918 results about "Feature screening" patented technology

The invention belongs to the technical field of high polymer material design and intelligent manufacturing, and discloses a machine learning driven epoxy resin based composite material thermal-mechanical property aided design method, which comprises the following steps: S1, data acquisition and feature construction; s2, performing feature screening; s3, constructing and training an interpretable prediction model; s4, carrying out reverse design and optimization; and S5, performing closed-loop verification and updating. According to the method, the quantitative relation of structure-process-performance is constructed through an interpretable machine learning model, and the contribution mechanism of each factor is revealed by means of SHAP analysis. And finally, reversely designing an optimal epoxy resin monomer structure and a matched curing process according to the performance target. The limitation of a traditional trial and error method is broken through, collaborative optimization of the material structure and the forming process can be achieved, and the development efficiency of the epoxy resin-based carbon fiber composite material is remarkably improved.

The invention belongs to the technical field of flight simulator maintenance, particularly relates to a flight simulator predictive maintenance method based on machine learning, and solves the problems that existing maintenance depends on regular inspection and passive maintenance, fault early warning lags behind, and the false and missing report rate is high. The method comprises the following steps: acquiring historical operation data, sensor time sequence data, fault records and environmental parameters of a flight simulator, and carrying out cleaning, labeling and feature fusion preprocessing on the historical operation data, the sensor time sequence data, the fault records and the environmental parameters; constructing a composite health feature set containing statistical features, dynamic health state values and aerial material reliability parameters; a mixed prediction model (random forest feature screening + LSTM time sequence prediction + adaptive correction reliability evaluation) is adopted to train a model, prediction result fusion analysis and multistage decision rule post-processing are combined, and a maintenance work order and a spare part demand plan are output. According to the method, the accuracy and timeliness of fault prediction are improved, the maintenance conversion from passive response to active pre-judgment is realized, and the maintenance cost and the non-planned shutdown risk are greatly reduced.

The invention discloses a drainage pipeline defect detection system and method based on multi-scale feature fusion and occlusion perception, and the system comprises a data set construction module which is used for constructing a drainage pipeline data set covering multi-defect, multi-scale and multi-occlusion scenes; the model training module is based on establishment of a defect collaborative detection model, and a backbone network of the model training module adopts a feature pyramid sharing convolution module to reinforce the multi-scale detail extraction capability; the neck network introduces an advanced screening path aggregation network and a selective feature fusion module to realize dynamic feature screening and fusion; the detection head is integrated with an MCFEM module, and shielding perception and scale adaptability are enhanced. According to the method, the problems of high omission ratio and poor robustness caused by large defect scale change, serious shielding and complex background in drainage pipeline detection are effectively solved, the small target defect identification precision and the shielding scene detection accuracy are remarkably improved, and the method is suitable for high-precision and light-weight detection of multi-scale defects in a complex shielding environment.

The invention discloses a system and a method for predicting postoperative recurrence risk after triple negative breast cancer neoadjuvant therapy based on multi-modal time sequence medical image data, and belongs to the field of medical image analysis. The system comprises a data processing module used for constructing a multi-modal data set; the multi-modal feature extraction and screening module is used for extracting deep learning, radiomics and tumor habitat features from the region and carrying out feature screening; the model training module is used for constructing a time sequence model based on a Transform architecture and carrying out training through a multi-task learning strategy integrated with time consistency constraint and gene association auxiliary loss; and the recurrence risk prediction module is used for loading the trained model and outputting a recurrence probability and a risk level. According to the method, the multi-modal time sequence image and gene information are fused, so that the recurrence risk of the triple negative breast cancer patient is dynamically and accurately quantified, and support is provided for clinical individualized treatment decision.

