265 results about "Adaptive selection" patented technology

The invention belongs to the technical field of computer data processing, and provides a biodiversity inversion method based on multi-source remote sensing image fusion. Comprising the steps of remote sensing image data acquisition, image preprocessing, image fusion processing, multispectral resolution image data generation, spectral feature extraction, final frequency feature extraction, feature integration and target ecological variable prediction. According to the invention, through wave basis adaptive selection and multi-scale wavelet decomposition, spectrum fidelity and space structure maintenance are considered, differential fusion of high and low frequency components under different scales is realized, and multi-source data complementarity and fusion image quality are improved; through spectral resolution refinement processing and multi-bandwidth scale simulation, the limitation of single resolution is broken through, and the capability of capturing complex spectral features of vegetation is enhanced; the spatial correlation is enhanced through spatial neighborhood feature fusion; and through an ecological variable inversion estimation model, multi-index synchronous prediction is realized, and the universality of the model is improved.

The invention discloses a resource adjustment method and system based on power grid frequency deviation, and relates to the technical field of AGC control, and the method comprises the following steps: obtaining environment state data, and constructing an environment state vector; initializing a control agent according to a preset control mode, taking the environment state vector as input, training the control agent through a reinforcement learning reward function, and outputting a strategy action; dynamically switching the strategy action into a control strategy of the AGC controller based on a soft switching and strategy interpolation mechanism; a model prediction control method is adopted, and the power set value of the AGC control unit is optimized in a rolling mode; according to the method, the trained control agent is integrated into the AGC controller through reinforcement learning, so that the AGC controller can dynamically switch the control strategy of the AGC controller, self-adaptive selection of control actions is realized, and the power grid frequency is controlled to be stable by correcting the set value of the unit in the AGC controller and improving the control precision.

The invention discloses a visual large model Token adaptive optimization method, system and device based on differential evolution and a medium, and the method comprises the steps: carrying out the data processing of an image classification data set, an instance segmentation data set and a saliency target detection data set, and obtaining all Tokens corresponding to each image through a Patch Embedding and position coding method; obtaining a plurality of groups of Tokens corresponding to each image through a random selection mode, and performing data processing to output all Tokens corresponding to each image and the plurality of groups of Tokens selected from each image; constructing a Token adaptive selection module, a self-attention optimization module and a downstream task output module; a complete Token adaptive optimization visual large model is constructed; training a reconstruction model and a complete Token self-adaptive optimized visual large model; performing model reasoning to obtain an image classification result, an instance segmentation result image and a saliency target detection result image; the system, the equipment and the medium are used for implementing the method. The method can be widely applied to various visual tasks such as image classification, instance segmentation and saliency target detection.

The invention relates to the technical field of artificial intelligence, in particular to an injection molding process parameter optimization method and system based on hybrid algorithm and model fusion, and the method comprises the steps: optimizing a parameter combination of a support vector regression model through a simulated annealing algorithm, building a weighted fusion model based on the optimized support vector regression model and a random forest, and optimizing the model; constructing a hybrid model of an adaptive selection weighted fusion model and an optimized support vector regression model; constructing a three-objective optimization model including buckling deformation, volume shrinkage and production energy consumption, and searching a Pareto optimal solution set in a process parameter space by adopting a multi-objective genetic algorithm by taking the hybrid model as a target value evaluation tool; carrying out local correction on the key process parameters by adopting a gradient descent method until the deviation falls back to be within a preset threshold value, and obtaining optimized process parameters; the defect rate of products can be reduced, and meanwhile production energy consumption is reduced.

The invention discloses a multi-satellite task allocation strategy adaptive selection method based on deep reinforcement learning, and aims to solve the problems that the satellite task scheduling problem is difficult to solve due to the fact that the satellite attitude maneuver ability is improved, and the task number and the satellite scale are explosively increased, and the solving limitation of a traditional method is solved. According to the method, the problem is divided into an upper-layer multi-satellite task allocation part and a lower-layer single-satellite scheduling part, iterative optimization of a scheme is realized through multiple interactions of the two parts, a self-adaptive search method based on reinforcement learning is provided for the upper-layer task allocation problem, and task allocation strategy self-adaptive selection based on deep reinforcement learning is realized.

