669 results about "Smart grid network" patented technology

The invention relates to the technical field of smart power grids and artificial intelligence, in particular to a substation autonomous intelligent inspection method and system based on multi-modal data, and the method comprises the steps: obtaining inspection data of multiple modals, and generating fusion features; identifying system alarm information; performing intention recognition and task classification to generate an executable task sequence; generating a multi-device cooperative scheduling scheme; executing the multi-device cooperative scheduling scheme; iterative optimization is carried out; according to the intelligent inspection method provided by the invention, more accurate and more robust multi-mode perception and diagnosis are realized, and deep understanding of complex instructions and safe and efficient cooperation of multiple devices are also realized; and meanwhile, through dynamic re-planning and a verification type feedback learning mechanism, high real-time performance and robustness are ensured, and meanwhile, the system is endowed with the capability of iterative optimization.

The invention discloses a micro-grid power control method, system and device based on fuzzy modulation and a storage medium, and relates to the technical field of intelligent grid integration of a photovoltaic system and a micro-grid, and the method comprises the steps: firstly obtaining the real-time power data of a photovoltaic system, carrying out the control processing through fuzzy logic, and calculating a reference instruction; according to the reference instruction, hysteresis time is dynamically adjusted through modulation hysteresis control, and an inverter switching signal is generated; converting direct current into alternating current based on the switching signal and a power switching device, injecting the alternating current into the micro-grid, calculating active and reactive power reference values, ensuring that the system operates according to a unit power factor, and giving active power to the reference value; according to the method, the power control precision and adaptability of the micro-grid are improved, excessive switching of the inverter is avoided, loss is reduced, the service life is prolonged, power matching and stable transmission are achieved, reactive loss is reduced, bus voltage is stabilized, harmonic waves can be reduced, the electric energy quality is improved, and efficient and stable operation of the micro-grid is guaranteed.

The invention provides an intelligent power grid energy optimization management method and system, and relates to the technical field of data processing, and the method comprises the steps: 1, synchronously collecting photovoltaic output fluctuation data, user load demand data and power grid frequency state data, so as to obtain an original monitoring data set; step 2, performing spatial-temporal feature mapping on the original monitoring data set, extracting a multi-dimensional feature vector, determining a dominant feature shaft system based on covariance analysis, and calculating a dynamic coupling degree between the feature shaft systems to divide a regulation and control domain; time sequence key characteristic quantities are selected in a regulation and control domain, a load response state transition path is constructed, a user behavior correction coefficient is deduced according to the load response state transition path, dynamic load balance is achieved through a multi-period rolling strategy, and a load distribution scheme is generated. According to the invention, through real-time dynamic regulation and control of power supply output, an excitation electricity price signal is generated and user demand response is linked, so that intelligent power grid energy supply and demand balance optimization is realized.

The invention discloses an urban energy network intelligent allocation method and system, and the method comprises the steps: obtaining building energy consumption data and energy supply network topology information, building a supply and demand matching reference, recognizing a load peak value dense time period and a peak clipping and valley filling potential region, and exporting transferable load data to form a regional supply and demand distribution diagram; generating a peak shifting adjustment space in combination with the user response delay duration and the equipment start-stop period; performing gap matching analysis on the regional supply and demand distribution map and the peak shifting adjustment space, creating a hierarchical regulation instruction set, and differentially generating an instant response instruction and a delay response instruction; a regulation and control coordination parameter is formed through coordination configuration of the peak shifting revenue coefficient and the instant response instruction; finally, a regulation and control execution range is determined, cost benefit evaluation is implemented, an urban-level energy scheduling execution scheme is formed, accurate matching and efficient scheduling of urban energy supply and demand are achieved, and comprehensive and efficient scheduling decision support can be provided for application scenes such as an intelligent power grid, regional heat supply and comprehensive energy service.

