932 results about "Spatial correlation" patented technology

The invention relates to the technical field of hydraulic engineering safety monitoring, and particularly discloses a hydraulic engineering dam safety monitoring and early warning method and system, which realize comprehensive perception and accurate early warning of the health state of a dam structure through a composite sensing technology and an intelligent analysis algorithm. A micro-mechanical resonance sensor and a distributed optical fiber sensor are cooperatively deployed, and an interface and structure integrated three-dimensional monitoring network is constructed; a three-dimensional interface stripping characteristic spectrum is constructed based on a time-frequency conjoint analysis technology, and the bonding degradation state between the sensor and the dam body is accurately identified; a strain field anomaly distribution matrix is established through spatial correlation modeling, precise positioning of internal damage is realized, a dual-channel feature fusion network and a deep neural network evaluator based on an attention mechanism are designed, and multi-dimensional correlation analysis is performed on an interface state and structural damage features; and finally, realizing progressive response from data verification and multi-source verification to emergency linkage through a three-level linkage early warning decision tree.

The invention discloses a Mama-based spectrum dynamic fusion and double-attention enhancement medical image segmentation method, which comprises the following steps of: firstly, constructing a Mama integrated spectrum domain and attention pyramid module, fusing spectrum dynamic characteristics and a self-attention pooling mechanism, and performing frequency domain information compensation and local characteristic enhancement to obtain a spectrum dynamic fusion image; the spatial correlation loss caused by image blocking processing is relieved; secondly, designing a layered enhanced U-shaped architecture, deploying an MISAP module in a shallow layer of an encoder to capture multi-scale global context features, introducing a bipolar routing attention mechanism in a deep layer, and dynamically allocating sparse attention weights to focus a key pathological region; according to the method, the segmentation precision of complex edge textures and tiny lesions in medical images can be remarkably improved, and the Dice coefficient in breast tumor, polyp and abdominal organ segmentation tasks is averagely improved by 6.5%.

The invention provides an urban functional area gridding identification and classification method based on multi-source spatio-temporal data, and relates to the technical field of area identification, and the method comprises the steps: carrying out the dynamic grid division of an urban area through employing a self-adaptive grid division algorithm, obtaining the multi-source spatio-temporal data of a gridding area, carrying out the space-time alignment, and generating a multi-dimensional time sequence feature set; extracting multi-dimensional feature vectors of the grid units through a multi-modal feature fusion algorithm, acquiring long-range time sequence features by using a bidirectional gating recursion unit, calculating spatial correlation features based on a multi-head attention mechanism, and inputting the two types of features into a feature fusion network with jump connection to generate enhanced spatial-temporal features; and an optimization strategy is adopted, a functional area clustering center point is determined through density peak value clustering in sequence to obtain an initial division result, the initial result is optimized by combining spectral clustering of spatial adjacency constraints, and the boundary of the functional area is optimized by using a simulated annealing algorithm, so that accurate identification and classification of the urban functional area are realized.

The invention discloses a wide-area landslide rapid identification method based on an interpretable intelligent algorithm, and relates to the field of remote sensing science and technology, and the method comprises the steps: building a dual-channel feature extraction architecture through multi-source spatio-temporal data fusion and knowledge graph dynamic weighting: capturing image local textures through a lightweight CNN, modeling geological spatial correlation through a graph convolutional network, and carrying out the recognition of the landslide. Combining the SHAP value and causal reasoning to generate an interpretable contribution degree thermodynamic diagram and a rule chain; a knowledge graph bidirectional verification system is introduced, spatial logic contradictions are verified by using prior rules, and co-evolution of a model and a rule base is triggered based on misjudgment samples; outputting a multi-dimensional credibility report, quantifying uncertainty by Monte Carlo Dropout, and customizing interpretation granularity according to roles; a terrain-adaptive block-stream processing architecture is adopted, and edge lightweight deployment and federated learning are combined, so that wide-area real-time early warning and model dynamic updating are realized. According to the scheme, the limitation of a traditional black box model is broken through, and a disaster prevention closed loop with physical driving, transparent decision and second-level response is formed.

