301 results about "Spatiotemporal correlation" patented technology

The invention discloses an urban water body pollution traceability system based on multi-source sensing data fusion, and the system comprises a data acquisition module which is used for deploying a multi-source water quality sensor to collect initial multi-source water body data, and carrying out the time-space unified alignment processing, and obtaining the time-space aligned multi-source time-space water body data; the pollution factor tracing module is used for constructing a pollution event deconstructor and a factor tracing reasoning engine based on a water network topological graph neural network on the basis of multi-source space-time water body data, and outputting pollution component vectors through pollution component decomposition driven by the pollution event deconstructor; inputting the pollution component vector into a tracing reason inference engine to carry out tracing reason space-time correlation to obtain a tracing reason pollution fusion map; and the traceability decision module is used for performing inversion through a reverse traceability algorithm based on the traceability pollution fusion map, calculating the probability that each upstream area is a pollution source, mapping the probability that each upstream area is the pollution source to a GIS platform, obtaining a pollution traceability confidence distribution map, and realizing accurate traceability of the urban water pollution source.

The invention relates to the technical field of unmanned aerial vehicle vision, and discloses a vision system of an unmanned aerial vehicle and the unmanned aerial vehicle. The system comprises a visual data acquisition module, a dynamic feature extraction module, an environment modeling module and a decision control module. The visual data acquisition module captures a synchronous frame sequence containing infrared wave bands, visible light wave bands and depth information in a target area through a multispectral sensor array; the dynamic feature extraction module performs cross-modal fusion processing on the original visual data stream to generate a space-time correlation feature tensor containing a target contour geometric invariance descriptor and a motion trail differential topological structure; the environment modeling module constructs a three-dimensional semantic grid map according to the feature tensor, wherein each voxel unit codes the material reflectivity, the dynamic obstacle occurrence frequency and the illumination attenuation coefficient; the decision control module generates a flight path control instruction containing a pitch angle adjustment amount, a yaw angle compensation value and a speed change gradient based on the map, and assists the unmanned aerial vehicle to better cope with a complex environment.

The invention relates to the technical field of geological engineering, and discloses a geological safety risk dynamic assessment method based on multi-source data fusion, which comprises the following specific steps: step 1, collecting and standardizing multi-source geological data; 2, performing semantic fusion and conflict resolution on the geologic features; 3, constructing a dynamic risk assessment model; 4, risk situation real-time updating and early warning are carried out; the satellite remote sensing system in the first step adopts the synthetic aperture radar interference measurement technology, the spatial resolution is better than 3 meters, the revisit period is shorter than 7 days, and the earth surface deformation monitoring precision reaches the millimeter level. Through standardized processing and semantic fusion of the multi-source geological data, the problem of heterogeneous data integration is effectively solved, the data basic quality of risk assessment is improved, a space-time coupling neural network model is adopted, nonlinear features and space-time correlation characteristics of geological risk evolution are accurately captured, and prediction precision and timeliness are improved.

The invention relates to the technical field of information security, in particular to a network security event association detection method based on big data analysis. Comprising the following steps: data acquisition; feature extraction and fusion; correlation detection is carried out, wherein an improved Apriori-Bayesian fusion algorithm is adopted, and discretization processing is carried out on the event feature vectors; mining a frequent item set by using an improved Apriori algorithm; and risk assessment and result output. According to the method, an improved Apriori-Bayesian fusion algorithm is adopted, discretization processing is carried out on event feature vectors according to types, meanwhile, a security event weight factor is introduced to calculate the item set weighted support degree, and a minimum support degree threshold value is dynamically adjusted to mine a frequent item set; the association confidence coefficient is calculated in combination with the Bayesian network, and the confidence coefficient is corrected through the space-time association coefficient, so that the association relationship between the network security events can be scientifically judged, the problems of limited association judgment accuracy and lack of quantitative correction in the traditional technology are solved, and the association false alarm and missing report probability is reduced.

