213 results about "Real time analytics" patented technology

The invention discloses a grassroots power supply enterprise compliance risk intelligent system and method based on big data analysis. The data acquisition unit is used for acquiring business operation data, historical violation records and policy and regulation update data of basic power supply enterprises to form a unified compliance data set. And the natural language processing unit performs text word segmentation and correlation analysis on the policy and regulation and violation record data, extracts key risk factors and labels compliance risk labels. And the risk feature construction unit performs multi-dimensional feature fusion on the business operation data and the compliance risk label data to generate a feature matrix for risk identification. And the intelligent risk assessment unit performs real-time analysis on the feature matrix by using a pre-trained machine learning model, identifies compliance risk categories and levels, and generates early warning information. According to the method, the accuracy of compliance risk identification is improved by using big data analysis and an intelligent algorithm, the compliance management cost of basic-level power supply enterprises is reduced, and the operation safety and compliance of the enterprises are improved.

The invention relates to the technical field of network security, in particular to a network security analysis method and system based on big data. Comprising the following steps: collecting related multi-source heterogeneous data of a network, and carrying out standardized processing such as cleaning and de-noising; network analysis is carried out based on the preprocessed data, network traffic is analyzed in real time by using machine learning and deep learning algorithms, and abnormal conditions are detected; constructing a risk prediction model according to a network analysis result and related information, and predicting a future network security risk level; if the risk level exceeds the threshold value, determining a security event source and a responsibility subject through data tracing; and finally, generating a safety response strategy according to risk prediction and data traceability results, and performing disposal. The corresponding system covers the modules of data acquisition, preprocessing, network analysis, risk prediction, data tracing, security response and disposal and the like, and all the modules work cooperatively to form a complete network security analysis and guarantee system, so that the stable operation of the network system is guaranteed.

The invention discloses a fault analysis method and system for computer room equipment, and particularly relates to the technical field of computer room equipment. Accurate fault traceability and equipment positioning are realized by acquiring operation data of machine room equipment, analyzing an abnormal data mode in real time by utilizing a machine learning model, generating preliminary fault early warning and adopting a multi-source data fusion technology in combination with a historical fault database and a correlation analysis algorithm, and meanwhile, based on an equipment topological relation and service correlation analysis, the fault positioning accuracy is improved. Quantifying the influence range of the fault on the whole machine room system, calculating the influence level, and automatically generating an optimized fault processing scheme in combination with a fault case library; after the fault is processed, the equipment is continuously monitored, whether the fault is thoroughly eliminated or not is verified, and stable operation of the system is ensured; according to the method, the fault detection accuracy, the traceability and the repair efficiency of the computer room are effectively improved, the risk of false report and missing report is reduced, and the service continuity and the data integrity are improved, so that the intelligent level of operation and maintenance of the computer room is enhanced.

The invention relates to a data security processing method and system based on distributed storage, and relates to the technical field of computer information processing. The method comprises the following steps: cutting data into encryption fragments with a configurable number by adopting a dynamic fragmentation strategy, and generating a physically isolated dynamic check block in combination with a timestamp to realize tampering prevention; a dynamic threshold value is dynamically calculated based on the data sensitivity index and the node load, and the node is optimized through the reliability score for cooperative decryption; a database table is divided into independent marshalling storage according to main foreign key association, foreign key fields are encrypted by adopting cross keys, and cross-marshalling access needs to meet a multi-key threshold condition; an intelligent threat perception engine is constructed, access logs and threat intelligence are analyzed in real time, and key rotation, fragment replacement and joint defense response mechanisms are dynamically triggered. According to the invention, full life cycle protection of data is realized, and the problems of key leakage risk and cross-table association attack are effectively solved.

The invention discloses a data access permission control method and system based on a block chain, and relates to the technical field of block chains, the system is composed of a plurality of function modules, and the system comprises a hierarchical permission module which adopts a role hierarchical strategy and defines the access granularity of a role through an intelligent contract; the dynamic permission adjustment module is used for dynamically adjusting the permission range by analyzing user behaviors in real time through machine learning according to the principle of following the minimum permission in the permission distribution process; attribute encryption and zero-knowledge proof are introduced, an access key is dynamically generated according to user attributes, and sensitive information is hidden in the verification process; the multi-chain collaboration module is used for coding an intelligent contract through an authority rule, automatically executing access request verification, storing encrypted data through a private chain, managing an index and an access strategy through an alliance chain, isolating a storage domain from an access domain, and carrying out process traceability on a data request through a cross-chain verification mechanism; and the key management module is used for generating a true random number private key by adopting a hardware security unit.

