38045 results about "Source data" patented technology

The invention discloses a tunnel structure health monitoring and early warning system based on multi-source twin data fusion, which relates to the technical field of tunnel health monitoring and comprises a data acquisition module, a multi-source data fusion module, a health state evaluation module and an early warning module. The data acquisition module can collect various monitoring data of equipment state data, tunnel environment data, traffic condition data and tunnel structure of a monitored tunnel, the multi-source data fusion module integrates and processes the collected multi-source data by using an advanced data fusion technology, and the health state evaluation module evaluates the health state of the monitored tunnel according to the fused data. And the early warning module is used for evaluating the health condition of the tunnel structure by adopting an intelligent evaluation algorithm and identifying potential structure problems, and the early warning module is used for forming an early warning result by applying an early warning algorithm on the basis of an evaluation result. The advanced data fusion method and early warning algorithm are adopted, and the accuracy and reliability of tunnel health monitoring and early warning are improved.

The embodiment of the invention relates to the technical field of ship scheduling, in particular to a multi-source data-based intelligent auxiliary decision-making method for ship entering and leaving a port and berthing and leaving the port, and the real-time performance and accuracy of port scheduling decision-making can be improved by fusing multi-source heterogeneous data to construct a ship navigation situation awareness system. The space-time alignment technology effectively eliminates space-time dislocation of environment interference factors and enhances the characterization capability of the ship motion state and environment coupling relation. Quantitative evaluation of multi-dimensional navigation risks is realized based on a ship behavior prediction model, and potential risks such as channel deviation and berthing conflicts can be identified. A collaborative decision-making scheme considering safety and efficiency is generated through a collaborative optimization mechanism of the dynamic scheduling rule base and the risk indexes, and ship trajectory control precision is optimized through dynamic planning of navigational speed adjustment and steering time sequence. Closed-loop linkage of port resource allocation and ship navigation control is achieved through automatic instruction generation and execution, and the reliability and the response speed of the port operation process can be ensured.

The present invention relates to the technical field of monitoring, in particular to a remote monitoring method and system for an aviation obstruction light. The method comprises the following steps: on the basis of an external sensor, acquiring electromagnetic signals sent by an aviation obstruction light; and by means of using a signal processing algorithm, processing the obtained original signals to eliminate noise interference and standardize the signal format, so as to generate signal-purified data. Using a support vector machine and a random forest algorithm in the present invention enhances the fault mode identification capability and the accuracy of predicting device performance degradation trends, and substantially improves the reliability of fault prediction; the combination of a Kalman filter and a multi-level decision tree provides powerful support for the integration and analysis of multi-source data, thereby ensuring the comprehensiveness and effectiveness of decision-making support information; and using a self-organizing map network and U matrix visualization technology not only shows advantages in the aspects of data mode identification and anomaly detection, but also improves the interpretability of data analysis by means of visual image displaying.

The invention discloses an ancient building risk prediction management and control method and system based on a large model, and relates to the technical field of ancient building protection. An ancient building risk prediction management and control system based on a large model comprises a multi-source data acquisition module, a multi-modal data fusion module, a large model risk prediction module, a risk assessment and early warning module, a digital twinborn dynamic management and control module and a repair decision module. According to the method, the structure state data, the visual image data and the environment data are fused, so that the state of the historic building is comprehensively perceived; the self-supervised learning pre-trained ViT-GPT model is combined with the LSTM time sequence model, so that the time sequence analysis capability and the multi-modal data processing capability of risk prediction are remarkably improved, the future damage probability and the risk trend of the historic building can be accurately predicted, and the defects of the prior art in long-time span risk prediction are overcome.