The invention belongs to the technical field of wind power climbing event prediction, and particularly relates to a wind power climbing event prediction method considering extreme weather and time-space correlation information. The method comprises the following steps: acquiring historical actually measured meteorological data of each wind power station of a cluster; carrying out cold-wave weather event identification on historical actually measured meteorological data, generating an antagonistic network based on a time sequence, and carrying out cold-wave event sample expansion; an extreme learning machine is constructed, and cold-wave weather prediction is carried out; performing historical climbing event detection on historical power output results of each station of the cluster; dividing the climbing events into various climbing conditions with different severity degrees by using a K-Means clustering algorithm; and carrying out climbing event prediction. According to the method, sample support is provided for training of the climbing prediction model, the climbing events are clustered and divided by fusing the fan operation state and the climbing characteristics, and the harm degrees of different climbing events, especially the climbing events in extreme weather, are finely measured.

The invention provides a multi-source data fused water quality pollution monitoring method and device and a storage medium, and relates to the technical field of water quality monitoring. According to the method, through the key steps of data preprocessing, cross-modal feature fusion, pollution concentration estimation, graph neural network traceability and the like, multi-source water quality data and monitoring point space topology information are deeply integrated, and a whole-process automatic monitoring system is constructed. According to the method, end-to-end processing from data standardization to pollution emission source positioning is realized, a multi-source heterogeneous data barrier is effectively broken, the complementary value and the time-space association rule of different modal data are fully mined, the integrity, accuracy and efficiency of water quality pollution monitoring are improved, the emission source position and the pollution diffusion path can be accurately positioned, and the water quality pollution monitoring efficiency is improved. Full-link and targeted technical support is provided for water quality pollution control and treatment decision, and the problems that traditional monitoring data is low in utilization rate and insufficient in traceability accuracy are solved.

The invention discloses a rainfall downscaling method and system based on a deep learning network model fusing rainfall priori knowledge, and the method comprises the steps: firstly collecting the topographic data and low-resolution day-by-day rainfall data of a target region, and taking the data as input data; a short-term high-resolution precipitation field generated in a mesoscale weather forecast WRF mode is used as training truth value data; according to the method, the function of accurately downscaling the rainfall data in combination with the convolutional neural network and the long and short term memory network is realized, the spatial-temporal correlation of rainfall is fully considered in the downscaling process, and meanwhile, a likelihood function combined with coupled censored data, Box-Cox conversion and time variation variance Gaussian distribution is adopted as a rainfall loss function; the method not only can represent zero expansibility, skewness and heterovariance characteristics of rainfall, but also can improve the rainfall downscaling precision and quantify the uncertainty of rainfall downscaling, and is suitable for wide popularization and use.

The invention relates to the technical field of die casting intelligent manufacturing, and discloses an online monitoring and process compensation method and system for residual stress and deformation of a die casting. The method comprises the following steps: embedding a distributed temperature-stress composite sensor array in a mold cavity, and synchronously acquiring temperature and stress signals; after the signal is purified, a space-time correlation matrix is constructed to quantify a thermal-mechanical coupling relation; identifying a stress distribution mode through a support vector machine model, and positioning a fluctuation abnormal region; analyzing a defect mechanism based on mutual information and Granger causality test, and calculating a pore formation probability in combination with fluid dynamics simulation data; matching the pore high-risk location with a historical crack correlation model to generate a crack prediction index; and performing inversion optimization on the mold filling speed and pressure parameters by using the potential quality hazard evaluation function. Real-time monitoring of residual stress and deformation in the die-casting process, defect dynamic traceability and process online optimization are achieved, and air hole and crack defects are effectively restrained.