The invention discloses a guaranteed power supply quality monitoring and optimization method and system, and relates to the technical field of guaranteed power supply quality monitoring, and the method comprises the steps: obtaining electrical and environmental parameters in real time, modeling a power grid topology into graph data, extracting space correlation and time sequence evolution characteristics, and dynamically adjusting the attention weight of the space and time sequence characteristics, generating a joint feature vector, adaptively setting a dynamic threshold value, comparing real-time parameters in multiple dimensions, marking abnormal nodes, reversely tracing an upstream path of the abnormal nodes, calculating a proportion of impedance attenuation and current-carrying capacity reduction by combining environmental parameters, judging whether a fault point is a fault point, and considering an environmental cumulative effect of the fault point and a load sudden change frequency. And establishing an insulation aging and residual current-carrying capacity model, quantifying a safe operation interval, and implementing a dynamic compensation strategy. Through real-time monitoring, dynamic analysis and self-adaptive compensation, the stability, safety and efficiency of power grid operation are remarkably improved, faults are effectively prevented, and the service life of equipment is prolonged.

The invention provides a power distribution network system fault monitoring method and system based on multi-source information, and the method comprises the steps: obtaining a multi-source monitoring data set of a power distribution network system, carrying out the data feature extraction of the multi-source monitoring data set, and obtaining the time sequence change feature of operation state data and the space correlation feature of environment influence data; calling a pre-constructed fault detection model to carry out cooperative fault analysis on the time sequence change characteristics and the space correlation characteristics, and generating a fault detection result of the power distribution network system; determining the fault type in the power distribution network system and the position characteristic of the fault in the network structure according to the fault detection result; and generating a fault early warning instruction containing the fault positioning coordinate based on the fault type and the position feature. The overall practicability and reliability of power distribution network system fault monitoring are effectively improved.

The invention belongs to the technical field of intelligent traffic, and particularly relates to a Mama-based traffic flow prediction method, which comprises the following steps of: collecting and preprocessing a data set; static graph construction and spatial feature extraction; performing space-time dual-token decoupling embedding; carrying out dual-branch Mama modeling; performing space-time-variable feature fusion; and carrying out multi-step prediction, outputting a prediction result, and finally, outputting a prediction value of a multi-step sequence through projection mapping. The static graph convolutional network extracts spatial correlation from the inherent topology of the road network, and the double-branch Mama architecture captures the time dependence and variable correlation of the traffic flow. According to the space-time double-token mechanism, the independent influence and the synergistic effect between variables are explicitly distinguished by decoupling the action boundaries of multi-source factors such as traffic flow, environment variables and event interference, and the modeling robustness and the interpretability in a multivariable coupling scene are remarkably improved.

The invention relates to the technical field of mine mining, and discloses an underground multi-parameter environment monitoring method, which comprises the following steps of: acquiring equipment pose, vibration spectrum and environment parameters in real time through a multi-source sensor, performing space-time alignment, filtering noise reduction and feature extraction on original data, outputting a standardized state vector, and performing data processing on the standardized state vector; fusing the pose data, the vibration signals and the environmental parameters under a unified space-time reference to construct a multi-dimensional feature matrix; and carrying out dimension reduction and redundant information elimination by adopting principal component analysis, and outputting a fused equipment state vector. According to the method, through real-time fusion of the equipment pose and the airflow dynamic state, the ventilation equipment is cooperatively regulated and controlled, the interference of mechanical vibration on the gas monitoring precision is eliminated and the reliability of gas concentration detection is guaranteed based on coupling modeling of vibration spectrum and pose drift and intelligent compensation of sensor reading errors, and through space correlation analysis and dynamic air volume optimization, the gas concentration detection accuracy is improved. Wind speed sudden drop caused by tramcar passing is actively eliminated, and roadway global continuous monitoring is achieved.

