307 results about "Data space" patented technology

The invention discloses a power grid load prediction and scheduling optimization system based on artificial intelligence, particularly relates to the technical field of power system automation, and solves the technical problems of low power grid load prediction precision, poor scheduling strategy robustness and insufficient source grid load storage coordination in the prior art. Multi-source heterogeneous data space-time alignment is realized by constructing a data acquisition layer based on edge calculation, a load prediction result is generated by adopting an AI prediction module fused by a graph convolutional network and an attention mechanism, and a source-network-load-storage collaborative scheduling scheme is generated through a multi-target risk hedging optimization algorithm. And closed-loop optimization is realized by using digital twinborn pre-check and incremental learning. And finally, the load prediction accuracy, the scheduling decision reliability and the system adaptive capability in the new energy access environment are improved.

The invention discloses a modeling method based on shield tunneling data feature analysis and parameter relevance, and relates to the field of tunnel engineering data processing. The method comprises the steps that shield tunneling time sequence parameters are obtained, and a non-uniform time sequence is resampled into a space-aligned standardized footage domain sequence through state cleaning and coordinate domain transformation; by means of mixed variable rejection and lagging correlation analysis, environment common cause interference is stripped, physical response delay among parameters is recognized, and a time-delay directed correlation graph model is constructed; and inputting the footage domain sequence and the graph model into a graph neural network, performing feature learning by using a time delay compensation aggregation mechanism, and outputting a key parameter influence degree set with symbols based on a prediction gradient. According to the method, the problem of data space-time dislocation caused by propelling speed fluctuation and the problem of parameter relevance misjudgment caused by physical response lag are solved, and accurate identification and explanation of shield tunneling key parameters are achieved.

The invention relates to the technical field of metallurgical raw material batching control, and discloses a bottom blowing smelting furnace automatic batching intelligent control method and system based on deep learning, and the method comprises the steps: achieving intelligent batching through multi-source data fusion, physical constraint modeling and dynamic optimization control; edge calculation is adopted to realize data space-time alignment and purification, and physical and economic mixed features are constructed; modeling a reaction path based on a graph neural network, and embedding conservation law constraint to synchronously predict key process parameters; and finally, in combination with gradient sensitivity analysis and reinforcement learning, constructing a differentiable optimization framework to realize multi-target dynamic ratio decision and real-time compensation control, and forming a perception-decision-execution closed loop. The system comprises a global sensing and data purification module, an intelligent decision-making and optimization batching module and a high-precision execution and closed-loop control module. According to the invention, the batching strategy is adaptively adjusted, and optimal resource allocation and maximum economic benefit are realized.

The invention relates to the technical field of motor fault diagnosis, in particular to a motor fault diagnosis algorithm based on multi-sensor fusion, and the algorithm comprises the steps: injecting a step excitation signal into a motor, synchronously collecting the original response waveforms of vibration and current sensors, and calculating the inherent response delay. Establishing a mapping relation library of delay values and current sensor filtering parameters, calling the delay values in real time according to the filtering parameters, performing reverse time offset compensation on a current harmonic signal time sequence, performing time alignment on the two types of data, finally performing cross-domain coupling analysis on the aligned data, extracting vibration pulse peak frequency and current harmonic fluctuation quantity, and determining the vibration pulse peak frequency and the current harmonic fluctuation quantity. Early faults are judged by combining the bearing outer ring fault characteristic frequency band and the load rate dynamic threshold value, graded alarm is generated by tracking characteristics, the problem of fault false judgment and missed judgment caused by sensor data space-time dislocation is solved, and the early fault diagnosis accuracy of the motor is improved.