The invention relates to the technical field of Internet of Things security detection, and discloses a network security detection system and method based on Internet of Things terminal equipment. The method comprises the following steps: capturing a terminal network communication data flow in real time, and extracting an equipment identifier, a communication protocol feature and a transmission content feature; performing credibility verification on the equipment identifier to generate a credible equipment list, establishing a protocol behavior model according to protocol characteristics to identify a protocol layer abnormal event, and identifying a content layer abnormal event through matching of transmission content characteristics and a preset rule; performing time-space correlation analysis on the two types of abnormal events to generate a comprehensive threat event set, and constructing and dynamically adjusting a terminal behavior baseline according to the comprehensive threat event set; the method comprises the following steps: performing deviation detection on a real-time data stream based on a baseline, performing threat tracing on a result to determine a threat source and an attack path, and finally integrating a tracing result and a comprehensive threat event set to generate a terminal security situation report, thereby realizing dynamic detection and management and control on the network security of the Internet of Things terminal.

The invention relates to the technical field of digital content production, and discloses an interactive digital content production system based on a Transform architecture. According to the system, text, image and audio data of original digital content are acquired through a content feature extraction module, cross-modal feature alignment is performed by using a multi-head attention mechanism, and content feature tensors with space-time relevance are generated; the dynamic weight distribution module calculates relative importance scores of different modal features based on the tensor, and adopts a gating mechanism to perform dynamic weight fusion to form content semantic enhancement representation; the interaction intention analysis module performs space-time coding matching on the enhanced representation and the user operation instruction stream, and analyzes an intention distribution matrix of user operation on the content dimension; a hierarchical decoding generation module constructs a multi-scale content generation path in a Transform decoder according to the intention distribution matrix; and the real-time rendering engine module loads implicit representation output by the path, so that efficient and intelligent digital content creation is realized.

The invention relates to the technical field of image processing, and discloses a port loading and unloading risk identification method and system based on image processing. The method comprises the following steps: acquiring standardized image data through multispectral image acquisition and sea wind disturbance compensation processing, extracting multidimensional risk characteristics of suspension arm swinging, cargo deviation and personnel violation, performing dual evaluation of collision trajectory prediction and violation behavior detection, identifying comprehensive potential safety hazards by adopting a spatio-temporal correlation adaptive region growth algorithm, and determining whether the potential safety hazards exist or not. And generating port loading and unloading composite risk early warning information. The technical problems that multiple risk factors are difficult to accurately identify and the composite risk state cannot be effectively predicted in a complex marine environment in port loading and unloading operation are solved, and the environmental adaptability of port loading and unloading risk identification and the accuracy of composite risk assessment are remarkably improved.

The invention is suitable for the technical field of intelligent traffic and unmanned aerial vehicle scheduling, and provides an unmanned aerial vehicle highway inspection task scheduling optimization method and system, and the method comprises the following steps: obtaining the dynamic state information and environment constraint information of all available unmanned aerial vehicles in real time, and forming an unmanned aerial vehicle resource state set and an environment constraint set; based on the initial inspection task set, constructing a space-time association diagram of an expressway road network, and reasoning to generate a derivative predictive task to obtain a to-be-scheduled task pool; according to the unmanned aerial vehicle resource state set, the environment constraint set and the to-be-scheduled task pool, a mixed integer programming model is established and solved, and an inspection task plan is allocated to each unmanned aerial vehicle; and monitoring the process of executing the inspection task plan by the unmanned aerial vehicle in real time, generating a predictive secondary task and an execution deviation degree, and judging whether to trigger rescheduling. According to the method, fundamental transformation from passive response to active prediction and from static planning to dynamic adaptation of highway unmanned aerial vehicle inspection is realized.

The invention belongs to the technical field of unmanned aerial vehicle millimeter wave communication channel modeling, and discloses an urban low-altitude unmanned aerial vehicle millimeter wave communication channel modeling method under sand and dust weather conditions. According to the method, a three-dimensional geometric random channel model is provided, and scatterers are divided into three types: near-field scatterers at the transmitting end and the receiving end adopt bicylindrical models, suspended dust adopts a complete ellipsoid model, and ground fixed scatterers adopt a lower half ellipsoid model with a larger semi-major axis. On the basis of the model, channel impulse response containing sight distance, primary scattering components and secondary scattering components is derived, and a space-time correlation function and Doppler power spectral density are calculated. And finally, establishing a joint path loss model: on the basis of the two-path fading model, introducing an additional attenuation factor related to the visibility of the sand and dust and the particle charge mass ratio, and determining specific parameters of the factor according to the grade of the sand and dust. According to the method, the channel characteristics can be accurately predicted, and theoretical support is provided for anti-interference design and link optimization of the millimeter wave communication system of the unmanned aerial vehicle.