The invention provides a data management method and system based on optomagnetic fusion storage, and belongs to the field of data management.The method comprises the steps that target data are obtained, data feature information is collected, and data popularity analysis is carried out; a basic storage scheme is determined according to data popularity, and intelligent decision-making of optical storage, magnetic storage or fusion storage is realized through data criticality analysis and popularity critical coefficient evaluation; predicting a storage loss parameter according to the data popularity and the storage scheme, and optimizing an error correction code or erasure code parameter in combination with the data criticality; and finally, implementing data management according to the determined storage scheme and optimization parameters. The technical problems that a traditional storage system cannot intelligently distribute storage media according to data characteristics, and access efficiency and storage safety are difficult to balance are solved, self-adaptive selection of a storage scheme and optimization of storage quality are achieved, and efficiency and reliability of data management are improved.

The invention discloses an Internet of Vehicles CAN bus intrusion detection method based on noise perception active learning, and belongs to the technical field of Internet of Vehicles safety and machine learning. The invention aims to solve the technical problems of false label noise interference, high manual labeling cost, high attack missing report rate caused by class imbalance and the like. The core of the method is to execute a noise sensing mixed query strategy in an iterative loop: firstly, generating a pseudo tag through clustering and correcting by using an integrated noise detector; secondly, calculating uncertainty scores and noise probabilities of the samples, fusing the uncertainty scores and the noise probabilities to obtain a comprehensive score, and preferentially selecting the samples with high uncertainty and low noise probabilities; and then adaptively selecting a sampling strategy according to the model performance and applying category balance constraint. The query batch is used to iteratively update the model while dynamically adjusting the classification threshold to reduce the missing report rate. According to the method, the influence of pseudo label noise can be effectively suppressed, and the attack detection precision and generalization capability are remarkably improved with extremely low labeling cost.

The invention discloses a security guarding method and system based on a communication-guide-remote fusion technology, and the method achieves the self-adaptive selection and redundant transmission of communication links in a weak network, shielding and other environments through the construction of a multi-link communication system, an indoor and outdoor integrated high-precision positioning mechanism and a multi-mode intelligent sensing cooperation mechanism. Cross-scene continuous positioning is realized by combining multi-source information such as GNSS, inertial navigation, geomagnetism and remote sensing image backbone maps, and inertial navigation, video, sound and environment sensing data are fused to generate an abnormal behavior recognition result, so that the whole-process and whole-scene safety guarding capability for all personnel is formed, and the accuracy of abnormal behavior recognition is improved. The problems of easy communication interruption, easy positioning misalignment and unreliable abnormity identification in the prior art are effectively solved, and the method is suitable for a plurality of application scenes such as crowd safety guarding and the like.

The invention discloses a sparse regularization direction of arrival estimation method based on a risk minimization principle, and belongs to the technical field of array signal processing and underwater acoustic signal processing. The method comprises the steps of receiving array signals and establishing an observation model; constructing a sparse representation and over-complete dictionary; establishing and initializing a regularization optimization model; carrying out adaptive weight updating and risk-driven parameter selection; after regularization parameters are determined, a fast iterative shrinkage threshold algorithm FISTA is adopted to carry out optimization solution, and dictionary refinement is carried out on the detected direction after each iteration convergence so as to reduce off-grid errors; and after a small amount of outer layer iteration is repeated, outputting a final DOA estimation result and corresponding power. According to the method, self-adaptive selection of regularization parameters and noise levels can be realized, and the problem of precision degradation under complex conditions of low signal-to-noise ratio, limited snapshot number, signal source correlation, power imbalance and the like is effectively solved without manual parameter adjustment, so that the robustness and practicability of estimation are remarkably improved.

