422 results about "Cause analysis" patented technology

The invention provides an intelligent subway equipment fault diagnosis method and system assisted by a large language model, and relates to the technical field of data processing, and the method comprises the steps: extracting key information through a large language model, constructing a multi-dimensional equipment fault knowledge graph, obtaining a historical fault data set, and extracting key fault monitoring parameters related to the fault, obtaining a sensing state data set, and judging whether the sensor state data is abnormal or not; and calling a pre-constructed sensing distortion correction algorithm, generating a fault monitoring correction parameter and executing parameter correction, inputting multi-dimensional monitoring data into a diagnosis engine driven by a large language model, and outputting a most possible fault type, cause analysis and recommendation processing strategy and a fault identification report. The technical problems that in the prior art, due to the lack of fusion modeling capacity for the unstructured fault text and the structured monitoring data, the intelligent degree of fault diagnosis is low, and accurate recognition and causal analysis are difficult to achieve are solved, and the fault recognition response speed and accuracy are improved.

The invention discloses an industrial system automatic fault diagnosis method based on a large language model. According to the method, a three-layer mapping system of industrial data, natural language description and knowledge reasoning is constructed, field multi-source sensor data are subjected to semantic conversion, and a quantitative calculation model based on a large language model is constructed based on historical data and logs. And a fault case is matched in real time with the help of a retrieval-enhancement generation technology to serve as a reference, the fault case and abnormal information are input into a knowledge reasoning model based on a large language model together, a structured logical reasoning chain is generated, and a diagnosis conclusion containing candidate faults, cause analysis and disposal suggestions is further output. Meanwhile, through user feedback and a reinforcement learning mechanism, the model and the knowledge base are adaptively updated, the defects of traditional static rules and expert experience are effectively overcome, the accuracy, interpretability and robustness of fault detection are remarkably improved, and the method adapts to complex and changeable working condition requirements.

The invention provides a thermal power plant thermal instrument fault diagnosis method and system based on vibration analysis, and relates to the technical field of fault diagnosis, and the method comprises the steps: collecting and processing a vibration signal of a thermal instrument, extracting an optimized vibration feature vector, constructing a hybrid neural network model, and training the hybrid neural network model to obtain an optimized fault diagnosis model; performing fault diagnosis on the vibration feature vector, and generating a fault type, possibility and confidence; and performing fault risk assessment and case reasoning, and generating a fault reason analysis report and a maintenance suggestion.

The invention discloses a failure chain quantitative analysis and risk assessment method and system based on a multi-level security model, and aims to solve the defects that accident cause analysis of a complex social technology system is inaccurate, and a risk assessment result is lack of effective verification. According to the method, a multi-level causal model is systematically constructed, a multi-dimensional failure chain (MDFC) is extracted, multi-dimensional risk quantification is performed on the MDFC, a directed weighted failure propagation network is constructed based on the multi-dimensional risk quantification, and structural features of the directed weighted failure propagation network are analyzed to identify key risk factors. The core innovation of the method is that reverse accident reason tracing and forward risk propagation path analysis based on the weighted network are fused, mutual verification and iterative optimization are realized by comparing analysis results of the two paths, so that the understanding of an accident evolution mechanism is deepened, and the reliability of evaluation is improved. The system vulnerability can be revealed more comprehensively, powerful support is provided for formulating accurate risk control measures, and the overall safety level of a complex system is improved.

A data processing system implements performing a root cause analysis that includes identifying a first anomalous signal data predictive of a root cause of a first anomaly in signal data received from a computing system, analyzing the sub-signals of the first anomalous signal data to generate labeled training data, training a gradient boosted tree model using the labeled training data, generating a decision tree based approximating a predictive performance of the gradient boosted tree model, determining insights data predictive of the root cause of the first anomaly based on the gradient boosted tree model and the decision tree, aggregating the insights and analyzing the aggregated insights data to determine a predicted root cause for the first anomaly, determining a confidence level associated with the predicted root cause, and categorizing the predicted root cause into one of a plurality of categories based on the confidence level.