The invention provides a mining area surrounding soil heavy metal spatial distribution inversion method based on hyperspectral data, and the method comprises the steps: processing multi-source monitoring data of mining area surrounding soil, and obtaining a consistent reflectivity data set; performing soil spectrum purification based on the consistent reflectivity data set to obtain a pure soil signal; spectrum key features are screened out from the pure soil signals; obtaining a modeling data set in combination with the spectrum key features and the heavy metal concentration labels so as to construct a multi-task inversion model, and outputting each metal prediction interval and an over-standard risk probability graph; and outputting a multi-layer package based on the model, performing global and local interpretation and mechanism verification, and generating an interpretation report and traceable evidence. According to the method, full-link unification and purification can be achieved, domain deviation and mixed pollution are remarkably reduced, the accuracy and interpretability of feature screening can be considered, and the model generalization ability, prediction accuracy and space credibility are improved.

The invention relates to the technical field of spacecraft orbit control and space safety, and discloses a space debris orbit prediction and avoidance method, which comprises the following steps that: a ground computing center screens candidate targets and generates enhanced data packets for uploading; the spaceborne computer combines real-time navigation data to carry out geometric feature screening, and an instant high-risk target list is generated; controlling the star sensor to execute optical observation, and resolving an optical measurement relative orbit state by utilizing displacement superposition and Kalman filtering; selecting a high confidence coefficient or a conservative probability threshold according to the optical measurement state acquisition condition, and generating an avoidance instruction when the collision probability exceeds the limit; and the propulsion system responds to the instruction to execute avoidance, verifies the effect by confirming the semi-major axis variation and executes track recovery by selecting an aircraft. According to the method, the ground-air collaborative screening and shift superposition enhancement technology is adopted, the problems that the low-precision ephemeris false alarm rate is high and the detection capability of a satellite-borne sensor on a dark and weak target is insufficient are solved, and the accuracy of evasion decision making and the execution reliability are improved.

The invention relates to the technical field of semiconductor manufacturing, in particular to a wafer pre-alignment device and method, and the device comprises a mounting platform, an XY motion platform, a rotating platform, a suction cup, and an edge detection assembly. The method comprises the steps of device correction, coarse scanning and circle center positioning, fine scanning and notch accurate positioning and alignment execution. Through software and hardware cooperation, online self-correction is used for eliminating system errors, high-order fitting and feature screening algorithms are used for restraining random errors and model errors, the repeated positioning precision, notch orientation precision and equipment consistency of the wafer pre-alignment device are improved, and the high-precision requirement for a wafer transmission system under the advanced manufacturing process can be met.

The invention relates to a few-sample image classification method based on hyperbolic space image-text local feature alignment, and belongs to the technical field of image recognition and artificial intelligence, and the method comprises the steps: generating word-level attribute description for a support set image through employing a multi-mode large language model; encoding the image and the text by adopting a vision-language model; constructing a hyperbolic local feature alignment module in a hyperbolic space, screening most relevant image local features for text local features by calculating hyperbolic cosine similarity, and fusing by using hyperbolic weighted average; designing a hyperbolic cross attention module, and aggregating key information from the multi-modal local features of the support set to construct a category prototype by taking query image aggregation features as guidance; and finally performing classification based on the hyperbolic geodesic distance. According to the method, the hierarchical modeling capability of the hyperbolic space and the semantic priori knowledge of the large language model are fully utilized, fine-grained multi-modal feature alignment is realized, and the small sample image classification performance is improved.

The invention relates to the technical field of fault diagnosis, in particular to a multi-modal bearing fault diagnosis method based on cross-domain transfer learning, which comprises the following steps: constructing a source domain and a target domain; calculating fault frequency characteristics and revolution frequency of the bearing; calculating a first feature vector; performing correlation coefficient and strategy feature standardization pipeline operation, collaborative feature screening strategy and dimension reduction on the first feature vector, and obtaining a second feature vector by using inherent importance and arrangement importance of a random forest classifier; training a plurality of benchmark test models by using the second feature vector of the source domain; evaluating the effectiveness of the second feature vector and determining a performance baseline; and training the target domain by using the 1D-CNN network, carrying out end-to-end cross-domain migration training on the 1D-CNN network by using the comprehensive loss function of the source domain and the target domain, and outputting a predicted fault type. The problem that the accuracy of transfer learning is affected due to lack of multi-modal data screening in an existing method is solved.