The invention discloses a self-adaptive text extraction method and system based on artificial intelligence, and the method comprises the steps: carrying out the analysis of the document structure entropy of an example document set, quantifying the noise density, geometric distortion degree and background complexity of the example document set, and carrying out the self-adaptive selection of a preprocessing assembly line intensity grade according to the above; dynamically configuring image preprocessing parameters and AI recognition model parameters, and generating a recognition engine instance to output a preliminary recognition text; after regularized coarse screening extraction is carried out based on key field description, a multi-candidate generation strategy is started for low-confidence-coefficient candidate text fragments, a multi-person cooperative verification process is triggered for lower-confidence-coefficient fragments, finally all the fragments are processed through a text standardization module, and structured text extraction information is output. According to the method, accurate adaptation of processing intensity is achieved through document quality quantitative evaluation, the extraction accuracy and system robustness of complex heterogeneous documents are effectively improved through a multi-level confidence coefficient verification mechanism, and the identification error risk caused by image quality fluctuation or rule solidification is reduced.

The invention provides a Chinese financial multi-task large model based on adaptive semantic space learning, and the method specifically comprises the following steps: S1, carrying out the adaptive selection of LoRA experts and data thereof, and obtaining the data of the LoRA experts; s2, collecting Chinese financial fine tuning data from the financial field; and S3, constructing a multi-task data set based on the LoRA expert data and the Chinese finance fine tuning data, and training a Chinese finance multi-task large language model.

The invention relates to a self-adaptive selection restoration method for defocus blurred image restoration, and belongs to the technical field of image restoration. The method comprises the following steps of: establishing a multi-scale branch which consists of three coding blocks and decoding blocks and is used for processing images with different resolutions; in each branch, shallow layer features of an input image with the corresponding resolution are extracted through a convolutional layer, and then image reconstruction is carried out from the shallow layer features by self-adaptive selection modules in corresponding coding blocks and decoding blocks; wherein each self-adaptive selection module comprises a self-adaptive double-branch fractional order module and a double-path fusion strategy based on gating reweighting, so that image reconstruction and feature fusion among different branches are carried out respectively, and finally a reconstructed image is output. Compared with the prior art, the method provided by the invention can realize a more efficient restoration effect on the compressed blurred image with lower model parameter quantity and calculation complexity.

The invention relates to the technical field of fault diagnosis, and particularly discloses an electric spindle fault diagnosis method and system based on multi-scale dynamic convolution, and the method comprises the steps: collecting a multi-channel vibration signal of an electric spindle through a vibration sensor, carrying out the preprocessing, and segmenting the multi-channel vibration signal into equal-length samples through a sliding time window; constructing a multi-scale dynamic convolution module, extracting local and global features of an input sample through a parallel multi-branch structure, and dynamically aggregating convolution kernel weights of different scales based on an attention mechanism; the extracted local and global features are input into an AMSoftmax module, and decision boundaries between fault categories are increased through angle interval constraint; and training a diagnosis model by adopting a combined loss function, adjusting hyper-parameters in combination with Bayesian optimization, and outputting a composite fault classification result. Local and global features contained in a vibration signal are captured through parallel convolution kernels of different scales, an expert mechanism is introduced to realize adaptive selection of convolution kernel parameters, and the adaptability of a model to composite fault mode changes is enhanced.

The invention discloses a self-adaptive multi-feature fusion hybrid retrieval sorting method and system, and belongs to the technical field of information retrieval. The method comprises the steps that after user query is received, a mixed retrieval process and an intention recognition process are executed in parallel; performing multi-dimensional feature extraction on the candidate documents obtained by the mixed retrieval, wherein the multi-dimensional feature extraction comprises semantic correlation features, keyword matching features, document authority features and timeliness features; and according to the identified query type, adaptively selecting a fusion weight, and carrying out weighted fusion on the multi-dimensional feature vector to calculate a final score and sort the final score. According to the method, the problem that weight distribution is rigid in traditional mixed retrieval is solved through an intention self-adaptive dynamic weight mechanism, meanwhile, by introducing multi-dimensional service features, the ranking result not only ensures the correlation, but also meets the quality requirement under a service scene, and the accuracy and practicability of a retrieval system are remarkably improved.