The invention discloses a power grid fault intelligent diagnosis method and system based on multi-mode cooperation, and belongs to the technical field of power system fault diagnosis. The method comprises the following steps: collecting and off-line importing multi-modal data of primary equipment and secondary equipment of a multi-space power system in a power grid fault range in real time and standardizing the multi-modal data, and sequentially inputting the standardized multi-modal data into a local edge professional model, the method comprises the following steps: preliminarily reasoning fault properties in combination with a local knowledge base, inputting a knowledge-embedded causal reasoning large model deployed at a cloud end, generating an edge-cloud fault collaborative diagnosis model, iteratively updating the edge-cloud fault collaborative diagnosis model in combination with a multi-modal fault case library, and converting newest collected and imported power grid fault data into a multi-modal fault case library; and inputting the updated edge-cloud fault collaborative diagnosis model, generating a latest fault diagnosis report, and realizing the intelligent diagnosis of the power grid fault based on multi-modal collaboration. According to the invention, the power grid fault reason analysis performance and the identification accuracy are improved.

The invention relates to the technical field of electric power energy monitoring, and discloses a remote monitoring analysis method and system based on electric power equipment operation data, and the method comprises the steps: collecting the multi-element energy operation parameters of a multi-source energy system and the multi-element energy use parameters of a user side in real time; after the collected data are preprocessed, the preprocessed equipment operation state data are evaluated, and an electric power equipment operation state index is obtained; whether equipment faults exist or not is judged according to power equipment operation state indexes, virtual mapping of a multi-source energy system is constructed, and system operation conditions under different energy scheduling strategies are simulated in real time by combining user energy demand prediction and external environment changes; when potential energy supply abnormity or equipment failure is detected, a multi-stage early warning mechanism is triggered, and a decision report including abnormity positioning, reason analysis, energy scheduling optimization and equipment maintenance disposal suggestions is generated, so that potential problems can be found in time, and the stability and reliability of energy system operation are improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a root cause analysis method and device based on a space-time dependency graph, equipment and a medium. Comprising the steps of constructing a space-time dependency graph, generating a diagnosis path blueprint, identifying a fault source and a root cause entity type, generating a graph query statement, executing query and performing cause and effect verification, and outputting a root cause analysis report. And the causal reasoning and root cause positioning of the system state change are realized by fusing the graph structure information and the natural language processing capability. Through cooperative processing of a language model and a graph data structure, fault symptom information and system structured state data are deeply fused, a path is generated in the graph structure, and a causal relationship is verified, so that the ability of understanding a complex system state evolution chain is improved, and accurate identification and diagnosis of root causes are realized. And the accuracy and the automation level of root cause analysis are obviously enhanced.

The invention discloses a road traffic accident cause analysis and responsibility judgment method and system, and relates to the technical field of traffic management data processing, and the method comprises the steps: carrying out the credible collection of multi-source data, and constructing an evidence chain; performing multi-source data preprocessing and cross-modal fusion optimization; performing multi-dimensional cause intelligent analysis; performing responsibility judgment based on a quantification rule; and the responsibility judgment result is subjected to multi-dimensional rechecking and rule iteration adaptation. According to the road traffic accident cause analysis and responsibility judgment method and system, through a full-process closed-loop design of data acquisition, preprocessing, cause analysis, responsibility judgment, re-checking iteration and report evidence storage, technologies such as multi-source perception and an AI algorithm are integrated; the method solves the problems of uncredible evidence chain, one-sided cause traceability, non-uniform judgment standard, low cooperation efficiency and the like in traditional accident processing, realizes intelligence, standardization, compliance and traceability of accident processing, remarkably improves the credibility, processing efficiency and judicial suitability of a judgment result, and provides core technical support for modernization of traffic control.

The invention relates to the technical field of geological disaster early warning, and discloses a geological disaster early warning device, which comprises an acoustic electromagnetic fusion sensor array for collecting multi-physical field data related to the internal structure change of a rock-soil body; the data pre-processing and feature extraction module is used for performing pre-processing and feature extraction on the original multi-physics field data so as to identify the change features of the internal structure of the rock-soil body; the multi-physics field collaborative inversion module reconstructs the internal structure distribution of the rock-soil body by adopting a multi-physics field collaborative inversion algorithm; the structure change cause analysis and risk assessment module is used for analyzing causes of internal structure change of the rock-soil body and assessing potential geological disaster risks; the grading early warning information generation module is used for generating targeted grading early warning information; according to the invention, high-precision monitoring of the internal structure change of the rock-soil body is realized through an acoustic electromagnetic fusion sensing technology, the monitoring precision is improved, and possible landslide or collapse events can be predicted in advance.