The invention provides a dynamic path planning method and system in an intelligent traffic system, and relates to the technical field of intelligent traffic. Constructing a dynamic road network traffic capacity model based on a space-time diagram convolutional network; generating a multi-branch trajectory prediction result with a confidence score, adaptively starting an encryption parameter synchronization mechanism in combination with vehicle density, and generating a candidate path set through multi-agent collaborative game optimization; performing multi-dimensional deviation degree evaluation based on actual driving data and planning expectation; synchronously generating a multi-mode cooperative guidance signal; the system comprises a multi-source data fusion unit, a space-time modeling engine, a hierarchical decision-making system, a path verification and execution module, a closed-loop feedback controller, an event response system and a multi-mode man-machine interface. According to the method, the global resource utilization efficiency is improved through cooperation of data-driven modeling and hierarchical game decision, and the real-time performance, the safety and the system adaptability of path planning are improved through a closed-loop feedback and event response mechanism.

The invention discloses a ring main unit monitoring method and system based on the Internet of Things, and the method comprises the steps: building a multi-source data collection network of a ring main unit, extracting the multi-dimensional time-frequency domain features of a signal through an edge computing device, generating a multi-source feature data set, and constructing a dynamic digital twinborn model, real-time mapping of the running state of the ring main unit is achieved through a physical-virtual synchronization algorithm, and twin data volumes are output; according to the node influence coefficient of the twin data volume, analyzing a topological structure of a ring network system by adopting a graph neural network, identifying a key fault propagation path and generating a priority monitoring node list; based on the priority monitoring node list and the twin data volume, dynamically generating an adaptive monitoring strategy through a reinforcement learning algorithm, executing the adaptive monitoring strategy and collecting feedback data in real time, and completing closed-loop iteration of the monitoring strategy through an online learning algorithm; through closed-loop control of multi-source perception-digital twinning-intelligent decision, high efficiency, intelligence and reliability of ring main unit monitoring are realized.

The present invention relates to the technical field of big data processing, and discloses a multi-source heterogeneous data fusion and processing method based on big data, including the following steps: data preprocessing, cleaning, complementing and feature extraction of multi-source heterogeneous data, providing standardized and high-quality input for subsequent processing; dynamic knowledge graph modeling, constructing a dynamic knowledge graph based on the semantic relationship of multi-source data, and generating a unified semantic representation; cross-modal alignment and deep fusion, semantically aligning multi-modal data, and generating a unified comprehensive representation through deep fusion of features; real-time stream processing, dynamically updating and processing real-time updated stream data to achieve real-time fusion of data; adaptive optimization, dynamically adjusting fusion strategy parameters, and improving the adaptability and robustness of the fusion strategy. The present invention realizes efficient fusion, real-time processing and semantic consistency expression of multi-source heterogeneous data through dynamic knowledge graph modeling, cross-modal alignment, real-time stream processing and adaptive optimization.

The invention discloses a construction site safety risk intelligent assessment method and system, and belongs to the technical field of engineering supervision. The method comprises the following steps: S100, collecting multi-source data including an equipment state, an image video, a personnel state, an equipment distance, an environment parameter and an engineering text in real time; s200, performing space-time alignment, preprocessing and cross-modal feature fusion on the multi-source data; s300, outputting a risk score and a risk level label based on the dynamic risk knowledge graph and a multi-model fusion algorithm; s400, generating a hierarchical disposal strategy according to the risk scoring hierarchy, and realizing risk closed-loop management and control through rectification verification and cooperation of multiple parties; and S500, outputting a three-dimensional visual risk distribution map and a compliance report. Through technologies of multi-source data fusion, dynamic risk mapping knowledge, intelligent closed-loop management and the like, comprehensive perception, accurate evaluation, efficient management and control and compliance landing of engineering supervision safety risks are realized, the occurrence rate of safety accidents is remarkably reduced, and reliable technical support is provided for intelligent construction site construction.