The invention discloses a high-temperature-resistant composite insulated cable for an intelligent power grid, and belongs to the technical field of cables. The objective of the invention is to solve the problems of single function, poor high-temperature resistance and easy overload failure of the existing cable. The cable sequentially comprises a compressed circular tinned copper conductor, an inner shielding layer, an XLPE inner insulating layer, an outer shielding layer, a self-repairing composite insulating layer, a metal shielding layer and an outer sheath layer from inside to outside, the self-repairing composite insulating layer is prepared by blending a self-repairing polymer matrix and a microencapsulation phase change material, and the melting point of the microencapsulation phase change material is accurately matched with the double dynamic bond response temperature of the self-repairing matrix. Through cooperation of self-repairing and thermal buffering functions, integration of multiple functions of high temperature resistance, self-repairing and thermal buffering is achieved, the long-term working temperature reaches 105 DEG C or above, no breakdown occurs during thermal shock at 180 DEG C, the overload repairing rate exceeds 80%, the high-temperature and overload working conditions of an intelligent power grid can be effectively adapted, the preparation steps are clear and reproducible, and remarkable technical advantages and application value are achieved.

The invention discloses an electric power field credible question-answering system based on a knowledge graph and multiple agents, and belongs to the technical field of intelligent power grids. The electric power field credible question-answering system comprises the steps that an existing knowledge base is stored through a vector knowledge base, the knowledge graph and a relational database, and then a multi-agent system is constructed based on a large language model; a complex problem is decomposed into a plurality of sub-problems by adopting a logic form language in real-time dialogue, solving is carried out based on operators such as planning, reasoning and retrieval, symbolization and structuring of a problem solving process are realized, knowledge is aligned and integrated by using a semantic reasoning technology, a semantic relationship between the knowledge is identified and established, and a real-time dialogue result is obtained. And realizing credible questions and answers in a manner of recalling related knowledge. According to the method, the deep fusion of the unstructured text, the structured data and the semantic relationship is realized by constructing the multi-modal knowledge storage system, the knowledge coverage rate and the semantic consistency are remarkably improved, and the understanding and response capabilities of the system to compound problems are improved.

The invention relates to the technical field of intelligent power grids, in particular to an urban power distribution network whole-process scene chain closed-loop monitoring method and system fusing digital twinning. The method comprises the following steps of: fusing a weighted evidence theory and a dynamic time bending algorithm to acquire multi-source data, and constructing a physical mechanism-data driven hybrid enhanced digital twinborn model; based on the model, abnormity is researched and judged through an improved isolated forest algorithm, grading alarms are generated, simulation deduction is conducted through Monte Carlo-Latin hypercube sampling, an optimal strategy is screened through a non-dominated sorting genetic algorithm III with uncertainty, and instructions are executed after graph neural network topology verification. Feedback is formed by the collection result, and the strategy is optimized through meta-learning and Bayesian optimization calibration model. The system comprises six cooperation units, an intelligent algorithm and a digital twinning technology are integrated, the power distribution network abnormity identification precision, decision scientificity and adaptive capacity are improved, and safe and efficient operation is guaranteed.

The invention discloses a fault prediction method and system for a transformer, and belongs to the technical field of transformer state monitoring and fault prediction. The technical problems that an existing transformer fault prediction method is low in accuracy due to single data modality, insufficient in model generalization ability due to scarcity of real fault data, lack of an adaptive mechanism and the like are solved. According to the technical principle, gas concentration, vibration spectrum, temperature, sound and thermal imaging data are collected through a multi-mode sensor array deployed on a transformer site; a multi-head attention mechanism is adopted in the edge calculation unit to realize multi-source data fusion, and fault prediction is carried out through an LSTM-Attention hybrid model; multi-node model parameters are aggregated through federal learning at the cloud, and model adaptive optimization is realized in combination with reinforcement learning. According to the system, the prediction accuracy is improved, the delay is reduced, training data is expanded by five times through the generative adversarial network, distributed deployment and privacy protection are supported, and an efficient transformer state early warning solution is provided for an intelligent power grid.

The invention provides a wind power prediction method based on improved Diffusion-Q. According to the method, a diffusion model and a Q-learning reinforcement learning mechanism are deeply fused through a data preprocessing module, a diffusion model forward process, a Q-learning module, a diffusion model reverse process, an output prediction module and a feedback and optimization module; a generative prediction system with a dynamic strategy optimization capability is constructed, a future wind power sequence is generated through probability distribution of modeling time sequence data in a forward diffusion process and a reverse diffusion process, online dynamic adjustment of diffusion process parameters is realized, and dynamic prediction of the wind power sequence is realized by defining a state space, an action space and a reward function. The model can automatically select the optimal sampling path according to current input and historical performance, prediction precision and robustness are remarkably improved, and the method can be widely applied to wind power plant short-term power prediction, power dispatching system and intelligent power grid energy management and has good engineering application prospects.