The invention relates to the technical field of energy prediction, in particular to an energy short-term load prediction method and system based on SE-Block improved Transform. The method comprises the steps of performing reversible normalization preprocessing based on acquired multi-element load sequence data; carrying out feature extraction and fusion on the preprocessed data by utilizing improved cross-scale interaction Patching, wherein the feature extraction and fusion comprise multi-scale feature extraction, cross-scale interaction alignment, residual error correction and dynamic fusion; and performing feature screening on the fused features based on a channel attention mechanism, wherein the feature screening comprises feature response based on improved SE-Block and non-linear interaction of context vectors. Aiming at the non-stationarity of the actual load caused by the influence of meteorological conditions and user behaviors, the model accurately depicts the fluctuation details of the load curve by automatically eliminating the noise interference among multiple variables, and the robustness of the model in the multi-element load prediction of the integrated energy system is reflected.

The invention relates to the technical field of remote sensing data processing and earth surface parameter inversion, and discloses an earth surface matrix parameter hyperspectral data inversion method and system based on machine learning. Comprising the following steps: constructing a multi-source heterogeneous hyperspectral data set; performing feature screening on the preprocessed hyperspectral data set based on an adaptive band selection algorithm, constructing a dynamic weight matrix by calculating mutual information entropy and inter-class distance measurement between spectral bands to realize intelligent screening of key feature bands, and combining spectral derivative conversion and spectral index calculation to generate an enhanced feature vector; and a multi-task transfer learning neural network model is constructed, and an output layer realizes multi-parameter collaborative inversion based on a multi-task learning architecture. And performing preprocessing and feature enhancement operation which is the same as that of the training data on the hyperspectral image data of the to-be-inverted region, inputting the trained neural network model, and outputting a surface matrix parameter inversion result.

The invention discloses a winter wheat yield forecasting method based on a random forest regression model, and relates to the technical field of winter wheat yield prediction.The winter wheat yield forecasting method comprises the steps of multi-source data integration, dynamic feature screening, random forest model optimization and yield prediction and verified.The multi-source data integration provides input data for dynamic feature screening; high-quality features screened by the dynamic features are used as training features of a random forest model optimization module, and a result of random forest model optimization is used as final yield prediction and verification of yield prediction and verification. According to the method, by designing multi-source data fusion and automatic model optimization, the problems that a traditional method is single in data source, difficult to capture complex agronomic relations and insufficient in model generalization ability are solved, high-precision and high-stability winter wheat yield forecasting is achieved, and powerful support is provided for agricultural production decision making.

The invention provides an effective wave height real-time prediction method and device based on dynamic residual correction, and relates to the field of ocean engineering. The method comprises the following specific steps: acquiring wave height data and performing multi-dimensional feature screening; constructing an integrated filter fusing L1 trend filtering and variational mode decomposition, optimizing parameters by using a sea image optimization algorithm, introducing a causal sliding window to extract features so as to construct a time sequence input tensor, and inputting the time sequence input tensor into a stacked bidirectional long-short-term memory network based on an attention mechanism after noise addition standardization so as to obtain a basic predicted value; calculating a manifold coherent structure, PID dynamics and physical statistical characteristics, and cascading with the basic prediction characteristics to construct a comprehensive element characteristic vector; a LightGBM architecture is constructed, and a prediction residual error is fitted after optimization is carried out through a sea image optimization algorithm; and finally, executing linear reconstruction based on the dynamic safety threshold constraint, and outputting a real-time correction result. According to the method, the error evolution rule is deeply mined by using manifold geometric features, and the real-time precision and robustness of significant wave height prediction are remarkably improved.