The invention relates to the field of security surveying and mapping, in particular to a network security space surveying and mapping method, system and device based on multi-source data fusion, and the method comprises the steps: obtaining network security data in real time, and constructing a dynamic network topological graph; calculating a time-varying vulnerability score based on the topological graph and a historical attack log, and predicting an attack path and a propagation probability through a Bayesian network; performing cross-domain fusion on equipment, service and user behavior characteristics by adopting a federated learning framework to generate a dynamic asset portrait; generating a risk thermodynamic diagram in combination with spatial autocorrelation analysis and a multi-index fusion algorithm; a defense strategy effect is simulated based on an attack graph reconstruction engine, a Pareto optimal strategy combination is generated through an NSGA-II algorithm, and closed-loop verification and dynamic parameter correction are realized by utilizing honeypot deployment and flow traction. Therefore, the problems of topology update lag, single risk assessment dimension, cross-domain threat association fracture, defense strategy static stiffness, non-closed loop of a verification system and the like in the traditional technology are solved.

The invention discloses an interactive question intelligent agent system based on a large language model, and relates to the technical field of dialogue interaction systems. Aiming at the technical defects of the traditional BI tool, the technical scheme is as follows: a heterogeneous data management engine realizes unified access and secure access of cross-source data; the NLP semantic recognition engine converts a natural language into structured semantics through multiple steps, drives the hybrid SQL generation engine and provides visual parameters; the generation engine realizes precise generation from semantics to SQL through a two-stage architecture based on metadata and authority rules; and the visual management console is combined with multi-party configuration and parameters to visually build a number-asking agent. The method is used for realizing intelligent conversion from a natural language to a structured query language (SQL) and a data service interface, and automatically generating an interactive data visualization result.

The invention discloses a vehicle-mounted Beidou positioning deviation self-calibration system fused with inertial navigation information, and relates to the technical field of navigation and positioning. The method is used for solving the problems of positioning deviation accumulation and signal failure in a complex environment. The system realizes space-time alignment of inertial navigation and Beidou observation data through timestamp interpolation and coordinate system conversion, and constructs a multi-source synchronous data stream. A difference value between an inertial navigation calculation speed and a Beidou Doppler speed is analyzed based on a vehicle incomplete constraint model, and a weight factor is generated in combination with a confidence threshold to suppress abnormal observation. A geometric matching error is calculated through a high-precision lane map curvature and a vehicle steering angle, and candidate tracks are screened to generate transverse calibration parameters. The noise covariance is dynamically adjusted through a tight coupling filtering algorithm, the mode is switched to a lane constraint calibration mode when a Beidou signal is interrupted, inertial navigation errors are compensated through error boundary constraint, and stable output is achieved. Multi-source data deep fusion and adaptive robust calibration are realized, and system robustness and positioning continuity are improved.

The invention discloses an electric energy metering box fault prediction method and system based on big data analysis, relates to the technical field of smart power grids, and solves the problems of progressive aging missing detection and instantaneous interference misjudgment caused by dependence on single parameter threshold alarm and fault positioning misalignment caused by multi-source data isolated analysis in the prior art. According to the scheme, electrical, environment and equipment state parameters are collected in real time through a multi-dimensional sensing network; the error drift of the mutual inductor is dynamically predicted based on an LSTM-Kalman filtering model, and core breakdown early warning is realized in combination with wavelet transform; outputting a corrected resistance value and a fault mark by using a BP neural network; predicting the life of the piezoresistor by adopting a gradient boosting decision tree and fusing lightning overvoltage characteristics; the transient interference is suppressed through the combination of a Transform self-attention mechanism and dynamic time warping; according to the method, the aging detection precision and the complex environment adaptability are remarkably improved, the misjudgment rate is reduced, and the multi-fault associated positioning and active defense capability is realized.

The invention belongs to the technical field of bridge diagnosis, and discloses a bridge crack intelligent diagnosis system based on multi-modal data fusion. By fusing multi-source data such as visual images, sound wave detection and vibration signals, comprehensive perception and characterization of crack features are realized; constructing a bridge crack characteristic spectrum diagram by adopting a cross-modal feature extraction and heterogeneous feature coding technology; generating a crack evolution situation map based on space-time correlation analysis and knowledge graph construction; the robustness of the system in a complex environment is improved through environmental adaptability feature enhancement and multi-scale characterization; constructing a bridge safety risk hypergraph in combination with multi-dimensional risk analysis and multi-agent collaborative diagnosis; analyzing and revealing a crack evolution mechanism by applying a causal relationship; and finally, through dynamic fusion and uncertainty quantification, a crack intelligent diagnosis comprehensive report is generated. According to the system, the limitation of traditional single-mode diagnosis is broken through, and dynamic prediction and accurate risk assessment of fracture evolution are realized.