The invention relates to the technical field of intelligent power grid network security, in particular to an adaptive scanning concurrency number adjusting method under micro-grid isolation, which comprises the following steps: S1, respectively deploying load monitoring nodes in a control area, a non-control area and a management information area, collecting service load and network state data of each area in real time through the load monitoring node; and S2, based on the collected service load and network state data, constructing a concurrent number calculation formula, and through dynamic concurrent adjustment driven by the real-time load, the vulnerability scanning efficiency is ensured, and the service operation of each region of the micro-grid is prevented from being interfered.

The invention relates to the technical field of intelligent power grid operation monitoring and state recognition, and discloses a method, a system and equipment for sensing the state of a high-proportion photovoltaic regional power distribution network based on a heterogeneous graph neural network and a medium. The limitation of a traditional method in processing complex network topology, multi-source heterogeneous data and disturbance driving behaviors is broken through. In the state sensing process, from acquisition of multi-source data, a series of preprocessing, construction of a reasonable graph structure, extraction of node embedding vectors and output of state labels and abnormal scores are performed by using a preset state recognition model, and all links are closely connected and have innovativeness. And key nodes or regions with potential risks or abnormal states in the power distribution network are identified through the abnormal score function, and state online sensing and risk early warning of the power distribution network containing the photovoltaic region are realized.

The invention discloses a power supply service intelligent management system, and relates to the technical field of intelligent power grids, and the system comprises a power grid holographic sensing module which obtains multi-dimensional unstructured data of a power system, extracts core features through cross-domain fusion analysis, and generates a power grid real-time operation state map; the equipment health twinning module is used for constructing an equipment health model in combination with a digital twinning technology and outputting an equipment fault early warning signal and a residual life prediction value; the customer energy efficiency service module is used for analyzing the association relationship and then generating a personalized energy efficiency optimization scheme and a power utilization suggestion; and the dynamic scheduling decision module is used for generating a power grid optimal scheduling strategy and an emergency response scheme. The power grid holographic sensing, digital twinning technology, dynamic scheduling, block chain and encryption technology are utilized, the power system is monitored in real time, equipment health is evaluated, faults are predicted, power utilization, energy conservation and emission reduction are optimized, data safety and stable operation of the system are ensured, and the cooperation efficiency of power enterprises and users is improved.

The invention discloses a multi-power-grid cooperative regulation and control method and system based on multi-level resource aggregation decoupling, and belongs to the technical field of intelligent power grid operation control. The method comprises the following steps: determining a time-varying feasible boundary of a microgrid in a power regulation domain of a common connection point by acquiring multivariate state information of heterogeneous resources of the microgrid; determining a power bidirectional interaction boundary constraint between the micro-grid and the power distribution network through the common connection point, and constructing a micro-grid power distribution optimization model according to the power bidirectional interaction boundary constraint and the electric energy characteristics of the power distribution network; dividing resource adjustment levels corresponding to the heterogeneous resources to obtain a multi-level adjustment sequence; when the micro-grid power distribution optimization model outputs a scheduling instruction, the scheduling instruction is decomposed into optimal scheduling strategies corresponding to the levels according to the multi-level adjustment sequence, and the optimal scheduling strategies are issued to the levels in sequence, so that the adjustment instruction issued by the power distribution network is decomposed in sequence according to the adjustable characteristics of heterogeneous resources, and the optimal scheduling strategies are obtained. The resource adjustment efficiency is improved; and the response cost is optimized.

The invention is suitable for the technical field of smart power grids, and relates to a low-voltage transformer area undisturbed topology identification method and system, a medium and a terminal, and the method comprises the steps: S10, cleaning and screening original communication and measurement data; s20, the RSSI between the devices is converted into the distance between the nodes, and an undirected weighted graph is formed; s30, determining an affiliation relationship between the meter box and the electric meter by using a clustering algorithm; s40, performing constraint matching on the transformer, the line and the meter box node according to the power utilization information, and optimizing the topological structure by using the law of conservation of energy and the Kirchhoff's current law; and S50, evaluating the accuracy of the identification result by using the adjusted Lam index and the normalized mutual information. The method is simple in process and convenient to operate, a complete topological structure is constructed step by step on the premise of not adding extra hardware, uninterruptible power and not injecting characteristic signals by relying on electric power data of an existing intelligent electric meter, a communication signal and an electricity utilization information acquisition system, and high-precision and automatic identification of the topological structure of the transformer area is achieved.