The invention relates to the technical field of rotating equipment fault diagnosis, in particular to a rotating equipment fault diagnosis method based on time-frequency characteristic decoupling, and the method comprises the steps: S1, obtaining a vibration signal sequence of rotating equipment; s2, respectively extracting time domain features and frequency domain features of the vibration signal sequence, and constructing a time-frequency feature set; s3, performing feature screening on the time-frequency feature set based on a multi-dimensional feature index to obtain fault sensitive features; and S4, inputting the fault sensitive features into the trained fault prediction model, and outputting a corresponding fault prediction value. According to the method, accurate matching of features and fault types is realized through a collaborative mechanism of two-dimensional feature extraction and multi-index screening, and meanwhile, the recognition capability of the model on minority types of faults is improved through multi-scale feature extraction and feature distribution conversion and sampling, so that the accuracy and reliability of fault diagnosis of the rotating equipment are improved.

The invention relates to the technical field of intelligent recruitment management, and discloses an intelligent matching recruitment management system based on a large model. The system comprises a talent database construction module which collects multi-dimensional ability characteristics of job seekers and enterprise post demand data and generates a three-dimensional recruitment data set containing ability, post and time dimensions; the personnel post adaptation field modeling module performs dynamic integrating degree analysis on the data set, constructs a three-dimensional personnel post adaptation field, and identifies adaptation field critical point distribution and a flowing path; the key feature screening module extracts core adaptive features meeting a threshold value; the large model matching engine module inputs the core adaptive features into a pre-training large model generator, introduces adaptive field topology and feature continuity constraints, and outputs optimized dynamic adaptive field distribution; the environment variable compensation module fuses the dynamic indexes of the labor market to generate compensation parameters, and corrects the core adaptation features; and the strategy generation module generates a post matching strategy set and a talent optimization path to adapt to the requirements of the recruitment market.

The invention relates to the technical field of intelligent diagnosis of power equipment, and discloses a switch cabinet partial discharge diagnosis method based on multi-information fusion and dynamic intelligent regulation, which comprises the following steps: firstly, collecting multi-source partial discharge signals and constructing a defect basic database, then carrying out multi-source feature generation, principal component dimension reduction and BPNN key feature screening on the signals, and carrying out multi-source feature extraction on the signals; then, a dynamic BPA value is constructed based on the key features; and then, the defect type is identified by fusing multi-source information through an improved D-S evidence theory, and the defect is positioned in combination with a TDOA and an optimization algorithm. According to the method, the accuracy and reliability of defect type judgment after multi-source information fusion are improved.

The invention discloses a terminal meter reliability dynamic evaluation method, system and device based on multi-source data features and a medium, and relates to the technical field of terminal meter fault prediction.The method comprises the specific steps that multi-source data of a metering terminal are obtained, and the multi-source data are integrated to form a structured feature matrix; and introducing a mixed feature screening mechanism to carry out mixed feature screening to obtain a key feature set of the terminal meter. And carrying out weight distribution by adopting a Bayesian algorithm to obtain a comprehensive weight. And constructing a comprehensive reliability scoring model, and outputting a comprehensive reliability score of the terminal meter. And dynamically setting a risk threshold according to the environmental parameters, and outputting a comprehensive risk level of the terminal meter. According to the method, terminal meter reliability dynamic evaluation under multi-source data fusion is realized, the accuracy of reliability evaluation is improved through dynamic feature screening and an adaptive weight adjustment mechanism, and fault risks and operation and maintenance weak links under different scenes are effectively identified.