The invention discloses a virtual power plant peak regulation optimization scheduling method and system, electronic equipment and a medium, and the method comprises the steps: obtaining multi-source data of distributed energy, energy storage equipment and a load side, and carrying out the cleaning and normalization processing; utilizing an LSTM neural network and an XGBoost model to carry out short-term and medium-and-long-term load prediction respectively; based on the prediction result and the resource state, generating a scheduling optimization strategy through a deep Q network (DQN) algorithm; according to an optimization strategy, distributed energy power generation, energy storage charging and discharging and load side response are dynamically managed, and multi-resource cooperative control is achieved; and through a feedback optimization module, DQN model parameters and strategy updating are automatically adjusted according to monitoring data, and the scheduling effect is optimized. The system comprises a data acquisition module, a data preprocessing module, a load prediction module, a scheduling decision module, a resource control module, and a monitoring and feedback optimization module. The method has the beneficial effects that the load prediction precision is improved, the resource scheduling is optimized, the operation cost is reduced, and the method is suitable for scheduling management of a multi-source energy system.

The invention relates to the technical field of communication command, in particular to an intelligent emergency command system and method based on scene twinning. Comprising a data acquisition module, a scene twinborn management module, an emergency situation analysis module, an emergency response decision module and a resource scheduling command module. Multi-source data of an emergency scene are collected through a sensor network, a scene twinning model is constructed by using augmented reality and digital twinning technologies, and real-time rendering of a virtual scene is realized. According to the method, emergency situation analysis, risk detection and trend prediction are performed in combination with a random matrix theory and a graph neural network algorithm, an emergency response strategy is generated by using a decision tree based on an analysis report, resources are scheduled and allocated through a multi-objective optimization algorithm, and an optimal emergency command scheme is ensured. According to the invention, the efficiency and scientificity of emergency command are improved, and the rapidity and accuracy of emergency response are enhanced.

The embodiment of the invention discloses a fire monitoring and early warning method and device based on big data analysis, and belongs to the technical field of data analysis. According to the embodiment of the invention, multi-source data such as satellite remote sensing heat source images, meteorological environment parameters, historical fire event records and geographic information raster data are integrated, a space-time associated dynamic monitoring system is constructed, and environmental anomaly symptoms before fire occurrence can be comprehensively captured. Wherein the space-time reference difference of different source data is eliminated through the space-time fusion processing, so that the heat source distribution, the meteorological change and the historical fire mode form multi-dimensional association under a unified geographic framework, the sensitivity and the reliability of early fire point identification are remarkably improved, and the subsequent accurate fire risk prediction is realized.

The invention relates to power distribution network fault processing equipment applied to the technical field of power distribution, and aims to improve the fault prediction, diagnosis and processing capabilities of a power system. The equipment comprises a pre-training module, a neural network architecture module, a data acquisition layer, an edge calculation layer, a core processing layer, a context learning module, a reasoning processing module, an output and interaction layer and a continuous learning and improvement layer, and is characterized in that a large-scale pre-trained Transform model is utilized, and professional knowledge of a power system is combined; according to the method, deep understanding and analysis of a complex power grid state are realized, through multi-source data fusion, edge calculation and central cooperative processing, the equipment can monitor the power grid state in real time, accurately predict potential faults and provide intelligent diagnosis and repair suggestions, and model pre-training and optimization are performed by adopting self-supervised learning and federated learning technologies; using reinforcement learning to optimize a fault response strategy; the interpretability of the model is realized, and the decision transparency is improved.