The invention relates to the technical field of data processing, and discloses a financial compliance risk assessment method and system based on big data, and a storage medium. The method comprises the following steps: collecting and standardizing multi-source data to form a structured data set; constructing a three-level evaluation system comprising risk fields, subclasses and indexes; training the stacked fusion network to obtain an evaluation model; analyzing and generating a risk report in real time; identifying risk points and performing quantitative evaluation; and an early warning threshold value is set, and an early warning signal is generated and sent to a risk management department when the threshold value is exceeded. According to the invention, efficient identification, accurate evaluation and timely early warning of compliance risks are realized, and the automation level and scientific decision-making ability of compliance risk management of financial institutions are improved.

The invention discloses an urban building three-dimensional automatic modeling and visualization method, and belongs to the technical field of building three-dimensional modeling. The method comprises the steps that point cloud data, high-resolution images and geographic information system data of urban buildings are acquired, data cleaning, registration and alignment are carried out, and preliminary building digital representation is formed; accurately segmenting each building, and identifying the contour and main structural features of the building; based on the data integrity and the building complexity, adaptively selecting a proper reconstruction strategy to carry out three-dimensional reconstruction; in the reconstruction process, the geometric structure is analyzed and optimized in real time, and potential topological problems are repaired; automatically generating missing details based on a predefined architectural style library and a component library, and performing material inference and texture mapping; a graph structure is used for representing the relation between the buildings, and the positions and orientations of the buildings are adjusted through a global optimization algorithm; a rendering engine supporting multi-level detail switching is developed, and smooth visualization and interaction of a large-scale city scene are achieved.

The invention provides an online monitoring method and system based on a power transmission line, and relates to the technical field of power transmission line monitoring. According to the method, the heterogeneous sensing terminal, edge calculation, the graph neural network and Bayesian reasoning are combined, multi-source data acquisition, state identification and risk prediction are realized, the fault diagnosis accuracy and the risk early warning capability are improved, and the intelligent level and the safety guarantee capability of power transmission line operation are enhanced; the monitoring data is analyzed in real time through an edge calculation unit to generate a state label, a potential fault mode is recognized by combining graph neural network modeling space-time relevance, a risk factor library is further constructed, and a real-time fault probability graph is generated based on a Bayesian network. And dynamic identification and early warning of risk types such as wire strand breakage, icing overrun and mechanical fatigue can be realized.

The invention discloses a multi-sensor data fusion control method for an automatic assembly line, and relates to the technical field of data fusion, and the method comprises the following steps: when multi-source data fusion is carried out, firstly, data information is obtained in real time through a multi-source sensor, the obtained data information is preprocessed, and the accuracy and integrity of data are ensured; by analyzing data quality and fusion performance indexes and system robustness and environmental adaptability indexes in real time, the system can accurately detect the influence of electromagnetic interference and environmental fluctuation on data and dynamically evaluate the state. And an early warning and adjustment strategy is automatically triggered in case of abnormality, so that data fusion continuity is guaranteed, and downtime and human intervention are reduced. And when the abnormity exceeds the self-repairing capability of the system, the system can give an early warning, so that the efficient and safe operation of the production line is ensured, and the capability of dealing with sudden problems is improved.

The invention relates to the technical field of signal switching control, in particular to a multi-network signal switching control system and method based on big data, and the system comprises a user track prediction module, a network coverage analysis module, a signal load evaluation module, a signal switching decision module and a switching execution monitoring module. According to the invention, accurate prediction of a user moving track is provided by integrating user positioning and moving speed data, a potential network switching point is effectively predicted, and a system is allowed to dynamically adjust a network switching strategy by analyzing user behaviors in real time, so that the continuity and the stability of network services are improved, and the user experience is improved. The system can predict the future network demand before the user state is stable by calculating the trajectory trend change rate of the user, the prediction mode combines the current data and the historical trend, the prediction accuracy is improved, the signal switching opportunity and the target network selection are optimized by analyzing the signal coverage and the real-time load of each base station, and the user experience is improved. And data packet loss and time delay are reduced.