The invention discloses a marine traffic flow probability prediction method based on multi-source AIS-meteorological data fusion, and belongs to the field of marine traffic safety management, and the method comprises the following steps: S1, obtaining a standardized multi-source data set; s2, fusing the dynamic statistical characteristics of the AIS ship and the meteorological environment characteristics through a ship motion transfer function, and constructing a space-time patch considering a short-term ship motion trend and a long-term sea condition trend; s3, based on the space-time patch, fusing patch Transform time sequence modeling and adaptive dynamic graph space correlation learning to obtain a space-time fusion feature; and S4, outputting the traffic flow state prediction value and the probability distribution of the traffic flow key indexes. The marine traffic flow probability prediction method based on multi-source AIS-meteorological data fusion breaks through the limitation that a traditional method is single in data dimension, insufficient in space-time correlation capture and lack of probability confidence, and high-precision probability prediction of the marine traffic flow under the complex sea condition is achieved.

The invention relates to an intelligent sensing and trajectory prediction method for a low-altitude, slow and small target in a complex dynamic environment, and aims to solve the problem that accurate recognition is difficult due to the fact that a small-scale and high-maneuvering target is easily affected by strong electromagnetic interference and a complex background in the complex dynamic environment, and a low-altitude target fusion detection method is constructed based on a multi-source heterogeneous sensor. The bottleneck of accurate identification of the low-slow small target under strong electromagnetic interference and complex background is broken through; a low-slow small target detection method based on hierarchical feature distillation and dynamic context awareness is provided, multi-level features from low-level details to high-level semantics are extracted by designing a hierarchical feature distillation network, capture of small target features under a complex background is enhanced by using the dynamic context awareness, and the detection precision is improved; an unmanned aerial vehicle target motion trajectory prediction model based on space-time association is constructed, and in combination with a behavior pattern library, accurate evaluation and intention reasoning of a target threat level are realized, and the intelligent sensing and prediction capability of a low-speed small target in a complex environment is integrally improved.

This invention discloses an end-to-end examination behavior analysis method and system, comprising a video input management module, a video decoding module, an image preprocessing module, a raw frame prediction queue, a raw frame queue, an AI module, an analysis result recording module, and an abnormal event video generation module. It utilizes an end-to-end AI model training and inference scheme, enabling efficient training using large amounts of historical data. In practice, it offers fast processing speed and high efficiency. The end-to-end AI model takes continuous video frames as input and outputs bounding boxes indicating suspected violations after inference. In the post-processing section, the model output undergoes spatiotemporal correlation and filtering to finally generate spatiotemporal location information of suspected violations. It exhibits good scene adaptability; when new scenes are needed or new actions to be identified are added, only new training data needs to be added and retrained to expand the algorithm, saving manpower and improving efficiency.

This invention discloses a method and apparatus for predicting power supply equipment faults based on deep learning, relating to the field of power system equipment fault prediction and deep learning application technology. The method includes: acquiring power grid topology, equipment operating status, historical fault records, real-time equipment load, and environmental condition data; constructing a spatiotemporal correlation graph based on the power grid topology and equipment operating status, and calculating the correlation strength using a graph neural network; combining the correlation strength and historical fault records, calculating the fault time delay using a long short-term memory network and determining the set of propagation paths; fusing multiple data points to calculate the cross-regional fault propagation probability and generating a list of predicted fault paths; updating the fault prediction output input to the long short-term memory network, and verifying and optimizing it using real-time power grid operating data to achieve accurate prediction of cross-regional cascading faults, ensuring the safe and stable operation of the power grid.