The invention relates to a hierarchical fine net load prediction and lean distribution network planning system, and belongs to the technical field of smart power grids. Comprising a load prediction engine which generates net load curves of a transformer area, a feeder line and a transformer substation according to a spatial hierarchy; the planning decision platform outputs a grid optimization scheme based on the prediction result; the dynamic feedback module is used for collecting operation data in real time to correct the prediction and planning model; according to the invention, the net load prediction error is reduced to below 7%; the utilization rate of distribution network equipment is increased to more than 65%; and the new energy consumption rate is increased by 15-30%. The invention relates to an integrated system integrating multi-level net load prediction and dynamic lean planning.

The invention relates to the technical field of intelligent power grids, in particular to an intelligent generation method and system for a substation switching operation ticket, and the method comprises the steps: constructing a weighted topological graph with switching equipment as nodes and electrical connection as edges according to a power line of a substation; assigning weights to the edges of the weighted topological graph; wherein the weight of the edge at least comprises direct operation cost and power grid disturbance cost; receiving a switching operation target task, and determining a search starting point and an end point in the weighted topological graph based on the target task; performing path search in the weighted topological graph by adopting an improved ant colony algorithm determined on the basis of a weight and an adaptive volatility coefficient based on the search starting point and the search ending point to obtain an optimal path; and converting the optimal path into a switching operation switch action sequence, and generating a switching operation ticket. Through the improved ant colony algorithm, the method comprehensively considers the number of operation steps and the impact influence of power grid operation, and facilitates the determination of a global comprehensive optimal path.

The invention provides a power transmission line bird damage risk assessment and early warning method and system, and relates to the technical field of intelligent power grid environment perception and risk early warning, and the method comprises the steps: data collection and multi-modal fusion: collecting image data, voiceprint data, meteorological parameters and ecological environment information along a power transmission line to form multi-modal data, the multi-modal data is uniformly coded by synchronizing timestamps and geographic coordinates, an original bird activity sample set is generated, and for the problems that the monitoring means is single and the recognition precision is insufficient, image, voiceprint, weather and ecological information are fused through a multi-modal data acquisition module, so that the recognition precision is improved. The acousto-optic image fusion algorithm and the two-channel convolutional neural network are adopted, all-weather bird type and behavior accurate recognition is achieved, the recognition accuracy is improved, key behaviors such as nesting and dung pulling can be stably captured even at night or in a foggy environment, and potential risk missing detection is remarkably reduced.

The invention relates to the technical field of power grid data privacy protection, in particular to a smart power grid data privacy protection system and method based on a homomorphic encryption technology. The system protects data privacy of an intelligent power grid based on a homomorphic encryption technology, and comprises eight modules: a data acquisition module is connected with an intelligent electric meter in a multi-protocol manner in a 15-minute period to adopt electric data; the data filtering and preprocessing module optimizes data quality; the homomorphic encryption module generates a ciphertext capable of being added and multiplied by using an FHE algorithm; the multi-level key management module manages multiple types of keys in the whole life cycle; the secure transmission module transmits the ciphertext by means of a hybrid protocol; the data analysis module decrypts and analyzes the ciphertext in the data center; the decryption module decrypts after authorization; and the auditing and monitoring module records operation and monitors abnormity. According to the invention, it is ensured that the power use data is kept in an encrypted state in the whole life cycle of collection, transmission and analysis, the data is available and invisible, and the data privacy protection level and quantum attack resistance of the smart grid are improved.

The invention discloses a low-voltage transformer area electric energy meter time synchronization method and system based on swarm intelligence, and relates to the technical field of intelligent power grid and power distribution automation. The method comprises the steps that a transformer area acquisition terminal issues reference time, and each electric energy meter collects neighborhood information through neighbor electric energy meter bidirectional message exchange; jointly estimating the clock skew and the drift rate by adopting a consistency algorithm; introducing a time-varying weight calculated according to SNR, a message success rate and historical stability to suppress the influence of an inferior link and an abnormal node; the virtual synchronization time is output through software compensation on the service side; and adjusting the synchronization period interval and the convergence step length in a linkage manner according to a double-threshold self-adaptive rule. According to the scheme, deviation and drift rate rapid convergence and stable noise suppression are realized in a time-varying and easy packet loss environment, communication energy consumption is reduced, time jump caused by hardware callback is avoided, and the method has the advantages of high precision, strong robustness and large-scale deployment.