The invention discloses a bridge health real-time monitoring system based on intelligent sensing and big data analysis. The bridge health real-time monitoring system comprises an intelligent sensing layer, a data transmission layer, an edge computing layer, a cloud computing layer and an application layer, the intelligent sensing layer deploys a multi-modal sensor on a bridge in a self-adaptive manner; the data transmission layer can perform data transmission; the edge calculation layer carries out preprocessing and real-time analysis on the data; the cloud computing layer is used for fusing multi-source data based on a constructed federal learning driven big data analysis platform and carrying out long-term performance prediction on a bridge; and the application layer provides a visual monitoring interface and intelligent decision support, and realizes autonomous optimization of a bridge maintenance strategy based on reinforcement learning. According to the invention, advanced technologies such as quantum sensing, dynamic digital twinning, federated learning and the like are integrated, real-time preprocessing and analysis of data are realized, and the response speed of a monitoring system is obviously improved; and multi-source data fusion and collaborative analysis are realized.

The invention provides a cross-border e-commerce big data analysis system and method, and the method comprises the steps: obtaining multi-dimensional original business data of a cross-border e-commerce platform, carrying out the real-time collection through a distributed data collection system, and obtaining a structured business data set; data collected in real time is cleaned, converted and structured, a unified data model is constructed, and integration of heterogeneous data sources is achieved; storing the processed structured data in a time sequence database to provide data support for real-time analysis; performing real-time aggregation and statistical analysis on the newly added data to generate business index and key performance index data; a Lambda framework is adopted, batch processing and stream processing are combined, the historical data and the real-time data are subjected to full-amount analysis, and an analysis result is obtained; based on an analysis result, a data mining algorithm is adopted to identify a service mode and predict a future trend; a multi-dimensional data cube is constructed, multi-dimensional and multi-granularity data exploration and interactive analysis are supported, and management decision making is assisted.

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 an engineering safety early warning method and system based on artificial intelligence real-time risk identification, and relates to the technical field of engineering safety, and the method comprises the steps: collecting scattered engineering safety data from each engineering platform in batches, and carrying out the preprocessing of the data, and constructing a dynamic database; according to the engineering safety data collected in batches, an engineering safety knowledge graph is constructed, and different risk levels are preset according to engineering safety standards. According to the method, multi-source engineering safety data are integrated, dynamically changing risk characteristics are analyzed in real time by using an AI risk identification model, the hysteresis of traditional manual inspection and static analysis is overcome, a nonlinear relationship among the risk characteristics is captured by using a random forest model through integrated learning of multiple decision trees, and the risk characteristics are analyzed in real time. And the probability values of high, medium and low risk levels are output in combination with Softmax probability normalization, so that the evaluation precision is remarkably improved, the risk features are positioned, the scientificity of risk traceability is ensured, and data-driven decision support is provided for engineering safety management.

The invention discloses a whole-process patient self-control analgesia management platform and method based on artificial intelligence, and relates to the related field of analgesia management, and the platform comprises a multi-source data interaction module which is used for building a time sequence standard data set; the information configuration module is used for starting the self-control analgesia pump; the AI pain evaluation module is used for generating a pain scoring result; and the identification early warning module is used for generating graded early warning. The technical problems of insufficient safety and unstable effect in the analgesia management process due to dependence on a fixed dose threshold and lagged artificial pain assessment in traditional patient self-control analgesia management are solved, the whole-process pain management platform based on artificial intelligence is constructed, multi-source data can be analyzed in real time, and the pain management efficiency is improved. And the pain state of the patient is dynamically evaluated, and the parameters of the analgesia pump are automatically adjusted, so that the technical effects of personalized analgesia management and safety and effectiveness in the analgesia process are realized.

The invention relates to the technical field of digital twinning equipment management, in particular to a digital twinning scientific research equipment intelligent management system integrating the Internet of Things and artificial intelligence. The system comprises a multi-source heterogeneous data acquisition unit which is used for performing real-time information acquisition on the operation environment and the equipment state of scientific research equipment; the digital twin mapping unit constructs a temperature and humidity coupling dynamic model fusing a temperature dynamic model and a humidity dynamic model by introducing a temperature and humidity coupling coefficient, and maps data collected from multi-source equipment and a laboratory environment to the temperature and humidity coupling dynamic model; the AI multi-dimensional prediction unit predicts the future running state of the equipment and the environmental risk; and the risk early warning unit analyzes the operation state of the air conditioning equipment and the environment coupling risk thereof in real time based on the prediction result of the AI multi-dimensional prediction unit, and sends out an early warning signal before potential abnormity occurs. The method is used for predicting the equipment fault risk in advance, thereby achieving the purposes of advanced prediction and active intervention.