The invention relates to the technical field of traffic control, in particular to a traffic signal cooperative control method based on multi-agent reinforcement learning, and the method comprises the following steps: modeling each signal lamp intersection in a road network as an agent, and constructing a distributed multi-agent control environment; dividing the whole road network into a plurality of small subnets according to spatial correlation, and sharing and aggregating traffic information in the subnets through a neighborhood information sharing mechanism; utilizing a space-time diagram attention network to extract traffic state characteristics including a space-time dependency relationship in an intersection agent and a neighborhood thereof; and defining a traffic state, a traffic signal control strategy, a reward and punishment function, a network architecture and a target function required by training of the distributed intelligent agent, and carrying out joint training on the distributed intelligent agent until a training target is achieved. According to the invention, the real-time sensing capability of the signal control intelligent agent to the traffic flow dynamic state can be improved, and the collaborative decision-making capability among multiple intelligent agents is enhanced.

The invention relates to the technical field of marine environment monitoring, and discloses an intelligent collection method and system for marine multi-dimensional information and a storage medium. The method comprises the following steps: synchronously acquiring environmental parameters at a plurality of measuring points and a depth layer, and constructing a hydrological characteristic data set; calculating spatial correlation and dividing a high variation region and a low variation region; constructing an equipment spacing model based on an information density classification result, and generating a point distribution scheme by adopting an optimization algorithm; combining real-time monitoring data to judge significant changes and dynamically updating layout parameters; and issuing an instruction to the equipment through the main and standby wireless channels to complete deployment. According to the invention, the adaptability and layout efficiency of marine environment perception are improved, and the method is suitable for intelligent monitoring application of complex and changeable sea areas.

The invention discloses a zinc coating shedding performance detection method and system, particularly relates to the technical field of metal surface treatment quality detection, and is used for solving the problems of high misjudgment rate and insufficient detection precision caused by coating surface pseudo defect interference in the existing method. The method comprises the following steps: synchronously acquiring a multispectral reflection image and three-dimensional morphology data of the surface of a zinc coating through an industrial vision imaging device, extracting grain boundary distribution information based on multispectral data, and identifying radial pseudo defects in combination with a curvature manifold geodesic line energy gradient direction of the three-dimensional morphology; and further performing micro-crack topology analysis and grain boundary spatial correlation verification on the candidate region, eliminating texture interference and grain boundary overlapping regions, and finally outputting a high-confidence fall-off defect detection result, so that the identification capability of real defects under a complex galvanized surface is remarkably improved, and the method is suitable for automatic detection requirements of a high-speed continuous production line.

The invention provides an intelligent collaborative management method and system for network security operation and maintenance work, and relates to the technical field of network security, and the method comprises the steps: obtaining operation and maintenance work order information containing a work order priority and a processing time limit, and converting the operation and maintenance work order information into a work order feature vector; inputting the skill score, the historical completion rate and the workload of the operation and maintenance personnel into a multi-objective optimization model, and calculating a work order matching degree score and an emergency degree score; performing state coding on the work order feature vector, the personnel feature and the constraint condition by adopting a multi-layer perceptron, extracting spatial correlation by utilizing a double Q network, and calculating a target Q value; and generating a work order distribution scheme based on a Pareto optimal solution algorithm, monitoring a processing state in real time through a task collaborative scheduling model, and generating a collaborative scheduling strategy. The intelligent level of work order distribution is improved, collaborative management of operation and maintenance tasks is realized, and the operation and maintenance efficiency is improved.

The invention discloses an online detection method for the sealing performance of a sealing element, particularly relates to the technical field of quality detection of industrial sealing elements, and is used for solving the problems of reference value deviation and misjudgment caused by neglecting the influence of mounting stress in an existing detection method. According to the method, a sealed contact interface phase field model is constructed by collecting installation stress data in real time, and crack network permeability is analyzed to predict a leakage diffusion path; high / low stress sensitive areas are divided according to the spatial correlation between the stress gradient and the leakage density, and differential signal acquisition is implemented; extracting a bar code entropy value through topological data analysis, generating a compensation priority sequence, and dynamically correcting a detection reference; and finally, based on the regional weight fusion compensation signal, outputting a high-precision pressure distribution and leakage flow trend result. Cross-field coupling analysis of installation stress and leakage signals is achieved, and the accuracy and reliability of sealing performance detection under complex working conditions are improved.