According to the electric power data prediction method based on large language model auxiliary knowledge graph construction provided by the invention, a high-coverage electric power knowledge graph is automatically constructed through a large language model, so that the problems of low knowledge acquisition efficiency and insufficient semantic relationship recognition accuracy of a traditional method are solved; through a multi-path causal attribution mechanism, a multi-hop and multi-relation path in the knowledge graph is effectively converted into a prior adjacency matrix which can be utilized by a neural network, and structured fusion of domain knowledge is realized; through the synergistic effect of the time sparse attention and the mixed adjacency matrix, the modeling precision of the power data space-time dependency relationship is remarkably improved, and an important technical support is provided for precise scheduling and optimized operation of a smart power grid.

The invention relates to a smart power grid space-time scheduling method based on a self-adaptive dynamic graph neural network, which comprises the following steps of: collecting time sequence data from distributed data nodes of a smart power grid in real time, and preprocessing the data; self-generating an adaptive adjacency matrix, and adjusting an edge weight in the adjacency matrix according to the change of real-time data by a self-adaptive algorithm; performing spatio-temporal feature extraction on the generated adaptive adjacency matrix through a spatio-temporal convolutional network STCN, capturing a complex spatio-temporal dependency relationship between power grid nodes, obtaining a feature graph containing a spatial and temporal dependency relationship, and representing the operating states and mutual dependency conditions of different time nodes of the power grid by the features; real-time scheduling optimization: inputting the extracted spatial-temporal characteristics into a scheduling optimization module, and calculating the optimal matching between the load demand and the power generation capacity; the node state is continuously monitored in the scheduling process, and the scheduling strategy is dynamically adjusted. Distribution of electric power resources is dynamically adjusted, and it is ensured that electric power supply of the load center meets requirements.

Provided is an interposer comprising: one or more photonic waveguide; one or more microwave circuit; one or more optical interconnect; and one or more electronic interconnect, further configured to support a quantum sensor, and methods of operating and fabricating thereof. Further provided is a the interposer containing one or more classical sensors. Further provided is a network of quantum sensors supported by said interposers.

The invention belongs to the technical field of power system automation control, and particularly relates to a low-voltage power distribution cabinet intelligent control system applied to an intelligent power grid, which comprises a multi-dimensional sensing module, an edge calculation and decision center and an execution and communication interface module. A multi-time-scale adaptive control mechanism adopted by the system separates protection, optimization and reconstruction decisions in different periods, the problems of response lag and target conflict of a single control loop are effectively solved, and the quick response capability and the adaptive adjustment capability of the system in a dynamic load and distributed energy access scene are remarkably improved. And the operation and maintenance mode is converted into predictive maintenance from post-maintenance through a deep fusion equipment health state evaluation and prediction function, so that the risk of unplanned shutdown is greatly reduced.

The invention discloses an intelligent power grid task autonomous planning method and system based on multi-agent collaboration, and the method comprises the steps: S01, carrying out the intention recognition according to user input information, carrying out the task decomposition based on the recognized user intention, and generating a subtask sequence; s02, task allocation is performed according to the states of all the available agents, and task allocation is adjusted according to the execution state of each agent; step S03, executing each subtask by each agent according to a task allocation result, and verifying and feeding back an execution result of each agent; and step S04, collecting multi-source feedback information in the task execution process by adopting an inverse planning strategy, and adjusting the subtask sequence according to the collected multi-source feedback information. According to the invention, autonomous planning, intelligent scheduling and efficient execution of complex power grid business tasks can be realized, and the intelligent level and autonomous response capability of the power distribution network can be significantly improved.

The invention relates to the technical field of the intelligent power grid industry, in particular to a power distribution and utilization ring network fault intelligent troubleshooting method and system. According to the method, a looped network situation primitive is generated through multi-source heterogeneous data fusion. The primitives are input into synchronous compression transformation to extract transient fault initial features, and the transient fault initial features are mapped into fault propagation path features through a graph convolutional network considering topological structure constraints; then, inputting the features into a space-time correlation prediction model, integrating defect type judgment and trend pre-judgment features, and generating a fault diagnosis evidence set; and starting an investigation mechanism based on the evidence set to determine an interval to be investigated, and performing closed-loop verification by constructing a fusion judgment matrix based on multi-modal physical sensing information to realize physical space locking of a fault defect point. According to the method, deep learning diagnosis and multi-mode positioning are integrated, and the detection precision and the positioning efficiency of looped network defects and the identification capability of transient hidden faults are effectively improved.