The invention discloses a circumferential contour prediction method based on cutting force and deformation field space-time correlation, and relates to the field of aerospace, and the method comprises the following steps: 1, carrying out the spatial discretization of a circumferential contour region of a target workpiece; step 2, establishing a lightweight structure stiffness matrix; 3, constructing an elastic mechanism model and solving delta-F mapping; 4, introducing the time sequence correlation of the cutting load into the elastic mechanism model; step 5, constructing a Boolean relationship between the tool path and the workpiece envelope; step 6, fusing actual measurement process signals, and performing state correction; step 7, iterating the elastic model again by using the corrected load; and step 8, reconstructing full-field deformation based on spatial correlation. According to the method, online prediction of the station-level circumferential deformation field is achieved, the method can be used in the multi-procedure machining process, deformation in-situ compensation of the procedure is achieved, a clamping positioning strategy of the next procedure is output, and the manufacturing quality and the production efficiency are improved.

This invention discloses a traffic prediction method and system based on a spatiotemporal hierarchical network. The method includes: acquiring traffic data and preprocessing the data to construct a hierarchical regional augmentation network and a traffic feature matrix; using the hierarchical regional augmentation network and the traffic feature matrix as input to a prediction model, learning spatial and temporal correlations, and outputting prediction results; the prediction model includes a region-aware spatial correlation model and a region-aware temporal correlation model. The system includes a preprocessing module and a prediction module. By using this invention, the spatiotemporal correlations in traffic data are effectively captured, improving the accuracy of traffic flow prediction. This invention, as a traffic prediction method and system based on a spatiotemporal hierarchical network, can be widely applied in the field of traffic prediction.

The invention discloses an internet of things edge computing dynamic load prediction data processing method and system, and belongs to the technical field of edge computing. According to the method, edge node operation, network link quality and Internet of Things task characteristic parameters are collected, a time sequence prediction model is adopted to complete edge node load prediction, and related parameters are integrated to construct a task dynamic decision state space; computing task routing allocation is completed based on the state space, and a heterogeneous resource scheduling multi-objective optimization model is established and solved to obtain a scheduling scheme; according to the scheme, the original data of the Internet of Things is subjected to mixed compression, and a prefetching rule is constructed by combining compressed data access statistics and time-space association attributes, so that data prefetching and cache management are completed. According to the method, dynamic adaptation of the computing tasks and reasonable utilization of resources can be realized, data transmission bandwidth occupation is reduced, the cache hit rate of the edge nodes and the system operation stability are improved, and the real-time processing requirement of the edge computing scene of the Internet of Things is met.

This invention discloses a method for improving the grid-load interaction capability of high-energy-consuming industrial users under carbon emission constraints. The method includes: real-time acquisition of dynamic power output characteristic data from new energy sources, capturing key dynamic features such as instantaneous power fluctuations, ramp rate changes, and spatiotemporal correlations of wind farms and photovoltaic power plants; constructing an adaptive generation model for multimodal risk scenarios, and using cluster analysis and stochastic process theory to summarize the complex stochastic fluctuations of new energy power output into several representative typical risk modes. The robustness-first rolling decision optimization mechanism of this invention prioritizes ensuring system power balance and safety under all generated risk scenarios in its core objective function and constraints. This fundamentally overcomes the decision failure risk caused by prediction bias in traditional methods, thus providing the power grid with excellent resilience and anti-interference capability under complex and uncertain operating conditions.

The invention relates to the technical field of new energy power system analysis, in particular to a new energy output scene generation method considering weather space-time correlation and energy endowment. Comprising the following steps: acquiring gridding meteorological historical data, new energy station equipment parameter data, wind and light historical output data and energy abundance and dryness characteristic data of a target area; processing the obtained data, and constructing a standardized data set; based on the standardized data set, extracting association rules of different time scales and space scales of the target area; establishing an energy Fengxi endowment evaluation model, and establishing a mapping relation between the Fengxi grade and the new energy treatment characteristic; and verifying the generated scene sequence. According to the embodiment of the invention, the accuracy and comprehensiveness of a new energy station output scene generation result are remarkably improved by considering meteorological factor space-time correlation, constructing a target area new energy station multi-dimensional abundance and dryness evaluation system and carrying out coupling fusion on conventional and extreme scenes.