The invention relates to the technical field of ecological environment monitoring, and discloses a coastal wetland intelligent monitoring method and system based on artificial intelligence, and the method comprises the steps: collecting unmanned plane data, satellite remote sensing data, Internet of Things sensor data and water quality monitoring buoy data of a coastal wetland; the method comprises the following steps: processing satellite remote sensing data by adopting a wavelet threshold denoising algorithm based on an attention mechanism, calibrating Internet of Things sensor data by adopting an LSTM network, and carrying out data space-time alignment based on a space-time attention fusion model to obtain preprocessed data; inputting the preprocessed data into a Transform-ResNet hybrid model to carry out environmental change evaluation, and outputting an ecological health index; when the predicted ecological health index is lower than a threshold value, a PPO algorithm is adopted to dynamically adjust a monitoring strategy according to the early warning level, and an early warning report is pushed; the whole process is intelligent, manual intervention is greatly reduced, and support is provided for coastal wetland ecological protection.

The invention relates to the technical field of safety monitoring of constructional engineering, in particular to an intelligent construction safety monitoring system and method based on multi-modal data fusion, and the system comprises a plurality of modules: a 5GMEC-based heterogeneous data space-time alignment module which completes coordinate system conversion between laser point cloud and a BIM model by using an improved Fast-ICP algorithm; the feature level fusion network comprises a geometric feature branch (improved unscented Kalman filter processing point cloud normal vector) and a time sequence feature branch (LSTM processing stress sensor data); the NeRFXL fusion engine is used for fusing the point cloud and the video data by adopting a multi-scale neural radiation field; the multi-task anomaly detector is used for constructing a hierarchical Transform architecture and introducing a modal gating mechanism; and the dynamic knowledge graph engine constructs a knowledge graph updating module based on a graph neural network, and all the modules are in communication connection with one another and work cooperatively, so that functions of data processing, fusion, anomaly detection, knowledge updating and the like are realized.

A civil aircraft component fault diagnosis method based on conditional diffusion in a variable working condition scene comprises the steps that limited high-confidence-coefficient fault samples and label-free fault data under different working conditions are collected, after hidden space feature reconstruction is carried out, a denoising diffusion probability model is adopted to carry out conditional generation augmentation on target domain high-confidence-coefficient fault samples of a target domain, and the target domain high-confidence-coefficient fault samples of the target domain are obtained; and through joint optimization of an objective function, a mapping relation between a generation feature and a diagnosis decision is restrained while fault sample condition distribution is deduced, and collaborative improvement of generation quality and model generalization ability is realized. According to the method, the fault sample generation and fault cross-domain decision-making process is jointly optimized through the conditional diffusion-based fault diagnosis algorithm (CDFD), so that the fault diagnosis classifier can fully sense noise hidden space and data space characteristics of a source domain and a target domain, and the robustness is effectively improved.

The invention discloses a deep learning and machine learning fused soil moisture inversion construction method, and relates to the technical field of measurement of physical properties of materials, and the method comprises the steps: capturing complementary information and spatial context of multi-source data through a multi-source heterogeneous data space-time adaptive fusion step by using a cross-modal attention mechanism and a graph neural network; through a deep learning and machine learning dual-path collaborative inversion step, advantage complementation is realized by combining data-driven nonlinear modeling and a physical constraint interpretable model; according to the method, the defects of single data source, insufficient model generalization ability and incomplete physical mechanism consideration in the prior art are overcome, the inversion precision is improved by 12%-18% under the complex earth surface condition, and the method has the advantages that the method is suitable for large-scale popularization and application. And a high-precision, strong-generalization and reliable technical means is provided for precise monitoring of soil moisture.

The invention relates to the field of building construction safety, in particular to a risk prediction method and system for building construction. Aiming at the defects of multi-source data isolated analysis, dynamic risk response lagging, insufficient prediction precision and the like in the prior art, a unified analysis base is formed by constructing a space-time fusion data space and integrating multi-dimensional dynamic data such as structure micro-deformation monitoring, environmental parameters, three-dimensional live-action scanning, personnel positioning, a building information model and the like; based on a deep neural network architecture, designing a multi-modal feature extraction mechanism to quantify the coupling risk, and generating a partition risk probability distribution diagram; and in combination with a construction stage characteristic matching security policy library, implementing a three-level early warning mechanism and an automatic avoidance instruction. A closed-loop optimization mechanism is introduced, model parameters and decision threshold values are dynamically adjusted through actual accident feedback, and continuous evolution of a prediction system is achieved. According to the method, the active prevention and control capacity of compound accidents such as collapse and high-altitude falling is remarkably improved, and a self-adaptive intelligent protection system is constructed for a construction site.