The invention provides an Internet of Things smart farm monitoring method and system, and relates to the technical field of agricultural Internet of Things, and the method comprises the steps: executing the triggering type sensing operation of crop growth and environment abnormality at a field end side visual node of an Internet of Things smart farm based on a preset triggering condition, and outputting an end side initial sensing data set; performing non-redundant feature screening on an end-side initial sensing data set, extracting a key feature set, executing a staged slow aggregation operation through a low-power-consumption communication network, outputting batch feature data packets according to a link state, and realizing field light-flow transmission; the cloud processing platform receives the batch feature data packets, then calls the agronomy knowledge set for restoration and diagnosis, outputs a farm monitoring diagnosis result, generates a field equipment linkage instruction according to the farm monitoring diagnosis result, sends the field equipment linkage instruction to the execution equipment, and outputs an equipment linkage response result, so that the farm monitoring efficiency and accuracy are improved, and the farm monitoring accuracy is improved. Intelligent management is realized, and the field operation cost is reduced.

The invention discloses a breast cancer recurrence risk prediction method and system based on multi-modal data missing interpolation and gene interpretability enhancement. The method comprises the following steps: firstly, dynamically generating and complementing features of a missing mode by matching a generative adversarial network with a mode missing mask matrix; then, a feature screening mechanism driven by gene information is introduced, through a multi-task learning network, image feature extraction is supervised by using a gene expression tag in a model training process, and image features highly associated with recurrence-related genes are screened out; and finally, fusing the complemented multi-modal time sequence characteristics by adopting Transform, and outputting a recurrence risk probability. According to the method, the robust prediction performance can be realized under the condition of data missing, and meanwhile, image interpretation with a molecular biology basis is provided for the feature screening process of the model, so that the reliability and clinical acceptability of the whole system are enhanced.

The invention discloses a multi-energy load prediction method based on feature screening and multi-model fusion, and belongs to the field of electric power and comprehensive energy load prediction. The invention provides a three-stage hybrid learning prediction framework. In the first stage, dynamic feature screening is achieved through a recursive feature elimination cross validation method based on expert knowledge constraints, key meteorological elements and multi-energy load time sequence features are reserved, and redundant feature interference is reduced. In the second stage, a multi-task long-short-term memory network is constructed, and coupling relation modeling and collaborative prediction of cold, heat and electricity multi-energy loads are achieved through sharing time sequence characteristic representation and a task exclusive output structure. And in the third stage, a random forest is adopted to carry out nonlinear correction on the residual error of the sub-model, so that the precision and robustness of prediction in sudden disturbance and local non-stationary scenes are improved, the prediction error is effectively reduced, and the stability of multi-energy load prediction is improved. And reliable support is provided for optimized operation, scheduling decision and renewable energy consumption of the park integrated energy system.

The invention discloses a leg health state evaluation and fault tracing system and method based on a time sequence large model, and the method comprises the steps: an input layer is responsible for carrying out the synchronous collection, normalization and time alignment of various sensor signals, and constructing time sequence input data in a unified format; the feature fusion layer adopts a sliding window mechanism to carry out Patch segmentation on an original signal, and local representation is enhanced in combination with feature engineering; a channel attention mechanism is further introduced, the weight of each channel is adaptively adjusted according to the dynamic relevance between the sensors, and information fusion and feature screening are achieved; the model layer constructs a long-term dependence modeling framework based on a time sequence large model and is integrated with an LoRA low-rank adaptation module, and the prediction layer performs uncertainty quantification on a health state prediction result through dynamic confidence interval estimation; and the application layer completes fault tracing and key component positioning according to time sequence attention distribution and channel weight change, and synchronously generates a health trend curve, confidence interval distribution and a visual early warning interface.

The invention discloses a vertical federal learning feature selection method based on context awareness, and belongs to the technical field of artificial intelligence and data privacy protection. According to the method, firstly, an unsupervised sparse network is utilized at a client to initialize the importance of local features so as to accelerate convergence and reduce calculation complexity; and then, obtaining the embedded representation of each client in a pre-training stage, and screening the embedded representation by combining context features through a server side, thereby indirectly identifying key features. In the feature selection stage, the client side performs local feature screening according to the significant embedded index issued by the server, and the influence of irrelevant features on calculation and communication is further reduced. According to the invention, an attention mechanism is introduced to dynamically evaluate contributions of different participants, so that fair weight distribution is realized. According to the method, through staged joint optimization, the communication and calculation cost in the federation training process is effectively reduced, and meanwhile, the prediction precision and interpretability of the model are improved.