The invention belongs to the technical field of smart power grid information security, and provides a privacy protection system and method based on smart power grid distributed data aggregation. The method comprises the following steps: constructing a privacy core, a power grid state and a privacy grading data stream of an electric vehicle charging pile network; generating a one-time session blind factor by using the power grid interference characteristics to perform data encryption; channel interference is evaluated based on the power grid state, dynamic verification is carried out on the ciphertext, and interference confidence is generated; executing anti-interference aggregation in a ciphertext state according to the privacy grade and the credibility score; and finally, carrying out differential privacy enhancement on an aggregation result, and outputting security data capable of directly driving power grid services. According to the method, dynamic binding of the secret key and power grid disturbance, elastic fault-tolerant aggregation based on interference perception and differential privacy enhancement fusing data classification and credibility evaluation are realized, so that the problem of balancing privacy protection and data availability in a plug-and-play scene of the electric vehicle is solved.

The embodiment of the invention provides a cable well fault early warning method, device and equipment, a medium and a product, and is applied to the technical field of smart power grids. The method is applied to a cable well fault early warning system, the system comprises a plurality of sensors, a lower computer and an upper computer, and the method comprises the following steps: collecting monitoring data of the plurality of sensors in a cable well and environment data corresponding to the cable well based on the lower computer; when the monitoring data and the environment data meet a preset adjustment condition, updating an initial early warning value of each sensor based on the monitoring data and the environment data to obtain an updated target early warning value; performing abnormality verification on each sensor based on the monitoring data and the target early warning value to obtain an abnormality verification result; and sending the exception verification result to an upper computer, and sending early warning information based on the upper computer when the exception verification result indicates that an exception exists. The method achieves the technical effect of improving the fault early warning precision of the cable well.

The invention discloses a power distribution network master station fault positioning method and system based on multi-dimensional information fusion, and relates to the technical field of intelligent power grids, and the method comprises the steps: obtaining FTU electrical quantity data, circuit breaker monitoring terminal switching value data and fault indicator state signal data; constructing a power distribution network structure matrix D in a normal operation state, and generating a fault information matrix G based on the state signal data; performing normalization processing on the electrical quantity and switching value data, and converting the electrical quantity and switching value data into a time axis image; inputting the image sequence into a convolutional neural network for deep learning, extracting fault features and outputting an inter-node fault initial probability matrix Q; and fusing Q, D, G and three types of original data, performing multi-dimensional information fusion by adopting a D-S evidence theory, generating a final fault probability matrix S, and outputting a fault section and type with the highest probability. According to the method, CNN feature extraction is guided by a topological matrix D / G, and the misjudgment rate of fault positioning is reduced through D-S fusion of electrical features, topological association and equipment state weights.

This invention provides an adaptive network management system for mesh networks, utilizing advanced wake-up mechanisms that include AI-driven predictive algorithms, adaptive transmission protocols, multi-layer verification processes, and device-specific wake-up profiles. The system is designed to improve network efficiency, reduce latency, and enhance energy management by selectively waking up devices based on real-time conditions, predefined schedules, or a combination of both. This system is applicable to a variety of fields, including military communications, industrial automation, and smart grids, where reliable and efficient network management is critical.

The invention relates to the technical field of smart power grids, in particular to a smart power grid fault real-time diagnosis system based on digital twinning. The system comprises a multi-point detection arrangement unit, a multi-fault feedback unit and a virtual digital twinning unit. Through cooperation of multi-point detection, dual fault feedback and digital twinborn mapping, high-precision real-time diagnosis of smart power grid faults is realized, a microscopic clustering and macroscopic verification combined mechanism is adopted, true and false faults are effectively distinguished, and islanding effect misjudgment is inhibited; reverse reasoning and pseudo-cluster filtering are introduced, so that the robustness of the system is improved; the fault situation is dynamically rendered based on BIM + GIS, visual operation and maintenance are supported, the problems that in traditional diagnosis, single-point misinformation is difficult to eliminate, and large-range positioning is delayed are solved, and the fault recognition accuracy and the power grid operation reliability are remarkably improved.