The invention discloses a building full life cycle-oriented BIM data asset precipitation and operation management system. The system comprises six modules including a multi-source data conflict resolution module, a Beidou grid coding association module, a space-time fusion index module, an AEM asset integration module, a full cycle BIM processing module and a data asset operation module. According to the system, a building multi-source data conflict resolution and lightweight algorithm, a Beidou grid coding space-time asset association model and a data space-time fusion and spatial index model are utilized, and an Adobe Explosion Manager Assets platform is combined, so that BIM data of the whole life cycle of a building is processed. Through module collaboration, multi-source data conflicts are eliminated, data space-time association and fusion are achieved, the data are integrated to a professional platform, and a full-cycle data association graph is formed and operated. According to the system, the problems of low data integration efficiency, insufficient time-space association and the like in the prior art are solved, effective precipitation and efficient operation of BIM data assets are realized, and support is provided for refined management of buildings.

The invention belongs to the technical field of visual processing, and particularly relates to a time sequence NDVI crop distribution extraction method based on a mask auto-encoder, which mainly comprises four steps. Firstly, time sequence NDVI data are prepared, a multi-temporal remote sensing image in a complete growth cycle of target crops in a target area is obtained and processed, and the recognition precision is improved by using NDVI feature changes in the growth stage of the crops. And then performing time sequence transformation on the ViT model, dividing data space dimensions, completing Patch flattening and embedding, enabling the Patch to be adaptive to time sequence data, and simultaneously performing image processing advantages. Then, model training is carried out, a mask auto-encoder is pre-trained in a self-supervised mode through a large amount of unlabeled data, and then supervised fine tuning is carried out through a small amount of labeled data; and finally, processing new data by using the fine-tuned model, obtaining pixel-level crop category prediction, and generating a complete distribution map. According to the method, by means of time sequence data and model transformation, crop types and growth stage differences are effectively distinguished, and accurate extraction is achieved.

The invention discloses a high-strength steel welding defect nondestructive detection and identification method based on multi-modal data fusion. The method comprises the following steps: welding data acquisition: acquiring a two-dimensional image and three-dimensional point cloud data of a high-strength steel welding part to form a data pair; performing data space alignment: generating a space-aligned image-depth map data pair; feature extraction and fusion: obtaining fusion features with spatial geometric information and two-dimensional visual information through feature extraction and fusion; defect identification and classification: identifying defect pixels, decoding and recovering space information of a defect area, calculating the three-dimensional size of the defect, taking the feature information associated with a connected domain of each pixel with the defect and the three-dimensional size as input, and automatically classifying defect categories through a pre-trained full-connection neural network classifier. According to the method, the welding defects of the high-strength steel can be accurately identified and accurately and quantitatively analyzed.

The invention discloses an enterprise carbon emission measuring and calculating method and system based on production activity carbon footprint analysis, relates to the technical field of enterprise carbon emission, and effectively solves the problem of difficulty in data space-time alignment and process association in a complex production process by introducing a time sequence mapping and process feature decoupling mechanism. Multi-source heterogeneous data are fused in the feature recognition stage, and the integrity and interpretability of energy consumption behavior modeling are improved by combining graph structure modeling and causal attribution analysis methods; structured expression and dynamic weight updating of a carbon emission path are realized based on a carbon emission calculation map, and the response capability and traceability of the model to working condition changes are enhanced; high efficiency and adaptability of carbon emission measurement and calculation are realized through distributed path analysis and a carbon factor dynamic adjustment mechanism; the finally output time-phased and process-divided carbon emission result provides a scientific basis and technical support for an enterprise to carry out refined carbon performance evaluation, carbon asset management and green transformation decision-making.