The invention relates to the technical field of defect detection, in particular to a mold defect detection method and system based on an image recognition technology. The method comprises the following steps: acquiring a parting surface image of a target mold; performing region segmentation on the parting surface image to generate parting surface contour data; identifying a mold closing gap of the parting surface contour data, and generating a parting surface gap distribution diagram; an ejection mechanism of the mold is positioned based on the parting surface gap distribution diagram, and position coordinates of the ejection mechanism are obtained; controlling an image acquisition device to shoot a local area of the ejection mechanism according to the position coordinates of the ejection mechanism to obtain a local high-definition image of the ejection mechanism; performing ejector pin assembly segmentation on the local high-definition image of the ejection mechanism, identifying the contour of an ejector pin positioning plate and extracting coordinates of a mounting hole; according to the method, high-precision identification and cause analysis of mold defects are realized through precise positioning, refined defect classification and process simulation, and the defects of inaccurate positioning, fuzzy identification and lack of dynamic adaptation in traditional detection are overcome.

The invention provides a traffic abnormity and congestion cause analysis method and system based on a knowledge graph, and relates to the technical field of intelligent traffic perception, and the method comprises the steps: carrying out the target detection through employing preprocessed laser radar point cloud data, recognizing traffic participants, carrying out the continuous frame tracking of the traffic participants, and extracting the motion track and behavior characteristics in an event dimension; identifying a behavior event of the traffic target based on the motion trail and the behavior characteristics, binding the identified behavior event of the traffic target to a corresponding target entity, and performing target behavior modeling to form standardized structured information; based on structured information of a traffic target, an intersection-oriented traffic state knowledge graph is constructed, a rule-based abnormal event judgment module is utilized to perform semantic analysis on behavior and event nodes in the traffic state knowledge graph, abnormal traffic events are identified, and potential causes causing traffic congestion are traced. According to the invention, refined understanding and active perception of the intersection traffic state can be realized.

The invention provides a construction site pollution source analysis method and system, and the method comprises the steps: determining the atmosphere pollution source intensity information of each monitoring region of a construction site based on a preset Gaussian diffusion model, environment monitoring data and meteorological data, and carrying out the detection and recognition of a video stream through a pre-trained target detection model, the comprehensive score of the construction site in a preset time period is determined based on a preset multi-dimensional scoring model, the environment monitoring data, the detection and recognition result and the atmospheric pollution source intensity information, and the key pollution information of the construction site is determined based on the atmospheric pollution source intensity information and the satellite image data and the construction progress information of the construction site; and performing pollution cause analysis based on the environmental monitoring data and historical construction data of the construction site, and determining a pollution source analysis result of the construction site based on the comprehensive score, the key pollution information and the pollution cause analysis result. With the adoption of the method, the high-real-time pollution source analysis requirement caused by quick change of the actual site construction condition of the construction site can be met.

The invention belongs to the technical field of petroleum and natural gas development engineering, discloses a shale reservoir three-dimensional dessert prediction method based on physical-data dual drive, and solves the defects of an existing shale reservoir dessert evaluation method in the aspect of space continuity. The prediction method comprises the following steps: (1) constructing a three-dimensional attribute field, and forming a multi-channel standardized three-dimensional data volume through voxelization processing; (2) fusing multi-parameter attributes of the shale reservoir and constructing a multi-channel three-dimensional feature body; (3) constructing a dessert prediction model based on the physically constrained space-attribute joint attention three-dimensional convolutional network; and (4) calculating a comprehensive dessert index, performing dessert grading and cause analysis, analyzing and quantifying the contribution degree of each attribute to dessert grading, and analyzing the dessert connectivity and the advantage extension direction. The predicted productivity potential and fracturing potential dessert field keeps reasonable gradient change and continuous distribution in the inter-well area, and the inter-well prediction error is small.

The invention provides a bridge monitoring data abnormal value intelligent judgment and processing system, which comprises a monitoring module used for acquiring dynamic displacement data of a plurality of monitoring points of a bridge in a non-contact manner; the preprocessing module is used for performing time synchronization and quality fusion on the displacement, load and environment data to generate multi-dimensional time sequence data; the anomaly recognition module is used for extracting collaborative deformation and power fingerprint features and performing anomaly recognition and initial classification based on the feature deviation degree; the abnormality diagnosis module is used for performing abnormality cause analysis in combination with the load and the environmental condition and outputting an abnormality diagnosis result; and the strategy generation module is used for generating a grading early warning and disposal strategy according to the diagnosis result and the structural key index usage degree. The accuracy and efficiency of bridge structure safety monitoring can be improved, and safe operation of a bridge is guaranteed.