The invention discloses a multi-modal sentiment analysis method based on a gating circulation hierarchical fusion network, which comprises the following steps: S1, multi-modal data preprocessing: collecting text, audio and video data, carrying out preprocessing and time sequence alignment, and constructing an annotation data set; s2, constructing a gating circulation hierarchical fusion network: designing a three-level network architecture comprising a modal feature extraction layer, a gating fusion layer and an emotion recognition layer; s3, model training and optimization: adopting an Adam optimizer, taking a cross entropy loss function as a target, carrying out iterative training on the labeled data set, and inhibiting overfitting through Dropout regularization and learning rate attenuation; s4, emotion analysis: inputting data acquired in real time into the trained model, analyzing the emotion state of the user in real time through a feature extraction layer, a gating fusion layer and an emotion recognition layer, and outputting an emotion analysis result; according to the method, information of different modes can be fully utilized in the sentiment analysis task, and the sentiment recognition accuracy is improved.

The invention relates to the technical field of data processing, and discloses a heat supply network heat supply system control method based on a prediction model, and the method comprises the steps: S1, collecting historical operation data and real-time operation data of a heat supply network heat supply system, S2, carrying out the preprocessing of the historical operation data and the real-time operation data, and generating a standardized heat supply data set; by constructing a prediction model based on historical data and real-time data, the thermal load demand change in a future time period can be dynamically predicted, the problem of adjustment lag caused by dependence on a static model in a traditional control method is solved, the response ability of a heat supply system to extreme weather and user demand sudden change is improved, and the heat supply efficiency is improved. The stability and reliability of heat supply quality are guaranteed, the time hysteresis between a regulation and control instruction and the actual heat supply effect is reduced by analyzing the heat transmission characteristic and the delay effect of each node of the pipe network in real time, the phenomena of insufficient heat supply and energy waste caused by untimely regulation of the system are avoided, and the energy consumption is reduced. And the operation efficiency and the energy utilization rate of the heat supply system are improved.

The invention discloses a data hierarchical storage method and device, computer equipment and a storage medium, and the method comprises the steps: collecting an access log of target data at a current moment, determining the data information of the target data according to the access log, carrying out the feature extraction of the data information, and obtaining a target access feature and a target data feature; inputting the target access features and the target data features into a trained deep learning model, outputting a predicted access mode within a preset time period after the current moment, and according to the predicted access mode, adjusting a distribution strategy of the target data in at least two storage layers of different read-write rates of a database to obtain an updated storage strategy, and migrating and storing the target data according to the update storage strategy. The data access mode can be analyzed and predicted in real time through the model, the distribution of the data among different storage layers can be automatically adjusted, and compared with an existing static storage strategy, the storage efficiency and the access efficiency can be effectively improved.

The invention discloses an intelligent decision support system and method for grassroots social governance based on multi-modal fusion. The method comprises the following steps: S1, acquiring and preprocessing multi-modal data in the field of grassroots social governance; s2, constructing a heterogeneous graph structure according to the data type and the treatment subject category; s3, extracting single-modal features respectively and generating cross-modal dynamic association features; s4, performing collaborative optimization on the heterogeneous graph structure and the cross-modal fusion parameters by adopting a flying fox optimization algorithm; s5, analyzing data in real time according to the optimized heterogeneous graph, and generating potential risk early warning, event trend prediction and event association deduction information; s6, pushing an auxiliary decision-making scheme and a disposal strategy in real time; and S7, continuously optimizing the heterogeneous graph structure by using a feedback result. The decision-making efficiency and accuracy of grassroots social governance are effectively improved.