The invention provides a geological disaster prediction method and device fusing space-time sequence analysis and causal reasoning, and belongs to the technical field of geological disaster monitoring and early warning. Aiming at the problems of non-uniform data space-time reference, lack of causal logic, poor real-time performance and weak scene adaptability of a model in the prior art, the method comprises the following steps: performing standardization processing on acquired multi-source data, processing missing values by adopting an improved K nearest neighbor algorithm in combination with stratum characteristics, and processing abnormal values through a 3 sigma criterion and geological verification; based on an information theory and an improved SURD algorithm, three types of causal entropies among variables are calculated, a time attenuation coefficient is introduced, a core causal chain is constructed, and a dynamic causal graph is constructed; a core causal variable is used as input, a multi-feature attention-multi-relation space-time diagram recursive network model is constructed, a hour-level predicted value is output through space-time diagram convolution, residual training and a geological physical constraint layer, and'causal-space-time 'fusion is realized through a causal weight adjustment model; the method can be widely applied to early warning of geological disasters such as landslide and debris flow.

The invention belongs to the technical field of root cause analysis in intelligent operation and maintenance, and discloses a method for mining fault propagation weight parameters, and the method comprises the following specific steps: S1, supervising data root cause probability initialization, S2, supervising data topology perception labeling, S4, unsupervised data space-time slicing, S5, multi-modal root cause reasoning, S6, parameter increment fusion and S7, online adaptive optimization. Precise modeling and dynamic adaptation are achieved through multi-stage collaborative optimization, and the root cause positioning capacity of a complex system is remarkably improved: a supervised and unsupervised data dual-drive strategy is adopted, a root cause probability baseline is constructed by utilizing work order history, and alarm streams are processed in combination with time-space slice standardization to form a structured knowledge base; the method comprises the following steps: quantifying a transition probability in a CMDB dependency relationship through topology perception annotation and a four-dimensional observation matrix, and constructing a feature matrix containing TP / FP counting to support accurate calculation of a probabilistic graph model;

The invention discloses a high boundary sensitivity three-dimensional inversion method based on an electromagnetic gradient constraint matrix, and belongs to the technical field of electromagnetic signal inversion, and the method comprises the steps: constructing an initial inversion model and a target function, and introducing a model roughness item based on electromagnetic gradient constraint into the target function; calculating the electromagnetic gradient anomaly response at each measuring point under each measuring frequency, obtaining an electromagnetic gradient anomaly response vector of each measuring point, performing normalization processing, combining into a matrix, obtaining an overall normalized observation gradient anomaly matrix, and constructing an electromagnetic gradient constraint matrix in a data space; converting the electromagnetic gradient constraint matrix in the data space into a model space through a frequency-depth weighted mapping matrix; and solving the target function by adopting an iterative optimization algorithm until the model converges. According to the method, the gradient constraint matrix based on the electromagnetic field is introduced, the dynamic adjustment of the change amplitude weight of each grid unit in the roughness item of the model is realized, and the target boundary identification precision is improved.

The invention discloses a vehicle test boundary scene generation method and system based on a pre-boundary scene. The method comprises the following steps: acquiring a first feature data set; obtaining and discriminating a real boundary scene and a real pre-boundary scene based on the first feature data set and a preset risk discrimination criterion; constructing a second feature data set based on the real pre-boundary scene data and the discriminated real boundary scene data; based on the real pre-boundary scene data, utilizing the generation model to obtain generated pre-boundary scene data; training a prediction model based on the real pre-boundary scene data and the discriminated real boundary scene data; and obtaining test prediction boundary scene data based on the generated pre-boundary scene data and the prediction model. According to the method provided by the invention, the test prediction boundary scene data is acquired based on the real pre-boundary scene data, and data expansion is performed on the real boundary scene data by using the generation model and the prediction model, so that the technical problem of sparse generated test scene data caused by limited scene generation data space in the prior art is solved.

The invention discloses an intelligent groundwater storage variable prediction system based on multi-source data fusion and parameter optimization, and relates to the technical field of groundwater storage change prediction, and the system comprises a data collection module, a hydrological sensor connected with the Internet of Things, a satellite remote sensing data interface and a meteorological data interface. According to the intelligent groundwater storage variable prediction system based on multi-source data fusion and parameter optimization, the problem of prediction time lag caused by data updating lag, data spatial-temporal scale inconsistency and parameter stiffness of a groundwater storage change evaluation system is solved by constructing a double-circulation collaborative architecture of a real-time assimilation ring and a parameter evolution ring. High-frequency assimilation is used for fusing multi-source real-time data, and it is ensured that model input is synchronous with a real environment state; the prediction system continuously tracks the transient process of an underground water system, and the timeliness and reliability of underground water storage volume change prediction under the scenes of extreme rainfall, extreme drought, high-intensity mining and the like are effectively improved.

