time topological structure containing equipment connection relations, electrical parameters and space coordinates is generated, intelligent prediction is carried out on the equipment fault probability in combination with a depth prediction model, and an augmented reality display interface with prediction and early warning information is generated through a three-dimensional visualization engine. According to the invention, the accuracy and the real-time performance of power grid equipment monitoring are effectively improved, the fault early warning and decision support capability is enhanced, and the operation safety and the management efficiency of a power grid are improved.

The invention provides a multiphase collaborative purification intelligent treatment system for heavy metal wastewater, which relates to the technical field of wastewater treatment and comprises a pretreatment module, a main treatment module, an advanced treatment module, a post-treatment and recycling module, a sludge treatment module and an intelligent control module. The intelligent control module comprises a data acquisition layer, a control layer and a management layer, and intelligent control on each treatment module is realized by monitoring parameters such as pH value, ORP (oxidation-reduction potential), conductivity and heavy metal ion concentration of the wastewater on line. The data acquisition layer acquires processing parameters in real time through various sensors; the control layer executes control logic based on the PLC / DCS and monitors the operation state through SCADA (Supervisory Control And Data Acquisition); the management layer adopts AI algorithms such as a water quality-energy consumption correlation regression model and a time sequence water quality fluctuation learning model to optimize processing parameters. According to the system, the treatment process can be intelligently adjusted according to the characteristics of the wastewater, the maximization of the heavy metal wastewater treatment efficiency, the minimization of chemical consumption and the minimization of energy consumption are realized, and the treatment effect and the system stability are improved.

The invention discloses a power equipment fault prediction system based on big data analysis. The method comprises the following steps: acquiring initial equipment multi-dimensional data; constructing a dynamic topology network of the power equipment, including a dependency relationship between the equipment and a fault propagation path, performing embedded learning on the dynamic topology network by using a GNN graph neural network, and extracting equipment collaboration features in the initial equipment multi-dimensional data; a multi-task learning framework is constructed in combination with the equipment cooperation features to predict the equipment fault probability and the remaining service life, and an equipment health index is obtained; and acquiring environmental parameters, dynamically adjusting a fault judgment threshold based on the equipment health index and the environmental parameters, generating a prediction result, integrating the prediction result with an SCADA system, and triggering graded early warning. And the influence of environmental factors on the operation state of the equipment is fully considered. Under different environmental conditions, the equipment fault risk can be judged timely and accurately.

The invention relates to the field of wind power plants, in particular to a dynamic grey correlation analysis method and system for the health state of a wind power transmission chain. The method comprises the following steps: constructing a multi-source heterogeneous monitoring data set by collecting SCADA operation data, CMS vibration spectrum data and gearbox oil temperature data of a wind turbine generator transmission chain in real time; and performing sliding window division and time domain alignment on the data to generate a standardized feature sequence with time consistency. Based on a grey correlation analysis algorithm, calculating a dynamic correlation degree between each monitoring parameter and a preset fault feature sequence, and generating a correlation degree matrix; and carrying out weighted fusion on the correlation degree matrix by adopting a self-adaptive weight adjustment strategy, and constructing a transmission chain health state evaluation model. And according to a model output result, generating a real-time health degree score and fault probability distribution of the transmission chain component. And when the health degree score is lower than a dynamic threshold value, triggering a multi-level early warning mechanism and generating a maintenance priority list. And carrying out sliding window iteration updating on the evaluation model based on a newly added fault sample, optimizing a grey relational degree calculation parameter, and realizing dynamic self-adaption of health state evaluation.

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 relates to the technical field of product test data management, and discloses a product test data storage and management system based on an MES system. The system comprises a dynamic configuration module which can use a self-adaptive parameter adjustment algorithm to configure data acquisition parameters according to experiment types; the closed-loop feedback module is used for binding experimental data with production batches and process parameters based on a real-time association algorithm; the two-stage verification module is used for detecting data exception through a double-layer cascade verification algorithm; and the intelligent storage module is used for generating hierarchical storage labels by using a multi-dimensional structured coding algorithm. In addition, the system integration module realizes data interaction with ERP and SCADA systems based on an interface adaptation algorithm. The system solves the problems that in a traditional data management mode, collection parameters are fixed, data relevance is poor, a verification mechanism is imperfect, storage and retrieval are inconvenient and the like, data can be efficiently collected, accurately associated, strictly verified and intelligently stored, and the enterprise data management level and production competitiveness are improved.