The invention discloses a trajectory planning method based on a multi-stage optimization strategy and a mixed diffusion model. The method comprises the following steps: fusing multi-source sensor data and extracting features; screening and fusing intention anchor point tracks; and optimizing the fine-grained trajectory based on the Transform-Mamba mixed diffusion model. According to the method, an intention anchor point track screening mechanism is provided, the anchor point track matched with the scene is selected from the compact vocabulary through the dynamic screening module and fused with the static prior anchor points, the calculation complexity is reduced, the diversity of the initial track and the scene consistency are guaranteed, and the problem that a traditional fixed anchor point set is insufficient in flexibility is solved. According to the method, a mixed diffusion decoder is designed, space environment dependence is efficiently modeled through a cross attention mechanism, a bidirectional Mama module captures time sequence motion dependence with linear complexity, global perception ability and long sequence modeling efficiency are both considered, and safety and dynamics feasibility of generated tracks are ensured.

The invention discloses a pre-eclampsia prediction method and system based on machine learning and early pregnancy indexes. The pre-eclampsia prediction method comprises the following steps: acquiring early pregnancy laboratory index data and FMF model evaluation parameters of a to-be-predicted pregnant woman; based on a preset preeclampsia type, performing feature screening on the early pregnancy laboratory index data by using a Boruta algorithm to construct corresponding original features; inputting the original features into a trained first-level machine learning prediction model corresponding to the pre-eclampsia type to obtain a risk score; according to the risk score and an FMF model evaluation parameter, obtaining a joint feature; and inputting the joint features into a trained second-stage machine learning prediction model corresponding to the pre-eclampsia type to obtain a corresponding pre-eclampsia risk prediction result. According to the method, information of different sources and different physiological dimensions is deeply fused, analyzed and judged, and high-precision and low-cost early prediction of different pre-eclampsia types is realized.

The invention discloses a large language model robustness visual diagnosis method based on multi-dimensional features and adversarial attacks. The method aims to break through a mode that traditional evaluation only depends on a single aggregation index, and a multi-dimensional text feature exploration system covering vocabularies, syntax, semantics and a structural layer is constructed, and a large-scale antagonism disturbance mechanism and a task self-adaptive quantification strategy are combined. And generating structured feature-adversarial instruction-robustness diagnosis data comprising the cue word to be evaluated and the corpus. On the basis, an interactive visual analysis system is constructed, and through bidirectional linkage of a feature statistical view and a semantic projection view, a user is supported to realize progressive exploration from macroscopic feature screening to microscopic semantic attribution under the double view angles of cue words and corpora, so that a root cause causing the fragility of the model is deeply diagnosed. According to the method, the key feature combination influencing the stability of the model can be identified, so that a basis is provided for directional optimization of the model, and the diagnosis depth of robustness evaluation is improved.

The invention provides a photovoltaic power prediction method and system, a computer and a storage medium. The method comprises the following steps: acquiring related data of a photovoltaic power station; non-linear feature screening is carried out, redundant and weak correlation features are removed, a main influence feature set is obtained, and cloud picture semantic features are extracted by using a convolution self-attention encoder; performing multi-scale decomposition and reconstruction to obtain a high-frequency sub-sequence, an intermediate-frequency sub-sequence and a low-frequency sub-sequence; based on the multi-dimensional meteorological variables, dividing weather modes through a clustering algorithm; constructing a physical information neural network model, and obtaining physical enhancement features based on the multi-modal input data set; and constructing a mixed time sequence framework fusing expanded long short-term memory network sparse self-attention and multi-head attention mechanisms, and performing short-term photovoltaic power prediction based on multi-source heterogeneous input. The prediction precision and robustness of the model under complex meteorological and data sparse conditions are significantly improved, and the method is suitable for a high-proportion photovoltaic grid-connected scene.