The invention discloses a project quality detection method and system based on green building design, and relates to the technical field of project quality detection. The engineering quality detection method and system based on the green building design comprises the steps that S1, BIM structured data, real-time monitoring data, structure acceptance data and construction event and window data are preprocessed; s2, on the basis of BIM structured data and in combination with a BIM model, the qualification state of the component and the quality grade of the partition are judged; s3, identifying a working condition switching anchor point and an event, and performing segmented division of a working condition process; s4, feature clustering and performance state discrimination are carried out, different working condition types are identified, and tagging management is carried out; s5, identifying dominant factors and performing attribution assignment through multi-dimensional label attribution and traceability judgment; and S6, performing grade judgment and grade response through multi-source time sequence item-by-item difference analysis. The problems of state interval disjunction and difficulty in accurate attribution and stable diagnosis caused by space-time mismatching of construction progress, BIM nodes and energy consumption environment data and data asynchronization are solved.

The invention discloses an earth surface downlink short-wave radiation space downscaling method based on a super-resolution reconstruction technology, belongs to the technical field of meteorological data analysis, and solves the problems of insufficient solar radiation data space resolution and weak earth surface heterogeneity characterization capability in the prior art. Carrying out super-resolution reconstruction on the radiation data set by adopting a super-resolution combination model, and carrying out weighted fusion on a reconstructed data set output by the super-resolution combination model; according to the method, the output results of the multiple models are subjected to weighted fusion, weight optimization of the super-resolution sub-models is performed in combination with the high-resolution terrain factors, spatial downscaling from 5km to 1km resolution is finally realized, the spatial precision of the solar radiation data is remarkably improved on the basis of keeping the hour-level time resolution of the original data, and the accuracy of the solar radiation data is improved. And more earth surface heterogeneity information can be captured, and meanwhile, the high-time-resolution application requirement is met.

The invention discloses a cross-modal causal coupling water supply network cascade failure deduction method and system, and relates to the field of safety prevention and control of urban water supply networks. The method comprises the following steps: firstly, constructing a water supply network heterogeneous graph, abstracting physical pipe sections as core nodes, associating the pipe sections as heterogeneous edges, and obtaining static and dynamic attributes of the heterogeneous edges; multi-source heterogeneous data space-time alignment is completed through an exclusive scale adaptive coding layer, and a standardized feature matrix is generated; taking the cross-modal causal directed acyclic graph as a hard constraint, and generating a dynamic coupling adjacency matrix through a cross-modal graph attention layer; dynamic weighted coupling of multi-modal characteristics is realized through a space-time dynamic gating unit; finally, cascade failure evolution is simulated based on the coupling characteristics and the adjacent matrix, critical inflection points are recognized, and graded early warning signals are output. The method solves the problems of modeling distortion, false correlation and inflection-point-free pre-judgment in the traditional technology, greatly improves the deduction precision and the engineering credibility, and is suitable for safety prevention and control and emergency early warning of the urban water supply pipe network.

The invention relates to the technical field of semiconductors, and discloses a semiconductor defect and yield Map-based automatic calibration method, which comprises the following steps of: acquiring wafer surface data in real time through a multi-modal sensor, analyzing station process parameters, and generating a time-space aligned wafer holographic data set; performing nonlinear deformation compensation based on the wafer holographic data set to generate a dynamic Die grid; utilizing a multi-task deep reinforcement learning model to predict initial Offset parameters of the defect Map and the yield Map; and performing global optimization on the initial Offset parameters through a quantum annealing algorithm, and outputting an optimal Offset. Data are collected through a multi-mode sensor, space-time alignment is carried out through Kalman filtering, a wafer holographic data set of space-time alignment is generated, the data space-time unification is achieved, the wafer state is accurately and completely reflected, and a high-quality data basis is provided for defect analysis and yield evaluation. The problem that data of the multi-modal sensor are inconsistent in time and space and are difficult to effectively fuse is solved.