The invention provides a solar power generation power real-time prediction method, system and device based on multi-mode deep learning and a storage medium. Wherein the prediction precision is improved by fusing multi-dimensional features of earth surface radiation, equipment temperature and environmental data, mutation fluctuation decomposition is performed on long and short wave components of earth surface solar radiation, and a frequency domain sub-modal sequence is extracted; constructing a correlation model of a photovoltaic module temperature field and hot spot infrared data, and generating a dynamic feature map reflecting hot spot interference space distribution; establishing a multi-mode time sequence prediction model, and synchronously processing frequency domain features of radiation sub-modes, spatial correlation of hot spot feature maps and time sequence dependence of temperature and humidity data; and integrating the prediction results of the sub-modals through a frequency domain superposition reconstruction technology. According to the technical scheme provided by the invention, through multi-modal spatial-temporal feature joint modeling and dynamic interference decoupling, the power prediction precision in extreme weather and hot spot abnormal scenes is remarkably improved, and meanwhile, the dynamic adaptive capacity of a prediction system to complex environment disturbance is enhanced.

The invention belongs to the field of flight path prediction, and discloses a flight path stability optimization method and system, a storage medium and equipment, and the method comprises the steps: responding to aircraft kinetic parameters, pilot physiological state parameters and three-dimensional space environment parameters which are synchronously collected from multiple sources, and constructing a dynamic fusion data set through timestamp alignment; based on a pre-constructed dual-channel feature extraction architecture, dual-channel features of the dynamic fusion data set are extracted, a first channel analyzes a dynamic coupling relationship in a time dimension through a sparse attention mechanism, and a second channel models a spatial correlation topology of environmental parameters through a graph neural network; a deep reinforcement learning decision engine is constructed by fusing dual-channel features, an environment state-control action-flight path stability mapping strategy is established, and control strategy optimization is driven through a reward mechanism; and generating a multi-parameter collaborative optimization instruction of the flight control plane based on the real-time decision engine. According to the method, the efficiency and precision during flight path prediction can be improved, and then the flight path is optimized.

The invention provides a cradle head inspection dynamic regulation and control method and system for a power transmission line, a storage medium and a program product, and relates to the technical field of power transmission line monitoring, and the method comprises the steps: obtaining the multi-source data of a target power transmission line, inputting the multi-source data into a preset dynamic risk assessment model, obtaining a dynamic risk thermodynamic diagram output by the preset dynamic risk assessment model; establishing a spatial characteristic model for a plurality of cradle head preset positions of the target power transmission line, and performing spatial correlation analysis on the spatial characteristic model and the dynamic risk thermodynamic diagram to obtain spatial correlation characteristic parameters of each cradle head preset position in the plurality of cradle head preset positions; determining an equipment state weight value of each piece of pan-tilt camera equipment in a plurality of pieces of pan-tilt camera equipment of the target power transmission line, and determining a comprehensive inspection weight value of each pan-tilt preset position according to the equipment state weight value and the spatial correlation characteristic parameters; and converting the comprehensive inspection weight value into a target inspection period of each cradle head preset position by using a first preset inverse proportional function.

The invention relates to the technical field of power grid monitoring, and discloses a voltage sensor multi-parameter real-time monitoring method and device in an Internet of Things environment. According to the method, real-time voltage fluctuation data of a target area is acquired by deploying a multi-channel voltage sensor array and is transmitted to an edge computing node; dynamic feature extraction is executed in the edge nodes, and a multi-dimensional feature matrix containing voltage fluctuation ratio, harmonic distortion and phase deviation features is generated; identifying voltage sag, overvoltage and harmonic resonance event characteristics based on an anomaly detection model; spatial correlation modeling is carried out in combination with power grid topological structure parameters, the coupling strength of adjacent sensor node parameters is calculated, and a multi-parameter correlation map containing event propagation paths and influence ranges is generated; inputting the voltage into a diagnosis model to generate a power grid state diagnosis report, and dynamically adjusting the sampling frequency and filtering parameters of the voltage sensor according to the power grid state diagnosis report. According to the method, real-time monitoring and analysis of multiple parameters of the power grid can be realized, and comprehensive support is provided for power grid state evaluation.