The invention relates to an artificial intelligence technology, in particular to a task adaptive parameter adjusting method and system for a weather and climate basic model. The adjusting method comprises the steps that a weather and climate basic model is initialized, the weather and climate basic model comprises an encoder, a main body network and a decoder, and the main body network comprises a parameter efficient fine adjustment framework model provided with a task self-adaptive dynamic prompt module and a random snow-consuming guided self-adaptive selection module which work cooperatively; performing task self-adaptive dynamic prompt through a task self-adaptive dynamic prompt module, generating soft prompt lexical elements, and fusing the input weather data with the soft prompt lexical elements to obtain an enhanced lexical element sequence; and through a random snow-consuming guided adaptive selection module, random snow-consuming guided adaptive selection is carried out, and parameter fine tuning of the weather and climate basic model is realized. Model parameters can be dynamically, selectively and finely adjusted according to downstream tasks, a very small number of trainable parameters are used, and the calculation and storage cost is remarkably reduced.

The invention provides an arrhythmia classification method based on multi-scale space-time learning and an adaptive selection neural network. Aiming at the problems that electrocardiosignals have different complex wave band information of various scales, pathological changes have the characteristics of transience and intermittency in time sequence, and different channels in space contain different pathological change information, a multi-scale thought is introduced in the invention to extract the complex wave band information; a multi-scale sparse time sequence attention module is designed to capture local tight and remote sparse time sequence feature information, a multi-scale residual convolution module is designed to interact and integrate spatial feature information in different channels, and an adaptive selection module is designed to dynamically select different scales and time steps. In some public data sets, the method has better classification performance and higher diagnosis speed.

The invention relates to the technical field of computer vision and target detection, and discloses a spatial faint target detection method based on semantic large model assisted reasoning, which comprises the following steps: preprocessing an image to be detected; by improving the primary detection of a YOLOv10 double-detection-head frame, the frame supports a user to controllably or adaptively select a detection mode; extracting a semantic generation prompt from the initial result, and inputting a large language model to obtain a potential omission category; key categories are extracted and mapped into IDs, and secondary directional detection is carried out; and fusing the two results and outputting. According to the method, the YOLO and the large model are combined to propose a YOLO-large model detection framework, compared with a single YOLO algorithm, the method can better complete detection of space dark weak tiny targets or low-pixel targets, meanwhile, the problems of missing detection and error detection in target detection are effectively solved, the defects of an existing YOLOv10 technology are overcome, and the optimal balance of precision and speed is achieved.

The invention discloses a power grid tower cross-domain adaptive type selection method, system and device and a medium. The method comprises the steps that tower data and multi-domain environment data are collected; inputting the tower data and the multi-region environment data into a domain adversarial neural network, and constructing a tower-region calculation model; based on the multi-region environment data, obtaining environment parameters of a target region through an LSTM prediction model, and obtaining tower parameters through a multi-objective evolutionary algorithm; and according to the environmental parameters and the tower parameters, generating a three-dimensional tower model, and performing safety analysis on the three-dimensional tower model to generate an optimal cross-domain adaptive type selection scheme. According to the method, multi-region data cross-domain sharing and privacy protection are realized through federated learning and homomorphic encryption, the domain adversarial neural network, LSTM prediction and a multi-objective evolutionary algorithm are combined, and a BIM modeling and knowledge graph reinforcement scheme is matched, so that the cross-region type selection suitability and precision of the power grid tower are improved, the type selection is safe and efficient, and the method is suitable for large-scale popularization and application. The problems that traditional model selection data are limited, and dynamic optimization is weak are solved.