The invention provides a traffic real-time road condition prediction method and system based on knowledge engineering, and the method comprises the steps: collecting multi-source traffic data in real time, carrying out the missing data filling and data space-time alignment processing of the multi-source traffic data, and obtaining the processed multi-source traffic data; fusing the processed multi-source traffic data by adopting a method based on dynamic space-time tensor modeling to obtain fused traffic data; based on the fused traffic data, constructing a self-evolution traffic knowledge graph with time-space attributes; on the basis of the self-evolution traffic knowledge graph, constructing a road condition hybrid prediction model by adopting a knowledge-guided meta-learning framework; and performing traffic real-time road condition prediction based on the road condition hybrid prediction model. According to the invention, the robustness of the system is obviously enhanced, and more accurate road condition prediction is realized.

The invention discloses a millimeter wave radar point cloud compression method based on heterogeneous representation and an implicit neural network. Firstly, point cloud fine geometric features are extracted through a point-based compression network, global semantics are aggregated through a voxel-based compression network, and point-voxel heterogeneous joint representation and preliminary compression are realized. Secondly, constructing an adaptive octree to code a compressed data space structure to generate a partitioned bit stream, and performing statistical compression on the voxel-level high-dimensional features by using context-aware entropy coding to generate a feature bit stream; then, joint transmission is performed on the partition bit stream, the feature bit stream and the neural network weight for implicit representation. And finally, decoding and recovering the coarse-grained point cloud at a receiving end, inputting the coarse-grained point cloud and a neural network weight into a point-based synthesis network based on implicit neural representation, and finally reconstructing a high-fidelity point cloud through continuous surface modeling and detail compensation. According to the method, the compression efficiency and the reconstruction quality of the millimeter wave radar point cloud under the low code rate are remarkably improved.

The invention discloses an electroencephalogram signal clustering method based on a Mama framework and comparative learning, and belongs to the crossing field of engineering application and information science, and the method comprises the following steps: collecting and preprocessing label-free electroencephalogram data; the method comprises the following steps of: constructing a Mama-based feature extractor, dividing an input electroencephalogram signal into a plurality of slices by adopting an electroencephalogram signal slice embedding strategy so as to obtain fine-grained local information, and modeling a potential context relationship by utilizing a slice perception scanning mechanism; the method comprises the following steps: constructing an original data view and an enhanced data view, and respectively inputting samples of the two data views into two Mamba feature extractors with shared network parameters for feature embedding; processing the feature pairs of the two views using an instance projector, constructing weighted instance level contrast learning wherein the distance in the original data space provides weight information; processing feature pairs of the two views by using a clustering projector, and constructing a clustering distribution discrimination contrast learning branch and a semantic perception contrast learning branch; and finally, performing joint training by using the comparative learning branches, and outputting a high-quality clustering result by ensuring feature, clustering distribution and semantic consistency.

The invention discloses a vehicle emission identification system and method based on artificial intelligence, and relates to the technical field of vehicle emission detection. The snapshot recognition module is provided with a multispectral camera array and a synchronous controller, dynamically adjusts imaging parameters in combination with a laser radar and a millimeter wave radar, and supports multi-type vehicle recognition; the AI algorithm processing module adopts a heterogeneous computing power architecture to allocate resources; the vehicle anomaly judgment module constructs a multi-dimensional anomaly feature chain, calls OBD data to study and judge anomaly and identifies blacklist vehicles; the data storage and calling module adopts block chain evidence storage and hierarchical storage; the mobile law enforcement adaptation module supports end-side cloud collaboration; the multi-source data fusion module realizes data space-time alignment; the dynamic model self-updating module optimizes the model based on federal learning. The method improves the comprehensiveness of vehicle identification and abnormity determination, guarantees the law enforcement data to be legal and credible, relieves the computing force pressure of a mobile terminal, supports model continuous adaptation technology iteration, assists efficient mobile law enforcement, and provides powerful support for environmental protection law enforcement.