The invention provides an intelligent equipment fault diagnosis and reasoning method and system based on unsupervised learning. Belongs to the technical field of industrial equipment fault diagnosis and prediction. 2) unsupervised abnormal feature extraction and detection; (3) dynamic causal reasoning based on unsupervised learning; (4) a dynamic self-optimization inference engine; (5) intelligent fault reasoning and exploration; (6) enhancing the dynamic knowledge graph; and (7) fault prediction and decision support. Aiming at the problems that a large amount of annotated data is needed, causal analysis is lacked, and multi-source data fusion is difficult, non-annotated efficient detection and classification, fault mode diagnosis, prediction and decision support of equipment faults are realized through feature extraction of an unsupervised depth model, construction of a dynamic causal relationship, and fusion of a real-time self-optimization inference engine and a dynamic knowledge graph; therefore, the downtime and the maintenance cost are greatly reduced.

The present invention relates to the technical field of mine safety management, and discloses a method and system for dynamic monitoring and early warning of mine environment, including the following steps: multi-source data acquisition, collecting dynamic data of mine environment through remote sensing imaging equipment, ground sensors and meteorological stations; data preprocessing, noise cleaning, time alignment and spatial reference coordinate unification of the data; spatiotemporal data fusion, mapping multi-source data to a unified feature space through feature extraction, cross-modal fusion and attention mechanism; intelligent analysis and disaster prediction, predicting surface deformation, landslide risk and tailings dam stability in mining areas based on multi-task joint learning models; risk assessment and early warning response, dividing risk levels according to comprehensive risk scores, and dynamically generating early warning information, evacuation paths and dangerous area isolation boundaries. The present invention solves the problems of difficulty in multi-source data fusion, low disaster prediction accuracy and delayed early warning response in the prior art.

The invention provides a multi-source data fusion diagnosis method and system for new energy station equipment, and relates to the technical field of intelligent operation and maintenance of power equipment, and the method comprises the steps: collecting equipment operation data, and carrying out the preprocessing to obtain standardized data; transient features and drift features are extracted, a transient fault mode and a degradation fault mode are identified, and a dual-frequency-domain fault feature library is established; calculating a power transmission fluctuation value between devices, analyzing energy transmission efficiency, positioning a fault source device and identifying a fault propagation path; establishing a fault diagnosis evaluation index for grading diagnosis, and evaluating a fault development trend; and generating and executing a maintenance decision scheme. According to the invention, through multi-source data fusion analysis, accurate diagnosis and early warning of equipment faults of the new energy station are realized, and the equipment operation and maintenance efficiency is improved.

The invention discloses a computer equipment fault monitoring system and method based on artificial intelligence, and relates to the technical field of computer equipment fault monitoring. The system comprises a data access module, a semantic analysis module, a knowledge graph construction module, a dynamic semantic association module, a data fusion processing module, a decision output module and an adaptive optimization module. The data access module collects and standardizes hardware, software and network data; the semantic analysis module extracts and enhances semantic tags; the knowledge graph construction module forms a data semantic relation network; the dynamic semantic association module screens potential semantic relationships; the data fusion processing module generates a multi-dimensional feature vector; the decision output module triggers fault early warning; and constructing a feedback knowledge graph of the self-adaptive optimization module. According to the method, through event-driven interpolation, dynamic weight fusion, closed-loop feedback optimization and the like, the problems of multi-source data alignment, semantic fusion and dynamic adaptation are solved, the fault monitoring accuracy and the system adaptability are improved, and the method is suitable for fault monitoring and early warning of computer equipment.

Computer-implemented systems and methods for synchronizing data for dataset execution. The system includes a source database that stores a canonical dataset, a secondary database that stores a processed dataset, and a synchronization server that comprises a processor and a memory. The processor is configured to monitor for a publication of one or more source tables and when the publication is detected, identify the processed tables, corresponding to the source tables, to be updated in the processed dataset. The processor determines a tolerance level corresponding to each processed table and updates the processed tables in the processed dataset. The processor 10 determines whether the processed tables in the processed dataset were successfully updated within the tolerance levels and transmits a notification based on determining whether the processed tables in the processed dataset were successfully updated within the tolerance levels.