The invention discloses a human body posture reconstruction and tracking method based on a neural network, and relates to the technical field of posture reconstruction and tracking. Target echo data are acquired by adopting a multiple-input multiple-output (MIMO) antenna array, and a low-resolution three-dimensional radar image is constructed by adopting space-time weighting in combination with wavelet transform noise reduction and self-adaptive gain compensation; in the feature extraction stage, a Transform-based feature extraction network is introduced, short-distance joint details are processed in combination with a local convolutional neural network (CNN), the spatial correlation of a far-end joint is calculated by adopting a global self-attention mechanism, and a global feature enhancement (GFE) module performs feature completion on a low-confidence region; a hybrid memory network HMN is adopted to carry out short-time and long-time information fusion, a short-time memory unit STM is used to store current frame features, a long-time memory unit LTM is used to store historical key point data, a drift error is corrected in combination with time sequence alignment, and time sequence consistency of attitude estimation is guaranteed.

The invention relates to the technical field of resource allocation, in particular to a data preset learning-based dynamic material allocation system for multiple stations, which comprises a topology modeling module, a demand clustering module, a strategy generation module and a decision optimization module. According to the method, laser ranging and dynamic time warping are combined, the work station layout is converted into a moving time consumption parameter, a space correlation coefficient is adjusted according to a difference value proportion, a topological relation matrix is updated in real time, DTW quantifies the mode similarity of a demand interval standard deviation and a usage variable coefficient, a multi-dimensional demand feature vector is constructed, and the prediction and actual consumption matching degree is improved; the method comprises the steps of establishing a response capability curve based on OEE data, dynamically associating residual capacity with demand characteristics through linear programming, realizing strategy pre-generation and capacity early warning, constructing a cooperation unit through a Hungary algorithm, matching material and equipment parameters through cosine similarity, triggering strategy reconstruction, synchronously updating topology, forming a closed-loop optimization system and enhancing the cross-station dynamic response capability.

The invention relates to the technical field of supply chain risk early warning, in particular to a supply chain risk early warning method based on deep learning, and the method comprises the steps: obtaining supply chain data, extracting a material circulation relation between supply chain nodes, constructing a node relation graph, and employing a graph neural network to achieve the aggregation of the features of the nodes and adjacent nodes. And space correlation characteristics in the global network are extracted, and a multi-stage transmission and diffusion path of the risk is effectively modeled. And then, splicing node space features and historical time sequence features, inputting the spliced features into a long-short-term memory network, dynamically capturing the evolution trend of node risks along with time, identifying periodic fluctuations and sudden anomalies, and improving the prediction precision of the risk trend. And finally, a multi-layer perceptron is adopted to carry out nonlinear mapping and feature fusion on risk time sequence features output by the long-short-term memory network, node risk scores are generated, real-time early warning of high-risk nodes is realized accordingly, and the accuracy and timeliness of supply chain risk monitoring are greatly improved.

The invention relates to the technical field of pipeline safety detection, in particular to a pipeline hydrogen-doped leakage real-time monitoring and early warning method and system based on Fluent simulation, and the method comprises the steps: obtaining leakage diffusion data of gas in a pipeline detected by multiple sensors, and carrying out the preprocessing of the data; extracting data features: carrying out conjoint analysis on a time-frequency domain to obtain a time-frequency matrix; by calculating mutual information evaluation correlation of different sensor data, feature statistical correlation is realized, and features with non-significant spatial correlation are removed; the pressure data, the flow data and the concentration data are subjected to sub-channel filtering and fusion processing based on federated Kalman filtering (FKF); establishing a pipeline model based on Fluent, and simulating a pipeline hydrogen-doped leakage process by taking the extracted features as input conditions; a risk model is constructed based on real-time sensor data, simulation prediction data and environmental parameters, and a hierarchical early warning mechanism is established. Through cooperative monitoring of multiple sensors, leakage characteristic data can be accurately captured, and safety and reliability of the system are ensured.