The invention particularly relates to a distributed boundary network threat flow detection method based on a GCN model, which comprises the following steps of: constructing a BGP network topological graph according to a business relationship of each autonomous system in a wide area network provided by an existing data set, endowing different weights to corresponding edges in the BGP network topological graph, and initializing node features; according to the acquired BGP network topological graph information, learning a BGP network topological graph by using a GCN model, performing graph node embedding and retaining key graph features; monitoring traffic path changes of a plurality of global routing convergent points, and detecting a path change mode with security threats in traffic by analyzing a path change difference score in real time; and summarizing abnormal traffic, associating the common prefix and the autonomous system, and giving an abnormal report. According to the method, the problems that information configuration is difficult to carry out and the model interpretability is poor during boundary network threat flow detection are solved, and meanwhile, the method has good real-time performance, interpretability and relatively high accuracy.

The invention discloses a fish multi-target tracking method based on nonlinear motion modeling and category constraint, and the method comprises the following steps: constructing a basic fish target detection model which is used for generating a fish target detection frame; constructing a target tracking data set in an MOT format for target tracking and a target identification data set for appearance feature extraction, and constructing a basic fish appearance feature re-identification model for extracting appearance features of a fish target; and constructing a target tracking model associated with a nonlinear filter based on detection confidence weighting and category gating. A confidence-weighted extended Kalman filtering algorithm is provided, and the algorithm dynamically adjusts weight distribution of a detection result in a track updating process by analyzing a confidence score of a target detection frame in real time. According to the category gating mechanism, a category similarity penalty term is introduced into a cost matrix of data association, and by establishing an association barrier of category perception, it is ensured that detection frames and estimation frames of different categories are not wrongly associated to the same identity.

An automated data governance system with AI-powered anomaly detection that includes: an AI-based anomaly detection module configured to analyze historical and real-time data patterns using machine learning (ML) models to detect suspicious activity, inconsistencies, and security threats; an automated compliance checker configured to ensure that data governance policies comply with the legal framework by monitoring data transactions and access controls; a data integrity management module configured to validate the consistency, authenticity, and security of the data using cryptographic hashing, blockchain-based tracking, and checksum validation; an adaptive policy engine configured to dynamically update governance policies based on real-time analytics, risk assessments, and industry regulations; a user access and role management module configured to implement role-based access control (RBAC) and leverage AI-driven behavioral analysis to dynamically adjust access permissions; and a real-time alerting and remediation module configured to generate alerts upon detection of anomalies and perform automatic remediation actions such as access revocation, data isolation, or regulatory enforcement.

A system (100) for AI-driven real-time optimization of advertising placement in dynamic media streams, comprising: (a) a media stream ingestion module configured to receive and pre-process live or continuously streamed content; (b) a content analysis and context understanding module operatively coupled to the recording module and configured to analyse the media content in real time using natural language processing, computer vision and audio signal analysis; (c) an audience profiling and behaviour tracking module configured to generate dynamic viewer profiles based on demographic, behavioural and contextual data; (d) an AI-based ad matching and ranking engine configured to select and prioritize ads based on content relevance, audience profile match, and predicted engagement; (e) a real-time decision and placement engine configured to insert selected advertisements at contextually appropriate points in the media stream without interrupting the viewer experience; and f) a feedback and analytics module configured to track ad performance and update AI models for continuous optimization of future ad placements.

The invention discloses a power distribution network construction method, system and device based on digital twinning and a storage medium, and relates to the technical field of power systems, and the method comprises the steps: collecting the operation link data of a power distribution network; constructing a power distribution network digital twinborn model based on the power distribution network operation link data; optimizing the prediction and decision-making capability of the digital twin model of the power distribution network, and performing evaluation and verification; the optimized power distribution network digital twinborn model is used for carrying out full-life-cycle dynamic simulation on the power distribution network; in the dynamic simulation process of the whole life cycle of the power distribution network, the operation state of the power grid is analyzed in real time, an optimal scheduling scheme is provided, the state of power grid equipment is monitored in real time, fault hidden dangers are found, fault causes are diagnosed, and a repair scheme is provided. The method further comprises the steps of encrypting sensitive data, backing up important data regularly, establishing a perfect data recovery mechanism and implementing strict access authority management. According to the invention, the operation efficiency, reliability, safety and intelligent level of the power distribution network can be improved.