The invention provides an energy storage device damage diagnosis and interaction system based on penetration vision and a large model, and relates to the technical field of energy storage device damage diagnos.The energy storage device damage diagnosis and interaction system comprises a penetration type 3D sensing module, a periodic topology analysis module, a feature mapping and retrieval module and a large model reasoning and interaction module, and the energy storage device is scanned and reconstructed; the method comprises the following steps: constructing a time-space decoupling periodic Transform network, introducing a periodic mask matrix to force the network to pay attention to a repeatability rule of an internal structure of a battery, and identifying internal tiny deformation and structural damage by calculating topological consistency under the condition that a large amount of negative sample training is not needed; visual defect features are mapped into text embedding by utilizing a feature projection technology, a diagnostic report containing physical cause analysis and maintenance suggestions is generated by combining a retrieval enhancement generation technology and a large language model, and a user is supported to perform interactive questions and answers in a natural language. The method can solve the problems that in the prior art, three-dimensional deformation is difficult to quantify, small samples are difficult to train, and intelligent decision-making ability is lacked.

The invention relates to the technical field of energy storage system fault analysis, in particular to an energy storage system fault analysis method, which comprises the following steps: arranging a plurality of sensors, and collecting fault associated data of an energy storage system; constructing a fault analysis model, and generating a fault analysis result according to the fault associated data; detecting whether abnormal data exists in the fault associated data or not, if yes, performing fault multi-stage analysis, and obtaining fault analysis results, including obtaining a first fault analysis result and a second fault analysis result by inputting the fault associated data and the corrected fault associated data into a fault analysis model, and comparing and analyzing whether the two results are the same or not, and analyzing reasons for abnormal data in combination with sensor fault detection and historical fault analysis results to obtain a fault analysis result. According to the scheme, when the sensor data is abnormal, reason analysis can be carried out, the output of a fault analysis result is not influenced, the accuracy of fault analysis is improved, and the safety of an energy storage system is ensured.

The invention relates to the technical field of industrial equipment maintenance, in particular to an industrial equipment maintenance system for industrial internet data acquisition. The system comprises a fragmented storage database and a maintenance associated data calling module. According to the method, the associated industrial equipment is matched according to the real-time associated data through the fragmented storage database, the corresponding real-time associated data is divided, meanwhile, the influence degree of each associated data is calculated, the storage data and the storage period are planned according to the influence degrees, real-time data cleaning is performed, and normal data and abnormal data are divided to be stored in order. Normal storage of conventional data is guaranteed, real-time calling is facilitated, abnormal data can be stored in advance, when monitoring data calling needs to be carried out in the maintenance work process, the abnormal data can be preferentially supplied according to the storage priority, the abnormal reason analysis efficiency is improved, and the maintenance efficiency is improved. Meanwhile, the storage period of each piece of monitoring data and the storage of the database are reasonably planned according to the storage priority, and the storage state is improved.

The invention relates to the technical field of wind power operation and maintenance, and discloses a wind power blade crack diagnosis method and system based on knowledge graph large model enhancement, and the method comprises the steps: S1, edge end image collection and crack semantic recognition: an edge end carries out the crack detection and semantic feature extraction of a collected wind power blade image sequence through an ECL-Net lightweight network, generating structured semantic information and transmitting the structured semantic information to a cloud; s2, cloud knowledge graph retrieval and semantic reasoning: the cloud performs semantic retrieval and causal reasoning on the structured semantic information through a KFR-Net knowledge graph reasoning network based on a pre-constructed wind power blade operation and maintenance knowledge graph, and outputs a knowledge retrieval result; and S3, multi-modal information is fused through an MMR-Cogntive multi-modal memory retrieval-cognitive generation network, and a structured maintenance report including crack detection summarization, cause analysis, maintenance measures and risk assessment is generated. The method has the advantage that intelligent closed-loop management of wind power blade cracks from detection to maintenance suggestions is realized.