The invention discloses a charging equipment self-diagnosis method and system based on multi-source heterogeneous data space-time fusion in the technical field of charging equipment diagnosis. The method comprises the following steps: performing data preprocessing on an obtained charging pile data set to obtain a processed data set; according to the processed data set, constructing a charging pile real-time topology network based on a dynamic graph convolutional network, and extracting a multi-dimensional spatial feature matrix; according to the processed data set, extracting a multi-dimensional time feature matrix based on a lightweight long-short term memory network; performing feature fusion based on an attention feature fusion algorithm according to the multi-dimensional spatial feature matrix and the multi-dimensional time feature matrix to obtain a spatial-temporal feature matrix; and according to the spatial-temporal characteristic matrix, realizing multi-scale anomaly detection based on a variational auto-encoder algorithm after adversarial training enhancement and sliding window dynamic statistics. According to the method, the blindness and resource consumption of troubleshooting can be effectively reduced, and real-time data support is provided for equipment update decision and maintenance resource allocation.

The invention discloses a composite game reinforcement learning method and device for a four-rotor unmanned aerial vehicle cluster, and relates to the technical field of unmanned aerial vehicle cluster control. The method comprises the steps that an evaluation-execution network of the unmanned aerial vehicle is used for directly approaching a value evaluation and cooperation strategy online in a state-control input data space of cluster flight, repeated offline solving of a high-dimensional coupling HJ equation set under strong coupling nonlinear dynamics of the unmanned aerial vehicle is avoided, and therefore the modeling precision requirement and the calculation overhead are remarkably reduced. By introducing a filtering mechanism with a'forgetting factor ', weighted integral processing is performed on samples such as pose / speed / relative formation errors, control instructions, energy consumption and safety cost collected in the historical flight process of a cluster, and a composite'evaluation' network error and regression signal fusing current and historical information is constructed; and a finite excitation criterion which can be inspected on line is given on an information matrix level, so that the unmanned aerial vehicle cluster can still keep effective learning under the condition that a continuous PE condition is not met.

The invention relates to the technical field of flexible production and industrial intelligent control, in particular to a liquid crystal display screen production line operation management method and system. Comprising the following steps: performing timestamp alignment and de-noising processing on multi-source heterogeneous data; inputting the physical state feature vector into a two-channel hybrid reasoning unit to generate a comprehensive prediction reference; calculating a difference value between the physical state feature vector and the comprehensive prediction reference, and generating an original residual vector; carrying out topological structure analysis on the original residual vector to generate a residual semantic signal; performing drift judgment on the dynamic tolerance boundary to generate a corresponding dynamic operation mode instruction; in response to a dynamic operation mode instruction indicating benign line change fluctuation, constructing an evolved cognitive model; adaptive closed-loop control from data space to physical space is constructed in response to a dynamic operational mode instruction indicating a malignant anomaly. According to the invention, a reliable basis is provided for subsequent differential regulation and control, and continuity and stability of operation of a production line are ensured.

The invention discloses a secondary equipment hidden fault identification method and system based on transient state-steady state data space-time alignment. The method comprises the following steps: collecting transient state recording data and steady state SCADA (Supervisory Control And Data Acquisition) data; unified time service is carried out on transient state and steady state data, and a time mark error is corrected; performing Lagrange interpolation resampling on the transient data to generate a virtual sequence aligned with a time axis of the steady-state data; constructing a cost matrix for the resampled transient sequence and the resampled steady-state sequence, and backtracking a shortest path to obtain an aligned fusion sequence; inputting the fusion sequence into a multi-scale feature fusion network, and outputting a fault type and confidence; the system comprises a data acquisition module, a clock drift correction module, a resampling alignment module, a dynamic time warping module and a fault feature extraction module. According to the method, the fusion problem caused by sampling rate difference, clock drift and dimension difference of transient and steady data is solved, millisecond-level space-time alignment is realized, and the precision of data fusion is remarkably improved.