The invention relates to the technical field of industrial robots, in particular to an industrial robot walking control system based on obstacle recognition. Comprising an environment sensing unit; the obstacle analysis and decision-making unit is used for processing the multi-dimensional data output by the environment sensing unit based on a deep reinforcement learning framework, accurately identifying static obstacle and dynamic obstacle types, motion trails and interaction influences, constructing a two-dimensional decision-making model of static obstacle avoidance and dynamic obstacle avoidance, and carrying out obstacle avoidance and obstacle avoidance on the basis of the two-dimensional decision-making model. A differential obstacle avoidance strategy is triggered; the path planning unit is based on a dynamic game path algorithm under space-time constraint; and an instruction transceiving unit. Through the multi-modal fusion sensing technology and the adaptive parameter adjustment module, space-time alignment and feature fusion of multi-source data such as three-dimensional point cloud, texture features and vibration spectrum are realized, a high-dimensional environment state model is constructed, and the problem of insufficient data fusion depth in the prior art is effectively solved.

The invention discloses a traffic supervision system applied to an intelligent street lamp and an intelligent supervision method thereof, relates to the technical field of intelligent traffic, and solves the problems that an existing intelligent street lamp system lacks a physical-digital mapping relation, edge computing resource allocation is low in efficiency and cloud computing delay is high. According to the scheme, on the basis of multi-sensor data fusion, space-time reference unification is carried out by adopting an atomic clock and a GNSS, and a dynamic causal graph is constructed through a graph neural network, so that abnormal event detection is optimized; an improved Jaccard space-time similarity algorithm is adopted to optimize calculation task allocation, an edge calculation cluster is constructed based on 5G-V2X, and high-risk region identification and traffic flow prediction are carried out; a LiFi or 5G-UWB communication medium is adaptively selected through a multi-modal fusion reinforcement learning algorithm, and efficient early warning information synchronization is realized; according to the method, the multi-source data fusion value and the early warning precision are remarkably improved, the computing power resource utilization rate is optimized, and the instruction real-time performance and the system self-adaptive capability in a complex environment are enhanced.

The invention provides a multi-modal enterprise credit risk assessment method based on a knowledge graph, which integrates data such as enterprise relationships, industry policies and supply chain information by constructing an enterprise financial knowledge graph, processes entity static attributes and associated information by using a multi-modal embedding technology, captures the associated information in combination with a heterogeneous graph neural network, and evaluates the credit risk of an enterprise. And the dynamic space-time attention mechanism mines time and space features of the time series data, identifies a core risk conduction path based on an attention weight, and finally fuses graph-level features, dynamic space-time features and business rules to output a structured evaluation result. According to the method, multi-modal data is effectively integrated, the problem of incidence relation modeling deficiency is solved, deep fusion of enterprise multi-source data and accurate extraction of risk features are realized, and the accuracy and interpretability of enterprise credit risk assessment can be effectively improved.

The invention discloses a multi-modal fusion AGV dynamic path planning and cluster scheduling system, and relates to the technical field of multi-modal perception and data fusion, and the system comprises a multi-modal perception module which generates a dynamic obstacle confidence map through multi-source data fusion in combination with a hardware-level time synchronization and Transform feature fusion network; the dynamic path planning module adopts an improved rolling window algorithm, integrates an LSTM space-time conflict prediction model and an adaptive weight cost function, and realizes dynamic obstacle trajectory prediction and non-oscillation global path generation; the cluster scheduling control module is used for optimizing multi-AGV task allocation and conflict resolution in combination with a dynamic priority preemption mechanism and digital twinborn simulation rehearsal based on a distributed contract network protocol of edge computing; and the data conflict resolution module is used for triggering a multi-modal re-calibration process through confidence weighting and sliding window time sequence verification. According to the system, in logistics storage and intelligent manufacturing scenes, the dynamic obstacle avoidance success rate and the robustness and operation efficiency of an AGV cluster are improved.