Multiple agent root cause analysis techniques are described. In an implementation, a processor performs operations to troubleshoot performance of a system operation by a plurality of domains (e.g., of a distributed system). The processor executes a system artificial intelligence (AI) agent to determine which domains of the plurality of domains include domain functions in support of the system operation, and to generate, using machine learning, queries to the determined domains based on the system operation. Domain responses are received from domain AI agents associated with the determined domains responsive to the queries and generated based on domain data associated with respective domains. A system response is generated by the system AI agent using machine learning based on the domain responses.

The invention discloses a hydropower station on-duty emergency processing method, system, equipment and medium based on a knowledge graph, and belongs to the technical field of hydropower station emergency processing, and the method comprises the steps: collecting the operation parameter data and historical fault information of a hydropower station, and carrying out the digital processing; based on a natural language processing and artificial intelligence training technology, constructing a knowledge graph including fault types, cause analysis and emergency disposal measures; establishing an intelligent fault diagnosis algorithm based on a knowledge graph, and carrying out fault identification and positioning on the operation data of the hydropower station; performing classification and priority ranking on the faults according to the identified fault result, and matching a corresponding processing flow; and according to the fault classification information, an emergency plan is started, a control instruction is executed to complete emergency processing operation, and recording is carried out. According to the invention, real-time sensing of the operation state of the hydropower station, accurate fault identification and priority classification are realized, and the response speed and processing accuracy of the hydropower station to emergencies are effectively improved.

The invention relates to the technical field of medical intelligent decision, in particular to an operating room nursing key node decision support system, which comprises the following steps: establishing a nursing operation reference containing a standard execution sequence and a parameter interval based on historical cases; by integrating discrete operation and continuous monitoring data in a real-time operation, a nursing process event chain with unified semantics is formed. The system compares the chain of events with an execution criterion to identify potentially risky segments and automatically extracts, for each segment, a complete operating room multi-dimensional state snapshot within a specific time window before and after its occurrence. And dynamically constructing a cause deduction network according to the state snapshots, and speculating cause chains possibly causing risks by traversing the network. And integrating all deviation information and cause analysis results, and generating a structured decision intervention prompt. According to the invention, deepening from automatic risk identification to root intelligent diagnosis can be realized, and accurate and efficient decision support is provided for operating room nursing.

The invention relates to an output power derating calculation method based on an energy storage charging pile. The method comprises the following steps: acquiring configuration data of an energy storage charging pile; calculating output power derating coefficient parameter information of a charging gun of the energy storage charging pile according to the configuration data; if the output power derating coefficient parameter information is lower than a preset derating reason analysis threshold value, acquiring state monitoring data of the energy storage charging pile; inputting the state monitoring data into a derating reason analysis model, performing derating reason analysis, and generating output power derating reason parameter information; and generating output power derating analysis result information of the charging gun of the energy storage charging pile based on the output power derating reason parameter information. By adopting the method, the power derating state and reason of the energy storage charging pile can be accurately distinguished and rapid intelligent diagnosis can be realized through the derating coefficient parameter and the derating reason parameter, the operation and maintenance cost of the charging pile can be obviously reduced and the fault can be accurately positioned, so that the intelligent and efficient upgrading of charging infrastructures is promoted.

The invention discloses an automatic driving traffic accident intelligent analysis system based on sand table simulation, and relates to the technical field of vehicle automatic driving. The system comprises a sand table simulation scene construction module which is used for simulating a high-risk scene of an automatic driving traffic accident and providing a physical and digital twinborn combined experiment environment; the automatic driving data acquisition and perception module is used for acquiring vehicle sensor data in real time and realizing environment perception and behavior decision simulation; the traffic accident intelligent analysis core module is used for realizing accident cause analysis and responsibility determination based on multi-source data and a knowledge base; and the cloud control platform and data visualization module is used for monitoring sand table simulation data in real time and providing a visual interface and an interaction function. The system can simulate and analyze the performance of automatic driving in various complex traffic environments and perform analysis and prediction, the analysis and prediction accuracy is high, and the analysis effect is better.

A communication method and a device. In the method, a network side can perform cause analysis on a secondary cell group (SCG) failure of a terminal device based on SCG failure information reported by the terminal device, and determine a problematic node for wrong candidate primary secondary cell selection, so that configuration optimization on the network side can be completed, and more proper configuration can be performed for the terminal device in a subsequent primary secondary cell addition or change procedure of the user equipment (UE), to reduce a probability of the SCG failure of the terminal device.