The present invention relates to the technical field of remote sensing target detection, and in particular to a method for directed target detection of remote sensing images based on dual-domain feature fusion. The method includes obtaining a remote sensing image data set to get a training set; constructing a remote sensing target detection network, which includes a feature extraction branch, a dual-domain feature fusion branch for fusing the spatial domain features and frequency domain features of the remote sensing image, and a target detection branch for judging the category and position corresponding to the remote sensing image according to different fusion features; using the training set to train the remote sensing target detection network to obtain a remote sensing target detection model; inputting the remote sensing image to be detected into the remote sensing target detection model, and outputting the corresponding detection category. The present invention fully considers the roles of the spatial domain and the frequency domain in remote sensing classification. Through the adaptive selection of the spatial domain and the frequency domain and the feature interaction and fusion between the two domains, it effectively enhances the fusion of global context information and local information, and makes up for the lack of target information.

The invention discloses a small target detection system in a complex construction scene, which belongs to the technical field of target detection and is characterized in that a standardized image is acquired through an image acquisition and preprocessing module, and a target is detected by using an improved YOLO11 model. The improved YOLO11 model divides feature information into high and low frequency features through frequency domain decomposition and performs enhancement processing on the high and low frequency features; the edge and texture of the high frequency features are enhanced through expansion convolution and a channel attention mechanism, and semantic perception of the low frequency features is improved through multi-scale convolution and an adaptive gating mechanism. And the enhanced features are fused through an SE attention mechanism, so that the small target detection capability is improved. The system is also provided with a scale adaptive selection module which can dynamically adjust a detection strategy according to a target size to meet different detection requirements. According to the method, the problems of high false alarm and missing detection of small target detection in a complex construction scene are effectively solved, the detection precision and robustness are improved, and the method has important practical application value.

The invention discloses a modular sensor adaptive integration and data processing method and system in a complex terrain environment, and the method comprises the steps: constructing an extensible sensor sensing layer through a standardized interface module, and achieving the plug and play of various environment monitoring sensors; performing an inspection task in a complex terrain area based on a mobile device, and performing fusion processing on real-time data acquired by multiple sensors through an edge calculation layer; according to the environment data characteristics, the terrain complexity and the running state of the mobile device, the inspection behavior strategy of the mobile device is dynamically adjusted, and follow-up data monitoring and processing are carried out; the processed real-time data is uploaded to a cloud collaboration layer for collaborative analysis in a self-adaptive selection communication mode, and fundamental conversion from a static and fixed monitoring mode to a dynamic and adaptive mode is realized through modular sensor integration, mobile inspection, edge intelligent processing and self-adaptive communication; and the coverage rate, the efficiency and the intelligent level of environment monitoring are remarkably improved.

The invention belongs to the field of non-contact vital sign detection, and particularly relates to a millimeter wave radar arrhythmia detection method based on particle swarm optimization variational mode decomposition and deep learning, and the method specifically comprises the steps: S1, obtaining a human chest micro-motion signal through a millimeter wave radar, performing static clutter filtering, phase extraction, unwrapping and detrending processing on the radar echo signal to obtain a chest displacement signal containing heartbeat and breathing information; s2, aiming at the thoracic cavity displacement signal, constructing a variational mode decomposition model, and carrying out adaptive optimization on a mode number and a penalty factor through a particle swarm optimization algorithm to obtain an optimal decomposition parameter and complete signal decomposition; s3, according to the center frequency and the energy distribution characteristics of each modal component, screening the modal components in the heartbeat frequency range and reconstructing the modal components to obtain heartbeat characteristic signals representing heart mechanical activities; s4, carrying out time sequence segmentation on the heartbeat characteristic signals, extracting local heart beat morphological characteristics by utilizing a convolutional neural network, and carrying out modeling on a long-time rhythm dependency relationship of the heartbeat signals in combination with a time sequence modeling network based on an attention mechanism to obtain heart rhythm depth characteristic representation; and S5, inputting the heart rhythm depth features into a heart rhythm discrimination model, analyzing the heart rhythm state of the detected person, and outputting an arrhythmia detection result. According to the method, adaptive selection of variational mode decomposition parameters is realized by introducing a particle swarm optimization mechanism, heartbeat signals and respiration and motion interference components are effectively separated, modeling is carried out on rhythm characteristics in combination with a deep learning model, and non-contact detection of arrhythmia is realized. The method does not need to wear an electrode or contact a human body, has the advantages of strong anti-interference capability, good adaptability and high detection precision, and has a good application prospect in the fields of heart rhythm health monitoring, disease screening and the like.