The invention discloses an underground facility vibration monitoring method and system based on a distributed sensor network. The method comprises the following steps: acquiring initial data of the distributed sensor network; constructing an initial network topology structure; collecting sensor node state data; dynamic topology adjustment is executed; network performance is evaluated and optimized; an optimized network topology structure is obtained; receiving original data acquired by the sensor and performing data cleaning and standardization by using a network topology structure; a filtering algorithm is adopted to remove noise, and features are analyzed and extracted; and generating a preprocessed feature data set. A multi-source data fusion algorithm module is adopted to integrate information; constructing an anomaly prediction model and optimizing parameters, and performing anomaly detection by adopting the optimized anomaly prediction model; outputting the fused data and an anomaly detection result; and judging whether to trigger early warning or not according to a preset threshold value for an abnormal detection result. Efficient and reliable real-time vibration monitoring is realized, and the anomaly detection accuracy and the system adaptability are improved.

The invention relates to the technical field of intelligent decision making, in particular to a simulation system intelligent decision making method and system based on a knowledge graph and federated learning, and the method comprises the steps that each simulation node constructs a knowledge graph sub-graph based on local dynamic data, and multi-modal semantic representation is generated through space-time modeling and event chain reasoning; the federal center initializes a simulation decision model architecture and issues the simulation decision model architecture to each node; each node uses a local knowledge graph to train a time sequence diagram network, extracts an equipment degradation path and abnormal propagation characteristics, and uploads gradient parameters in combination with homomorphic encryption; the federation center fuses the multi-node model through a dynamic weight aggregation algorithm to generate a global decision model; and driving knowledge graph evolution based on real-time data, and performing causal reasoning and decision optimization on an event chain through a federal model. According to the method, the problem of insufficient dynamic decision adaptability in a multi-source data island and privacy sensitive scene in a simulation system is solved.

The invention relates to the technical field of cable operation state detection, and particularly discloses a multi-source data driven cable operation state comprehensive evaluation method, which comprises the following steps of S1, adopting a layered distributed sensing network architecture, and deploying three types of core sensors at key nodes of a cable, through space-time calibration of the multi-source heterogeneous sensor, data consistency is improved, fusion deviation is eliminated, the problem of data islands of a traditional system is solved, and a precise evaluation foundation is laid; noise suppression and dynamic correlation modeling are adopted, environmental interference is stripped, a vibration and displacement coupling relation is quantified, limitation of a single parameter is broken through, heterogeneous fault features are captured, and evaluation comprehensiveness and sensitivity are improved; a self-adaptive threshold mechanism is constructed based on environment weight and historical data, the bottleneck of a fixed threshold is broken through, an evaluation standard is corrected along with equipment aging and environment change, misjudgment is avoided, and diagnosis robustness in different scenes is enhanced.

The invention discloses an underground engineering geological safety dynamic risk assessment method based on multi-source data fusion, which relates to the technical field of risk assessment, and comprises the following steps: collecting multi-source heterogeneous data related to underground engineering, extracting implicit information, modeling underground engineering geological safety risk factors into a risk network, and establishing a risk network model; calculating the comprehensive importance of the nodes based on a Stacking integration algorithm, and identifying key risk factors; acquiring characteristic parameters of key risk factors by using spatio-temporal characteristics of implicit information, introducing a random walk mechanism to acquire a dynamic accident prediction chain, and performing learning representation by using a graph attention network to acquire probability distribution of an accident evolution path; and assessing the vulnerability of the connection edge in the risk network, establishing a dynamic risk assessment model based on the node importance and the edge vulnerability, and obtaining a dynamic risk value corresponding to the accident according to the accident occurrence probability and the risk mitigation factor. According to the invention, intelligent identification, dynamic evaluation and accurate early warning of risk factors are realized, and the accuracy and real-time performance of risk identification and evaluation are improved.