The invention relates to the technical field of mining area safety monitoring, in particular to a mine mining subsidence real-time monitoring method and system.The method comprises the steps that a target mining influence factor set and a reference mining area data set of a target monitoring mining area are obtained, and a retrieval mining area data set is confirmed in the reference mining area data set based on the target mining influence factor set; obtaining an initial accident area set by using the retrieved mining area data set and a pre-constructed accident cause analysis method, determining a reference subsidence area sequence based on the initial accident area set and the retrieved mining area data set, and obtaining a target monitoring image of the reference subsidence area, and obtaining a monitoring frequency based on the target monitoring image and the bit sequence of the reference subsidence area in the reference subsidence area sequence, and obtaining a reference subsidence early warning report by utilizing the monitoring frequency and monitoring data obtained by the target monitoring mining area and utilizing the data analysis unit, the target monitoring image and the monitoring data. According to the invention, the accuracy and timeliness of monitoring the mining subsidence of the mining area can be improved.

The invention provides a real estate operation engineering construction progress intelligent supervision system based on digital twinning, which belongs to the technical field of engineering construction progress supervision and comprises a data acquisition unit, a data identification unit, a data twinning unit, a progress identification unit, a reason analysis unit, a progress prediction unit and a visual supervision and management unit. According to the invention, the progress risk point can be predicted 3-7 days in advance, the project period deviation is reduced by 37% in actual measurement, the construction cost is reduced by millions of millions, a manager can supervise the progress of the construction process and non-standard behaviors in the construction process in real time, real-time remote supervision is realized, and the manager can make a decision in advance by predicting the progress, so that the construction site construction can be better managed.

The invention discloses a land subsidence area permeable channel identification and cause analysis method based on a high-density electrical method, which overcomes the limitation of traditional single profile data interpretation by arranging a three-dimensional observation grid and fusing and collecting multi-source data. Through combination of finite element three-dimensional inversion and the improved convolutional neural network model, accurate description of the three-dimensional spatial morphology, the extension direction and the connectivity of the permeable channel is realized, and the accuracy and the spatial resolution of low-resistance abnormal region identification are improved. Compared with the prior art, the method has remarkable advantages in the aspects of permeable channel recognition accuracy, cause analysis comprehensiveness, result application practicability and the like, and effective support can be provided for long-acting prevention and control work of geological disasters in land subsidence areas.

The invention relates to the technical field of refrigerating machine room operation and maintenance, in particular to a refrigerating machine room operation sub-health state recognition method based on reinforcement learning. Preprocessing the data; constructing a reinforcement learning model; performing hyper-parameter optimization; training a reinforcement learning model; and real-time monitoring and early warning are realized. The reinforcement learning algorithm is combined with Bayesian optimization to optimize hyper-parameters, the complex relation between equipment operation parameters is learned more accurately, the system collects and analyzes data in real time, captures subtle changes of the equipment state in time, early warns potential faults in advance, strives for sufficient processing time for operation and maintenance personnel, adapts to the complex and changeable operation environment of the refrigerating machine room, and improves the working efficiency of the refrigerating machine room. The sub-health state of the equipment is found in advance, sudden faults of the equipment are avoided, the overall operation and maintenance cost is reduced, the hyper-parameter optimization efficiency is improved, after the sub-health state of the equipment is recognized, state information is pushed, related reason analysis is provided, closed-loop management is formed based on operation and maintenance feedback, and the overall operation and maintenance level of the refrigerating machine room is improved.

The invention relates to an intelligent test method for a wind power plant network-related test. According to the technical scheme, the method comprises the following steps that firstly, an intelligent test database is constructed, secondly, an intelligent control unit calls a scene simulation module, a combined scene of'basic working conditions + coupling working conditions' is automatically generated based on the database, test equipment and a wind power plant control system are connected through a standardized interface, network-related parameters are collected at the frequency of 50-100 ms / time, and the network-related parameters are obtained. The data processing module adopts a CNN-LSTM hybrid model to analyze and collect data, the intelligent control unit monitors the voltage and frequency of a power grid and the output power of a wind power plant in real time, when parameters exceed safety threshold values, graded protection measures are triggered, and a test report including grid-related performance scores, abnormal parameter curves, fault reason analysis and optimization suggestions is automatically generated. The method has the beneficial effects that the working condition configuration time and the data processing time are effectively shortened, the overall test efficiency is effectively improved, the deviation between a test result and an actual power grid scene is reduced, and the fault positioning accuracy is improved.