The invention discloses a Crohn disease focus automatic segmentation and activity evaluation system based on deep learning, which belongs to the field of medical artificial intelligence and comprises a data preprocessing unit, a focus automatic segmentation unit, a radiomics feature extraction unit, a feature screening and dimension reduction unit and an activity classification unit. According to the method, an nnU-Net deep learning segmentation model is combined with image omics feature extraction, multi-stage feature screening and machine learning classification technologies, so that full-process automation from CTE image preprocessing, focus automatic segmentation, feature extraction and screening to activity classification is realized. The system can efficiently and accurately segment the focus of Crohn's disease, automatically assesses the disease activity based on the screened key radiomics characteristics, significantly improves the consistency, objectivity and efficiency of diagnosis, and is suitable for clinical auxiliary diagnosis and scientific research analysis.

The invention provides an industrial internet network intrusion detection method based on a pre-trained large language model, and the method comprises the steps: carrying out the data preprocessing of original network flow data through protocol self-adaptive flow aggregation and session segmentation, feature screening and feature coding; constructing a stream format text data set used for training a generation model GPT-2 and a packet level classification text-label data set used for training a classification model DistilBERT; then fine tuning training is carried out on the generation model GPT-2 and the classification model DistilBERT; then calling a trained generation model GPT-2 to generate a traffic sequence, obtaining a prediction data packet, calling a trained classification model DistilBERT, performing anomaly judgment on sequence data in the prediction data packet one by one, and outputting a classification result of anomaly judgment; the active intrusion detection of first generation and then discrimination is realized. According to the method, prediction can be made before real attack traffic arrives, and network attacks are prevented.

The invention discloses a dynamic feature and physical gating fused wave height prediction system and method, belongs to the technical field of artificial intelligence and machine learning, is used for marine environment monitoring, and comprises a data acquisition and preprocessing module, a feature screening module and a model prediction module. The method comprises the steps of obtaining and preprocessing marine meteorological variable data, constructing a Shapley additive interpretation lightweight gradient elevator regression model to screen a core variable set, constructing a physical guidance gating feature perception Transform prediction model, constructing a mixed loss function constraint prediction model, and outputting a sea wave significant wave height prediction value after prediction model training is completed. According to the method, through a feature screening and dynamic weight distribution mechanism, the utilization efficiency and scene adaptability of multi-source marine meteorological features are remarkably improved, the method can be flexibly expanded to sea wave prediction tasks of different sea areas and spatial-temporal scales, and high-precision and high-reliability technical support is provided for ocean engineering, shipping safety and disaster early warning.

The invention relates to the technical field of fault diagnosis, in particular to an intelligent street lamp fault diagnosis and maintenance system, which comprises a signal collaborative acquisition module, a frequency domain conversion module, a fault identification module, a coupling verification module and a maintenance scheduling module. According to the invention, through a multi-source data synchronous acquisition and time sequence alignment technology, the influence of external environment interference on monitoring data is eliminated, and the stability and accuracy of photoelectric signals are ensured. Stable signal features are extracted through frequency domain conversion and feature screening, accurate fault recognition is achieved based on frequency components and similarity calculation, multi-dimensional verification is conducted in combination with instantaneous power comparison and communication delay detection, the accuracy and reliability of fault judgment are improved, therefore, misjudgment and missed judgment are avoided, maintenance scheduling is optimized, and the fault diagnosis efficiency is improved. The fault response efficiency and the overall stability of the street lamp system are improved, maintenance lag and resource waste caused by inaccurate fault diagnosis are effectively reduced, and the operation reliability and the maintenance response speed of the street lamp are improved.