The invention discloses a method for realizing a logic breakpoint debugging function of an industrial configuration control system, which relates to the technical field of industrial automation control, and comprises the following steps of: constructing a fault prediction model, performing multi-scale time sequence modeling and spatial correlation analysis on spatial-temporal characteristic tensors, and generating a high-probability fault pile point set; constructing an equipment logic association graph, performing ontology reasoning to obtain an association diagnosis characteristic graph of high-risk logic nodes, and generating a directional monitoring instruction set in combination with the high-probability fault pile point set; and performing space-time scene reconstruction and root cause path analysis on the high-confidence breakpoint trigger signal and the five-dimensional section snapshot to generate an interactive fault diagnosis report. According to the method, the equipment-logic association graph is constructed, graph traversal and ontology reasoning are carried out, the dynamic association relationship between equipment physical data and control logic execution data is modeled into a weighted directed graph structure, node risk values are quantized by utilizing a graph diffusion algorithm, and visual tracking of a fault propagation path is realized.

The invention relates to the technical field of power dispatching, in particular to a power distribution network photovoltaic energy storage collaborative optimization dispatching decision-making system based on big data and artificial intelligence. Comprising a source network load storage full-dimension data acquisition unit; a multi-source heterogeneous data fusion processing unit; the dynamic multi-target intelligent optimization decision-making unit is used for constructing a power distribution network photovoltaic energy storage collaborative scheduling strategy through an improved non-dominated sorting multi-target grey wolf optimization algorithm with adaptive weight adjustment; and a closed-loop control execution unit. According to the invention, photovoltaic, energy storage, load and power grid operation data in the power distribution network are comprehensively captured through the source-network-load-storage full-dimension data acquisition unit, and spatial correlation mapping of topological nodes of the power distribution network in a multi-source heterogeneous data fusion processing process is combined; and constraint conditions such as power balance and node voltage during dynamic multi-objective optimization decision making are included, so that neglect on topology and operation constraints of the power distribution network is effectively made up.

The invention relates to the technical field of network security of a power monitoring system, and discloses an attack event prediction method and system based on low-intensity abnormal mode data, and the method comprises the steps: intercepting abnormal data fragments from historical multi-source heterogeneous data based on the low-intensity abnormal mode data selected from the historical multi-source heterogeneous data; analyzing to obtain a target abnormal data fragment; training the gating loop network model based on target key time sequence abnormal features obtained by performing feature extraction on the target abnormal data fragments to obtain a future time sequence feature prediction model; performing structured processing on the low-intensity abnormal event to obtain a spatial correlation feature topological structure; obtaining future time sequence features based on the future time sequence feature prediction model; and obtaining an attack event prediction result based on an abnormal event evolution graph constructed by fusing the future time sequence feature and the spatial correlation feature topological structure. According to the method provided by the invention, accurate identification can be carried out in the preparation stage of the attack event.

The invention provides an electricity load equipment identification method based on spatial-temporal feature fusion and an attention mechanism, and the method comprises the steps: carrying out the dual preprocessing of the data through the real-time collection of the electrical time sequence data, operation state labels, spatial layout information and environment parameters of target equipment, comprising the steps of dynamically aligning a time sequence curve by time warping and adaptively adjusting data distribution in a standardized manner. A spatial correlation graph is constructed by using a graph neural network, and space-time proximity is calculated to generate a weighted adjacency matrix. A multi-head attention mechanism is adopted to fuse electrical time sequence features and spatial topology features, and bidirectional asymmetric attention modulation network optimization feature fusion is constructed. Time and space dimension features are extracted through a time flow module and a space flow module respectively, the features are dynamically weighted and fused through a gating attention unit, and the identification model is input to output the equipment type and the working state. According to the method, the accuracy and robustness of electricity load equipment identification can be improved, and the method has good adaptability and expansibility.

The invention discloses a coal mine earthquake time sequence feature prediction method based on a multi-source data space-time diagram convolutional network, and belongs to the field of mine dynamic disaster prevention and control. The method comprises the following steps: firstly, fusing micro-seismic data and charge data, and extracting and integrating features of two data sources by using a feature fusion technology so as to more comprehensively capture dynamic features of an underground environment; then, constructing a graph structure, taking the sensors as nodes, and defining edges among the nodes by using Euclidean distances among the sensors so as to reflect spatial correlation of geological activities; and dynamically capturing the correlation between the time dimension and the space dimension through the space-time diagram convolutional network. And then, optimizing hyper-parameters in the model by using WOA to obtain an optimal parameter combination, and improving the performance and accuracy of the model. And finally, introducing a space-time attention mechanism, and dynamically adjusting data input by calculating a time attention matrix and a space attention matrix so as to better capture space-time characteristics and improve the accuracy of model prediction.