This invention relates to time series prediction of UAV faults based on entropy weight fusion and a time-graph convolutional network (T-GCN). It acquires data on the UAV's acceleration, angular velocity, and angle in the x, y, and z directions using sensors; combines the data into a matrix and calculates the entropy weight fusion value; determines the threshold value for fault occurrence based on the chi-square distribution; and constructs graph data for input into a T-GCN model based on the relationships between the sensor data. n historical time data points are segmented from the graph data and input into the T-GCN model. After inputting n historical time data points, the T-GCN model can predict data at time T in the future. After model training, it captures the spatiotemporal correlation of the data obtained from the UAV's sensors and predicts the graph data based on the time series. The prediction results are accurate and reliable, facilitating timely maintenance of quadcopter UAVs.

The invention provides a high-power variable pitch system adaptive to an SL1500 wind turbine generator and a temperature control method, and is applied to the technical field of data processing. According to the method, the problem that the temperature of the variable-pitch motor of the SL1500 wind turbine generator is high is solved, unit operation data, variable-pitch system influence factors and abnormal state information of the motor are obtained firstly, and rated torque and operation temperature basic data of the variable-pitch motor are obtained through preprocessing; the temperature control improvement probability of the high-power variable-pitch system is calculated by dynamically fusing engine reinforced blade bearing wear and lubrication state weights and combining space-time correlation analysis and a regression model, and temperature anomaly feature information is generated by comparing data before and after technical improvement. The method comprises the following steps of: obtaining a multi-modal feature, processing the multi-modal feature to obtain an overload heating identification result, generating a risk influence factor in combination with a multi-task decision matrix and a technical improvement key index, and finally outputting a dynamic early warning result of over-high motor temperature by means of a time-space correlation early warning engine and a multi-target decision, thereby realizing problem solving and risk management and control.

This invention relates to the field of intelligent extraction of agricultural plastic mulch, and discloses a method for extracting agricultural plastic mulch by integrating multimodal remote sensing and deep learning. The method includes acquiring multi-temporal multispectral optical remote sensing images, SAR remote sensing images, and stereo image pairs of remote sensing images during key phenological periods of the plastic mulch crop, and processing them accordingly. A corresponding training sample set is constructed based on the processed remote sensing images. A deep semantic segmentation model containing four multi-temporal multimodal feature fusion methods is trained to obtain a target plastic mulch and plastic greenhouse extraction model, extracting the spatial distribution of plastic mulch and plastic greenhouses in the region. This method comprehensively considers the differences in spectral and structural features of ground objects and three-dimensional height information. By utilizing multimodal remote sensing and deep learning, it fully explores the complementary information and spatiotemporal correlations between multi-temporal and multimodal data, improving the extraction accuracy of regional plastic mulch and plastic greenhouses. It requires less manual intervention, has a high degree of automation, and has strong universality, making it easy to promote and apply at the regional scale.

The application relates to a data feature engineering processing method for improving distributed photovoltaic prediction accuracy, which comprises the following steps: initial data identification based on an iForest algorithm; data reconstruction through super-resolution reconstruction-double-channel convolutional neural network to obtain reconstructed data; correlation analysis of the obtained reconstructed data through a Pearson correlation coefficient; and finding an optimal time offset input into a physical model through correlation analysis and Granger Causality Test (GCT), transforming wind speed and wind direction, and normalizing meteorological data to input data into a data-driven model for processing. The data processing of the physical model in the application is specially used for processing the space-time correlation of field data, and finally, feature generation technology is introduced to realize data feature extraction maximization of the data-driven model; missing data is identified and supplemented, so that the data missing problem of the distributed photovoltaic power station is solved, and the missing data after the supplement has higher correlation with the original data due to the double-channel processing of the neural network.