The invention discloses a secure video transmission method based on a block chain and multi-path dynamic routing, and belongs to the field of video data secure transmission. According to the invention, malicious nodes are identified and isolated through a black hole detection mechanism, data packet loss and tampering are avoided, and the security of video data transmission is improved. And credibility calculation is carried out on the nodes, and only credible nodes are selected for data transmission, so that retransmission and resource waste are reduced. According to the dynamic routing optimization, the optimal path is quickly selected by analyzing the network topology and the node trust value in real time, the transmission delay caused by node failure or path change is reduced, and the network stability and the transmission efficiency are improved. And finally, introducing a block chain technology, recording operation in a data transmission process in a tamper-resistant distributed account book, and verifying the integrity and authenticity of the data by using a content identifier. The decentralization characteristic of the block chain enhances the transparency and traceability of the transmission process, and effectively prevents data tampering and information leakage.

The invention discloses a method and a system for carrying out government affair service interaction by utilizing a large model, and relates to a government affair service optimization technology in the field of government affair. Comprising the steps of 1, directly obtaining OCR data and ASR records from existing communication records of a cloud hall, 2, carrying out RAG updating and timed fine tuning on a large model according to the OCR data and the ASR records, and 3, deploying a client based on a pc end of the cloud hall, obtaining communication information in real time through the client, and sending the communication information to the cloud hall. Wherein screen information in the government affair service handling process is recorded through screen recording of the client side, the screen information comprises key steps and an operation interface, and voice content of a client is converted into text content in real time through a voice recognition method; and 5, automatically arranging and executing a government affair handling process based on a matching result, and carrying out authenticity judgment when the certificate is involved.

The invention discloses a real-time early warning system for analyzing abnormal behaviors of prison prisoners based on a behavior sequence, and relates to the field of real-time early warning, and the system comprises a data collection module which is used for collecting and obtaining structured data and unstructured data in a prison area; the behavior sequence extraction module generates a feature vector of a behavior atomic unit through three-dimensional convolution spatial-temporal feature extraction, and obtains a behavior vector through feature fusion; the behavior sequence modeling module constructs a behavior sequence through a double-flow LSTM architecture; the anomaly detection module calculates the KL divergence of the behavior codes and a hidden Markov model baseline by constructing the hidden Markov model baseline, and outputs anomaly probability distribution; and the grading early warning module performs grading early warning based on the output abnormal probability distribution. The method has the advantages that by fusing multi-source data and a deep learning technology, the behavior sequence of the prisoner is analyzed in real time, intelligent early warning is performed, the supervision efficiency and safety are remarkably improved, and conversion from passive monitoring to active intervention is realized.

The invention discloses a role-driven business process automatic approval method based on reinforcement learning, and the method comprises the following steps: S1, collecting business process data, carrying out the preprocessing, and constructing a preprocessing data set; s2, constructing a graph data structure, extracting role interaction features, and generating a role dynamic weight matrix; s3, optimizing training by adopting an improved near-end strategy, and optimizing an approval path through shearing and regularization; s4, searching an optimal hyper-parameter by using a He-Make algorithm, and optimizing a learning rate, a discount, entropy regularization and a shearing range; s5, generating an optimal approval path, and dynamically adjusting role permission and manual intervention; s6, analyzing the approval data in real time, and updating the network and the weight matrix online; and S7, deploying a strategy network, and monitoring and dynamically adjusting the approval strategy in real time. According to the method, reinforcement learning and improved near-end strategy optimization are combined with the Hereum algorithm, and dynamic intelligent optimization of the approval process and role permission is realized, so that the approval efficiency and accuracy are greatly improved, and the manual intervention rate is reduced.

The present application discloses a power equipment status monitoring method and related devices based on high-precision Beidou time service, which relates to the technical field of Beidou satellites. The power equipment status monitoring method based on high-precision Beidou time service includes: acquiring Beidou satellite signals and collecting real-time ionospheric data; based on the Beidou satellite signals and the real-time ionospheric data, performing ionospheric delay compensation according to a preset dynamic compensation model to determine a high-precision Beidou time service signal, and realizing precise synchronization of multi-source data of power equipment based on the high-precision Beidou time service signal; extracting characteristic information of real-time operation parameters of power equipment, obtaining global model knowledge of preset edge nodes in combination with a federated learning mechanism, and analyzing and judging the status of power equipment in real time according to a pre-trained lightweight neural network model to obtain real-time monitoring information on the health of power equipment, wherein the lightweight neural network model is obtained through parameter compression training by channel pruning and quantization-aware training.