The invention relates to the technical field of pipeline GIS, and discloses a pipeline GIS system and method. The method comprises the following steps: acquiring GIS underground pipe network topological data of a target area in a GIS system, and acquiring an earth surface abnormal data set of the target area through an unmanned aerial vehicle; performing multi-channel classification and weighted spatial interpolation on the surface anomaly data set to obtain surface anomaly risk distribution data; performing spatial correlation analysis through a heterogeneous attention module to obtain a spatial attention weight and earth surface anomaly propagation characteristics; performing time-space synchronization analysis on the surface abnormal data set and the GIS underground pipe network topology data to obtain risk cascade propagation data; and performing risk judgment on the risk cascade propagation data to obtain a safety state early warning result of the underground pipeline, and generating an unmanned aerial vehicle route adjustment instruction and a GIS system response instruction. According to the method, the complex space association relationship between the surface anomaly and the underground pipeline can be automatically identified and quantified, and the anomaly detection precision and credibility are improved.

The invention discloses a clean air switch equipment fault diagnosis method and system, and the method comprises the steps: firstly collecting a gas concentration signal, a partial discharge signal and environment parameters of clean air switch equipment, carrying out the preprocessing, feature extraction and feature fusion, and outputting a multi-modal feature vector; inputting the multi-modal feature vector into a pre-constructed CNN-LSTM-Attention model, and carrying out classification processing on the multi-modal feature vector to obtain a fault type and a fault severity degree of the clean air switch equipment; according to the pre-constructed CNN-LSTM-Attention model, a bidirectional long-short-term memory network is adopted to capture time sequence long-term dependence, a convolutional neural network is used to extract spatial correlation, and key fault features are focused through multi-head self-attention. According to the method, the technical problems that the monitoring precision of a single sensor is insufficient and the modeling capability of a traditional machine learning model on time series data and spatial correlation is weak are effectively solved.

The invention relates to the technical field of seismology, in particular to a seismic oscillation field stochastic simulation method based on spatial correlation, which is composed of a single-field-point seismic oscillation model and a spatial correlation model, and specifically comprises the following steps: step 1, building a single-field-point seismic oscillation time-frequency power spectrum model; 2, building a prediction equation of time-frequency power spectrum parameters; step 3, converting a time-frequency power spectrum to a time history; 4, building a spatial correlation model of the time-frequency power spectrum parameters; according to the method, the simulation speed of the seismic motion field is greatly improved, meanwhile, spatial correlation of model parameters is introduced, correlation of time-frequency characteristics between field points in space is well represented, and the seismic motion field simulated through the method can conform to physical cognition.

The invention relates to the technical field of power frequency resistance real-time monitoring, and provides a traction power supply 27.5 kV cable interval grounding grid power frequency resistance real-time monitoring method and system, and the method comprises the steps: obtaining the temperature distribution data of the surface of a conductor of a grounding grid; acquiring voltage and current response of the grounding grid when the local temperature rise value exceeds a preset reference value to perform frequency domain conversion, and generating a broadband impedance spectroscopy analysis result; aligning earth surface coordinates corresponding to the temperature measurement nodes with underground coordinates corresponding to the impedance measurement points, generating a space correlation grid, generating a temperature rise phase deviation coupling relation matrix, and generating a real-time monitoring result of the power frequency resistance of the grounding grid according to a power frequency impedance correction value in the space correlation grid and the temperature rise phase deviation coupling relation matrix; according to the invention, accurate space matching between the abnormal temperature rise area and the impedance change of the corresponding conductor is realized, the adaptability of a monitoring network to the extension direction and the burial depth change of the conductor is enhanced, and feature signal omission caused by fixed measurement point deployment is avoided.