The invention relates to the technical field of ocean engineering construction, and discloses a construction positioning and SCADA visualization integrated intelligent arrangement method for an arrangement ship, which comprises the following steps: synchronously acquiring coordinates and attitude parameters of the arrangement ship through a Beidou positioning terminal and an inertial navigation device, and constructing a three-dimensional digital twinborn model of a construction scene in combination with submarine topography surveying and mapping data; analyzing boundary and gradient characteristics of the target arrangement area based on the model, and generating an initial arrangement path planning scheme; position deviation and ocean current flow velocity data are monitored in real time in the operation process, and the laying path is dynamically adjusted; and the position of the laying ship, the path adjustment record and the submarine topography change are displayed in real time through a visual interface of the SCADA system. According to the method, high-precision positioning, dynamic path optimization and full-process three-dimensional visual monitoring of the laying operation are realized, the problems of large positioning deviation and response lag of a traditional method are solved, and the construction precision and efficiency are remarkably improved.

The invention provides an SCADA system label generation method and system based on structured multi-source information fusion, and relates to the technical field of industrial automation and control. According to the method and the system, firstly, an initial information modeling framework including a tag name, a data type and an equipment association relationship is generated based on an SCADA system architecture; establishing a multi-level constraint rule base according to engineering domain characteristics, analyzing the information modeling framework through a large language model, performing semantic verification in combination with a domain knowledge graph, and generating a structured tag file conforming to a used SCADA (Supervisory Control and Data Acquisition) system; and finally, through an application platform of an SCADA (Supervisory Control and Data Acquisition) system or by constructing a digital twinborn verification platform containing a simulation test environment, carrying out an injection test on the generated tag, and verifying the field integrity, logic consistency and system compatibility of a tag file. According to the method, the large language model can adapt to a specific industrial control scene when processing SCADA system label generation.

The invention provides a controllable SCADA (supervisory control and data acquisition) industrial production control platform based on an industrial internet architecture. The controllable SCADA industrial production control platform comprises a field layer, an edge layer, a communication layer, a cloud / fog computing layer and a data management layer, wherein the field layer is composed of a sensor array, an execution mechanism, a PLC controller and an RTU terminal; the sensor array comprises a temperature sensor, a pressure sensor, a flow sensor and a position sensor, and adopts 4-20mA current loop signal output; the execution mechanism comprises an electric valve, a servo motor and a variable frequency driver, and communicates with the PLC through a Modbus RTU protocol; the PLC controller is provided with a dual-core processor and is provided with 16 digital quantity input / output channels and 8 analog quantity input channels; the RTU terminal is internally provided with a LoRa wireless communication module; the edge layer comprises an edge computing node and a protocol conversion gateway; the edge computing node is provided with an FPGA coprocessor and supports an edge reasoning framework; and multi-protocol conversion is realized through the protocol conversion gateway.

The invention discloses a hybrid photovoltaic prediction method based on SARIMA-LSTM, and the method comprises the steps: carrying out the calculation according to historical photovoltaic power generation data in an SCADA system, wherein the historical photovoltaic power generation data comprise the power generation power and the accumulated power generation amount, and the meteorological data comprise the irradiance, the temperature, the humidity, the wind speed, the cloud cover and the like, and are obtained from a meteorological station or an open-source data platform; statistics is carried out by combining an SARIMA model with a seasonal prediction function and an LSTM model for processing a time sequence, and a more accurate photovoltaic power generation prediction value is obtained after combined prediction and evaluation. Compared with the prior art, short-term prediction and long-term prediction can be considered, and seasonal change data prediction is more accurate.

The invention relates to the technical field of vacuum induction heating temperature control, and discloses a vacuum induction heating temperature closed-loop control system integrated with SCADA (supervisory control and data acquisition), which is characterized in that a functional material layer with preset Curie temperature is compounded on the surface of an induction coil, and the absolute value of phase difference is analyzed by collecting voltage and current signals of the coil in real time; according to the invention, through the synergistic effect of the electromagnetic impedance sudden change characteristic of the functional material layer and the phase difference detection mechanism, non-contact accurate control of the phase change critical temperature of the workpiece is realized; and meanwhile, due to the coupling application of the asymmetric micro-pulse waveform design and the dynamic adjustment strategy, the contradiction between thermodynamic response and electromagnetic interference in a vacuum environment is effectively balanced, and the control precision and stability of the system are remarkably improved.

Disclosed is an explainable method for monitoring a state of a generator of a wind turbine generator system on the basis of a spatio-temporal graph. The method includes: S1: acquiring data collected by a supervisory control and data acquisition (SCADA) system; S2: carrying out data understanding on the SCADA data, selecting features associated with the generator, and carrying out data preparation on the selected feature data, and obtaining valid data; S3: embedding the SCADA data, and forming a directed spatio-temporal graph data sequence; and S4: carrying out modeling of a normal behavior model of the generator on the constructed directed spatio-temporal graph data sequence, computing a full-graph-level residual and a node-level residual, computing a residual through an exponentially weighted moving average (EWMA) control chart method, carrying out full-graph-level state monitoring on the generator, forming a fault information transmission chain relation, and enhancing explainability and robustness of a monitoring result.

