958 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 a deep learning-based deep fusion network production line fault prediction method, which comprises the following steps of: firstly, acquiring SCADA (Supervisory Control And Data Acquisition) system or sensor data of an automatic production line, and carrying out data preprocessing, feature extraction and fault prediction label construction; then a deep fusion network 1D-CNN-LSTM-Attention-DCE model combining a one-dimensional convolutional network, a long short-term memory neural network, a fusion attention mechanism and a dynamic cross entropy loss function DCE is established, the preprocessed data is input into the constructed model for training, and finally the trained model is used for fault prediction to obtain a fault prediction result. According to the method, a 1D-CNN-LSTM-Attention-DCE fault prediction model is provided, local features are extracted from time sequence data by using the 1D-CNN, then a long-time dependency relationship is captured through LSTM, and finally weight distribution of key features is enhanced through an attention mechanism, so that the processing capability of the fault prediction model on complex time sequence data is comprehensively improved, and the fault prediction efficiency is improved. And the fault of the production line can be accurately predicted within a certain time length range.

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 technical field of machine learning, in particular to a heating fault prediction method and system for a high-voltage circuit breaker and an isolating switch of the high-voltage circuit breaker. The method comprises the following steps: carrying out multi-source data acquisition on a high-voltage isolation switch and an associated high-voltage circuit breaker through an SCADA system, and carrying out data preprocessing to obtain a preprocessed data set; performing operation feature correlation analysis on the preprocessed data set to obtain a correlation operation feature set; performing dynamic association network construction on the association operation feature set to obtain a dynamic association network graph; acquiring historical fault data; performing propagation risk path search according to the historical fault data and the dynamic association network diagram to obtain a candidate risk path set; and performing path propagation probability calculation on the candidate risk path set to obtain a path probability data set. According to the method, the influence of the associated high-voltage circuit breaker on the heating risk of the isolating switch is effectively identified, so that the accuracy of fault prediction and the timeliness of early warning are improved, and a more reliable guarantee is provided for safe and stable operation of a power system.

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 present invention discloses an intelligent measurement and control system for a withdrawable switch cabinet and a control method thereof, which relate to the technical field of automatic control of electric power systems, and solve the problems of slow response speed, inflexible control strategy and difficulty in coping with extreme weather conditions in existing intelligent measurement and control systems when responding to changes in power grids; real-time data acquisition and analysis are realized by integrating an edge processing module, and state prediction and anomaly detection are performed in combination with a long short-term memory network; an adaptive control module is used to optimize the control strategy according to dynamic monitoring results; at the same time, the emergency response module monitors environmental variables through a SCADA system, and an intelligent scheduling algorithm adjusts the switch cabinet operation mode and switches standby equipment; the present invention greatly improves the safety and reliability of the switch cabinet, significantly enhances the rapid response capability and adaptive control capability of the power system to changes in the power grid, and ensures the stability and safety of the power supply.

The invention relates to the field of intelligent monitoring and fault prediction, and discloses a hydropower station computer monitoring system fault prediction method based on big data analysis, and the method comprises the following steps: collecting multi-dimensional sensor data of key equipment of a hydropower station, and carrying out the preprocessing of the data; a Morse function is constructed, the gradient and a Hessian matrix of the Morse function are calculated, topological features of the system are extracted, and a Morse-Smal complex is used for dividing system states to distinguish stable, transitional and fault states; based on the topological features and the time sequence data, using a Transform model to carry out fault prediction, and calculating the probability of equipment fault occurrence; and according to the calculated fault discrimination score value and in combination with a dynamic threshold adjustment strategy, if the score value exceeds a preset threshold, triggering an alarm mechanism of the SCADA monitoring system to realize real-time fault early warning. Through comprehensive data analysis and a deep learning model, the accuracy and real-time performance of equipment fault prediction are improved, and stable operation and safe maintenance of hydropower station equipment are ensured.

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 discloses a comprehensive algorithm for distribution and scheduling of AGV (Automatic Guided Vehicle) carrying tasks. S1: an SCADA system; equipment communication, data acquisition and storage, protocol analysis and logic processing; s11, the communication protocol, the address and the communication rhythm of the equipment are configured through the system, data acquisition data are stored in ES (ElasticSearch), the data acquisition data are stored for one year and backed up regularly, and the system achieves service logic processing through an LUA script. The carrying efficiency of the AGV is integrally improved, and the execution time of carrying a single task by the AGV is shortened by 10%; the production capacity of a workshop is improved, and the carrying rate exceeds 96% under the condition of no interference; the workshop logistics cost is reduced, and the overall number of AGVs is reduced by 10%; and the first pass yield of material carrying is improved by 15%. The deep fusion data analysis capability enables the whole system to predict demands more accurately, avoid conflicts, optimize the overall production efficiency, optimize the production process, reduce waiting and resource waste, and improve the flexibility of path planning and the reliability of task completion.