This invention discloses a multi-target tracking method for mining areas based on multi-scale attention and graph neural networks, belonging to the field of environmental perception for unmanned driving in mining areas. The method includes: multimodal perception and feature enhancement, employing a multi-scale attention mechanism for spatial, channel, and multi-scale adaptive weighting; target detection and re-identification feature extraction; temporal-spatial joint modeling, constructing a cross-frame target spatiotemporal map, and obtaining a spatiotemporal correlation probability matrix through graph neural network inference; obtaining a comprehensive correlation cost matrix based on mining area scene knowledge constraints, and using a knowledge base including static geography, dynamic operations, and equipment characteristics for hard constraint filtering and soft constraint optimization; trajectory lifecycle management, outputting a stable trajectory. This invention enhances feature robustness through multi-scale attention, achieves global spatiotemporal correlation through graph neural networks, and improves decision rationality through scene knowledge constraints, effectively solving the problem of target loss due to occlusion in complex mining environments.

This invention discloses a hail identification and tracking method and system for the entire life cycle of hail embryos, relating to the field of meteorological detection. The method includes: S1 acquiring dual-polarization radar volume scan data; S2 identifying hail embryo particles; S3 determining whether the hail embryo particles are new particles, and if so, creating a life profile; S4 tracking target particles in spatiotemporal correlation and updating the life profile; S5 determining whether the target particles have transformed into hail; S6 acquiring the current life profile of the hail particles; S7 determining whether hail precipitation conditions have been triggered, and if so, proceeding to S8, otherwise returning to S1; S8 analyzing the location of the precipitation area; S9 spatially clustering the precipitation area location and generating a probability map of the hail precipitation area and early warning information. By establishing a "life profile" for each hail embryo particle that includes its trajectory, phase evolution, and environmental thermodynamic conditions, the complete process of hail embryo formation from initial formation to final hail precipitation is tracked. Based on the statistical analysis of the life profile, key transformation characteristics of different hail storm types are adaptively identified, thereby achieving early hail warning.

The invention discloses a vehicle fault-tolerant control method under the abnormal condition of a multivariable measurement sensor, and belongs to the crossing field of electric digital data processing and intelligent driving vehicle control technologies. The method comprises the following steps: firstly, based on spatial-temporal correlation and physical consistency constraints of multi-source sensor data, carrying out redundancy modeling and credibility evaluation on a key state of a vehicle; then, when it is detected that the sensor is abnormal, real-time compensation and correction of failure observation are achieved through a state reconstruction model driven by redundant data; and further combining a rapid fault identification and virtual sensor switching mechanism to construct a fault-tolerant control strategy for the minimum function demand of the vehicle so as to ensure that the vehicle can still maintain basic driving and safety control capability under the condition that the functions of part of sensors are damaged. According to the invention, the real-time performance of the system is ensured, the safety and robustness of the intelligent driving vehicle in a complex environment and an abnormal condition are obviously improved, and the method is suitable for a multi-sensor fusion intelligent vehicle control system.

This specification relates to the field of compressed air energy storage technology, specifically disclosing a condition monitoring system and method for underground gas storage facilities. The system includes: an acoustic monitoring well located in the upper part of the underground gas storage facility, equipped with a multi-component shear wave detector for acquiring creep or deformation monitoring data of the roof and surrounding rock; a microseismic monitoring well located in the lower part of the underground gas storage facility, equipped with an underground microseismic detector array for acquiring rupture event monitoring data of the surrounding rock; a fault leakage monitoring device, including a tracer gas injector and a tracer gas detection mechanism, for acquiring surface gas concentration monitoring data; and a data processing device connected to a multi-dimensional monitoring network for performing spatiotemporal correlation and cross-validation analysis on the received multi-dimensional monitoring data to generate a fusion assessment result of the structural integrity status and sealing safety risk of the underground gas storage facility. The above solution enables high-precision monitoring and assessment of underground gas storage facilities.