The invention relates to the technical field of computer systems based on specific calculation models, and discloses a control method and system for an intelligent calculation center comprehensive energy supply device, and the method comprises the steps: collecting the operation data of energy supply equipment and an environment sensor in real time through an SCADA system, and carrying out the preprocessing; based on the collected data and simulation data generated by a simulation environment, performing offline mixed training on the reinforcement learning model to generate an energy scheduling strategy; inputting a real-time state into the trained reinforcement learning model to generate a preliminary scheduling instruction, performing security verification and interpretability analysis by using a large language model, and optimizing a strategy; and fusing the preliminary scheduling instruction with the suggestion of the large language model, generating a final scheduling command through the energy router control unit, and issuing the final scheduling command to the energy supply equipment for execution. The problems that in the prior art, black box decision making and simulation are not accurate, and experience is difficult to solidify are solved, and the purposes of decision making transparency, simulation high fidelity and experience structuring are achieved.

The invention relates to the technical field of new energy power data governance, in particular to a multi-source fusion cleaning and closed-loop quality control method and device for wind power data and a medium, and the method comprises the steps: generating an initial data set through collecting SCADA time sequence data and at least one type of external meteorological data of a wind turbine generator; based on the initial data set, executing physical consistency check, grouping adaptive statistical detection, unsupervised machine learning detection and time sequence residual analysis in parallel, and generating four independent exception judgment vectors; inputting the four abnormal judgment vectors into a preset fusion decision model, and outputting a comprehensive abnormal label; obtaining a repaired data set according to the comprehensive abnormal label; and calculating a multi-dimensional quality score representing data quality based on the repaired data set, and triggering a quality control closed-loop operation according to the multi-dimensional quality score and a comparison test result. Through the arrangement, multi-dimensional fusion detection and self-adaptive intelligent repair can be realized, and integrated cleaning of quality quantitative evaluation and closed-loop verification can be realized.

The invention discloses a wind turbine generator state monitoring method driven by network fusion of a knowledge graph and a space-time diagram. The method comprises the following steps: S1, collecting time sequence SCADA data and wind power text data of a wind turbine generator; s2, constructing the wind power text data into a wind power operation and maintenance knowledge graph; s3, generating time sequence diagram data by querying a wind power operation and maintenance knowledge graph and combining the preprocessed SCADA data; s4, carrying out normal behavior modeling on the time sequence diagram data by utilizing the time-space diagram neural network model, predicting each node in the diagram data in the healthy operation time period of the unit in the normal behavior modeling process, and calculating the maximum absolute value of the difference between the predicted value and the actual value of each node as the maximum absolute value of the difference between the predicted value and the actual value; the maximum absolute error is used as a health threshold value of each node to perform state monitoring; in the state monitoring stage, the matching condition of the mapping information of each node in the graph structure in the monitoring result of the real-time data and the fault transmission chain is compared, the health state of the unit is judged, and the fault early warning time is determined. According to the invention, the accuracy and robustness of the monitoring result are enhanced.

The invention discloses an AI-driven hydraulic model real-time checking and optimizing system which comprises a cloud platform, an edge layer and a sensing layer, the edge layer comprises a data preprocessing module, a local reasoning module and a network disconnection continuous transmission module, and the sensing layer comprises an SCADA pressure point, an IoT water meter, a GIS change log and a maintenance record. The cloud platform comprises a dynamic topology sensing module, a multi-modal space-time fusion module, an incremental dual-channel checking module, a multi-target optimization decision module, a digital twinborn visualization module, an edge-cloud collaboration module and an anomaly propagation deduction module. According to the method, labor dependence is reduced, working efficiency is improved, real-time performance is improved, emergencies such as pipe bursting can be found and processed in time, loss is reduced, the data utilization rate can be increased, data value is fully excavated, electric charge can be saved, annual scheduling cost is greatly reduced, a more accurate model is beneficial to optimization of a pipe network operation strategy, and the method is suitable for popularization and application. The water supply quality and reliability are improved.

The invention discloses a wind driven generator temperature monitoring method based on a graph space-time attention network, and the method comprises the following steps: carrying out the preprocessing of collected SCADA data, and obtaining a data set; each sensor is used as a node, the top-k neighbor relation of each node is calculated, edge weights among the nodes are calculated through a Gaussian kernel function, a weighted adjacency matrix is constructed based on the neighbor relation and the edge weights of all the nodes, and therefore a multi-sensor time sequence diagram of the wind driven generator is obtained, time sequence characteristics are given to the nodes by time sequence data collected by different timestamps, and a multi-sensor time sequence diagram of the wind driven generator is obtained. Forming a space-time diagram structure, and outputting time sequence characteristics of each sensor; a graph attention network is introduced into wind driven generator temperature state monitoring, and global and connectivity characteristics of a multi-sensor network are modeled. According to the method, the generator temperature prediction precision is remarkably improved by fusing a graph space-time two-dimensional attention mechanism and key variable screening, redundant variables are eliminated by adopting the maximum information coefficient MIC, and noise interference is reduced.