The invention relates to a power grid business intelligent auditing method based on AI enhancement, and the method comprises the following steps: S1, collecting data from a power grid SCADA system and a financial system, integrating historical and real-time data, and constructing an auditing knowledge graph; s2, constructing an instruction recognition model by using RoBERTa to analyze a natural language instruction of an auditor, and combining knowledge graph information with a language model to improve the understanding and reasoning ability of an instruction background; s3, constructing a multi-task deep learning framework to realize processing and fusion of multiple auditing tasks; s4, on the basis of a natural language instruction input by an auditor, extracting query targets, conditions and parameters involved in the instruction according to the instruction recognition model, obtaining corresponding auditing data in the database, and obtaining intention classification; and S5, calling a corresponding auditing model based on the intention classification result and the auditing data, and obtaining an analysis result. According to the invention, efficiency and precision in power grid business auditing work are effectively improved.

The invention belongs to the technical field of electromechanical equipment fault diagnosis, and discloses a wind turbine generator transmission chain online fault diagnosis method, which specifically comprises the following steps: S1, adopting an SCADA (Supervisory Control and Data Acquisition) system to collect environment, electrical and vibration multi-source monitoring parameters of a wind turbine generator in real time and pre-process the parameters, thereby effectively improving the accuracy and availability of data; s2, through the design of a multi-source feature association fusion module, complementarity of environment, electrical and vibration parameters is fully utilized, and the accuracy and reliability of fault diagnosis are improved; s3, a teacher-student network framework is constructed, online fault model construction and real-time diagnosis are achieved, the real-time performance and the intelligent level of fault diagnosis are improved, due to the application of a class balance sampling mechanism and a positive and negative combined learning strategy, the influence of continuous state label offset is eliminated, pseudo label noise interference in online samples is effectively reduced, and the real-time performance of fault diagnosis is improved. And the adaptive diagnosis capability of the model in a working condition environment dynamic change scene is improved.

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 discloses a wind driven generator anomaly detection method and system based on SCADA periodic data. The method comprises the steps of obtaining operation data collected by an SCADA system of a wind driven generator in actual operation; according to the method, a normal data set is constructed besides the abnormal data of SCADA alarm, and the abnormal data collected by an SCADA system comprises a periodic type, a stable type and an irregular fluctuation type; performing preliminary labeling on three types of data in the abnormal data; performing data pattern recognition on the obtained data by using a neural network model to recognize periodic data; performing feature engineering processing on the data of which the output result is periodic, and extracting corresponding data features; enhancing the features in a mode of calculating abnormal scores by using an isolated forest; and performing anomaly classification on the feature vectors formed in the time window by using a machine learning model, and detecting abnormal data in the periodic data. According to the method, the accuracy of abnormal data detection of the wind driven generator can be improved.

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 wind generating set abnormal state detection and joint interpretation method and device. The method comprises the steps that multi-dimensional operation data are acquired from an SCADA system of a wind generating set and preprocessed; clustering by adopting a k-means clustering algorithm, and selecting the calculated center features of the center cluster to construct a new feature data set; training a wind turbine generator abnormal state detection model by using a classification decision lifting algorithm, and detecting and identifying an abnormal state; establishing a simplified model output by the detection model, calculating a global contribution degree of an input feature to an abnormal state detection result by using an SHAP algorithm, and providing global explanation; establishing a local model of the detection model instance, generating local explanation for a specific abnormal instance by adopting an LIME algorithm, and analyzing contribution of key features in the specific abnormal instance; according to the method, the accuracy and interpretability of anomaly detection of the wind generating set are improved, so that solid data support and theoretical basis are provided for maintenance and optimization of a complex system.

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.

The invention relates to the technical field of industrial data acquisition and processing, and discloses a different industrial data optimal acquisition scheme analysis system and method based on an SCADA system, and the system comprises an industrial data acquisition information processing module, an industrial data acquisition scheme analysis module, and an industrial data acquisition optimal scheme generation module. According to client target industrial data acquisition scheme type information, in combination with an intelligent identification algorithm and industrial data acquisition scheme equipment composition information, industrial data acquisition scheme equipment composition information efficient retrieval is carried out, and equipment information used by different types of industrial data acquisition schemes is efficiently and scientifically analyzed; the industrial application applicability of the SCADA system is improved; the unit price information of the client target industrial data acquisition scheme composition equipment and the cost information of the client target industrial data acquisition scheme are scientifically and accurately counted, the cost information of different types of industrial data acquisition schemes is autonomously measured on line, and the scientificity of the industrial application of the SCADA system is 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.