The invention relates to the technical field of monitoring and data acquisition systems, and discloses an SCADA (supervisory control and data acquisition) system based on a large language model, an implementation method and equipment, and the system comprises a user interaction terminal which receives a natural language instruction input by a user and displays a dynamic user interface; the natural language processing module is used for preliminarily analyzing the natural language instruction to obtain a user intention and key information; the large language model is used for deeply analyzing the user intention and the key information and generating a task description or a task execution plan; the MCP arrangement engine is used for mapping the task description or the task execution plan to a predefined function to obtain executable structured data; and the dynamic UI generator is used for generating a dynamic user interface based on the executable structured data and a preset UI instruction. According to the method, the usability and the data analysis capability of the SCADA system are improved through natural language interaction, and meanwhile, the reliability and the safety of the system are ensured by utilizing an MCP and a predefined function mechanism.

The invention discloses an oil and gas pipeline leakage monitoring method based on SCADA, and relates to the technical field of safety monitoring, and the method comprises the following steps: S1, building an SCADA data preprocessing model; s2, establishing a leakage feature extraction model; s3, performing leakage state identification through the leakage detection classification model; s4, positioning a leakage point; s5, calculating the leakage rate; s6, evaluating a risk level based on the leakage risk evaluation model; s7, performing real-time early warning, and dynamically adjusting the early warning level; and S8, making an emergency scheme. According to the oil and gas pipeline leakage monitoring method, the influence of environmental noise and operation disturbance can be effectively removed by establishing a plurality of models, the accuracy of leakage feature extraction is ensured, and high precision and dynamic adaptability of leakage positioning and rate estimation are realized. Compared with a traditional method, the method has the advantages that the sensitivity and accuracy of leakage detection are remarkably improved, and monitoring parameters can be dynamically adjusted in a complex environment.

The invention relates to the technical field of fan blade fault diagnosis, and discloses a fan blade fault diagnosis system and method based on HHT and DBSCAN. Synchronously acquiring vibration signals acquired by a three-axis acceleration sensor and wind speed and rotating speed working condition data acquired by an SCADA (Supervisory Control and Data Acquisition) system; eMD empirical mode decomposition is carried out on the collected vibration signals, the vibration signals are decomposed into a limited number of IMF signals, and effective IMF signals are screened; generating a Hilbert spectrum through HHT, extracting multi-scale frequency band energy features, and fusing SCADA data to carry out working condition adaptive normalization on the features; and then, processing the normalized features by adopting a self-adaptive DBSCAN clustering algorithm based on K-distance map parameters, and realizing fault diagnosis by identifying an abnormal cluster with a sample proportion of less than 5% and combining with a frequency band energy deviation threshold. According to the method, the problems that a traditional method is not thorough in decomposition of non-stationary signals, depends on labeled samples and is insufficient in clustering robustness are solved, and unsupervised and high-precision blade state monitoring is achieved.

The invention discloses an unmanned aerial vehicle patrol management method and system based on a new energy station, and relates to the technical field of unmanned aerial vehicle monitoring, and the method comprises the steps: calling a wind power plant SCADA system to obtain the topological information of a wind power plant, and synchronously building an electromagnetic interference distribution diagram through an unmanned aerial vehicle sensor in combination with Gaussian regression; sound pressure measurement is combined with a CFD inversion model to simulate a fan wake flow vortex core to construct a wake flow three-dimensional thermodynamic diagram; calculating a three-dimensional path cost diagram based on the electromagnetic interference distribution diagram and the wake flow three-dimensional thermodynamic diagram, and planning an unmanned aerial vehicle inspection path through a path planning algorithm in combination with unmanned aerial vehicle dynamics constraints; fan operation data are collected and processed through an airborne PDV system of the unmanned aerial vehicle, and fan operation faults are recognized based on frequency spectrum transformation and a support vector machine model. According to the method, the safety and the executive performance of the unmanned aerial vehicle routing inspection path in the complex space environment of the wind power plant are improved, and the efficient perception and the accurate diagnosis of the operation state of the fan are enhanced.

A system includes a message broker that uses a publish / subscribe messaging protocol. An edge gateway operatively couples one or more edge devices to the message broker. The edge gateway receives information from one or more of the edge devices and publishes at least some of the received information to the message broker in a protocol that is compatible with the publish / subscribe messaging protocol of the message broker. A host application is operatively coupled to the message broker and subscribes to at least some of the information published by the edge gateway. The edge gateway is configured to determine an edge gateway connection state and publish the edge gateway connection state to the message broker. The host application is configured to determine a host application connection state of the host application and publish the host application connection state to the message broker.