The invention discloses a sleep emotion state recognition-oriented multi-mode sensing signal analysis method and system, and relates to the technical field of sleep emotion data processing. According to the scheme, a progressive data processing method is constructed, and emotion-sleep state recognition and self-adaptive intelligent feedback regulation and control are achieved. The core process comprises the steps of ensuring the time sequence consistency of original data through multi-mode signal transmission time sequence interference analysis, adaptive selection of fixed clock calibration alignment and multi-mode signal dynamic resynchronization, then extracting and fusing deep features of sleep emotion multi-mode signals by using a deep network, and further completing emotion-sleep state recognition. And finally, sleep emotion collaborative intelligent regulation and control are driven according to a sleep-emotion state recognition result, and a physiological safety monitoring mechanism is introduced, so that the reliability and safety of the regulation and control process are ensured, and the effect of improving the emotion-sleep state recognition and intelligent feedback regulation and control accuracy is achieved.

The invention discloses a non-intrusive power user identification method based on self-supervised comparative learning, and relates to the technical field of non-intrusive power monitoring and user identification, and the method comprises the steps: input data are electricity utilization power time sequence data recorded by equipment such as a household intelligent electric meter; inputting the data into a double-layer contrast learning framework based on clustering guidance, and completing power user identification in combination with an adaptive time-frequency feature enhancement mechanism; the method comprises the following steps: constructing a double-layer contrast learning module, introducing a CEEMDAN algorithm to decompose IMF components of power consumption data of different dates, mapping distribution characteristics of the IMF components into selection probabilities of convolution kernels with different widths by a training network, completing adaptive selection of the convolution kernels by means of a Gumbel-softmax mechanism, removing accidental fluctuation of data through convolution in a time domain, and obtaining a time domain; and finally, fusing features of different dates through an attention module, mapping the features into probability distribution through a full connection layer, and classifying the probability distribution, thereby finally realizing accurate identification of non-intrusive power consumers.

The invention discloses a finite element identity recognition system based on voiceprint comparison. The finite element identity recognition system comprises a voiceprint extraction module, a sound channel finite element modal analysis module, a front balance processing module, a double-unit construction module, a pseudo measurement constraint module, an excitation unit parameter adaptive selection module, a representation unit correction module and an identity recognition module. The invention belongs to the field of voice identity recognition, and particularly relates to a finite element identity recognition system based on voiceprint comparison. According to the scheme, real glottis excitation and playing simulation signals are distinguished through finite element combination modality and double-unit joint estimation, and the detection capacity of anti-recording and anti-synthesis attacks is enhanced; pseudo measurement constraints are introduced, redundant excitation components are eliminated, and the distinguishing degree of different speakers is improved; through the correction of the characterization unit, the real-time self-adaptive capability is provided for the environment sudden change aiming at the non-stable speaking and environment, dynamic scaling measurement noise and process noise, the performance fluctuation caused by assumed mismatch is reduced, and the identity recognition effect is further improved.

The invention provides a path planning method, system and equipment for underwater rock drilling operation of a high-frequency breaking hammer, and relates to the technical field of underwater rock drilling. A multi-source data modeling technology and a position indicator area positioning technology are fused, microscopic details of a rock drilling target are complemented through optical data, equipment motion constraints are calibrated through position indicator data, and three-dimensional environment modeling of a macroscopic operation area in combination with the microscopic rock drilling target and the equipment motion constraints is achieved; a multi-objective optimization model is constructed, and an objective function for aggregating the path length, the path fluctuation, the energy consumption and the breaking hammer slip rate is a composite cost function; solving a global optimal operation path corresponding to the composite cost function by adopting IPSO; obtaining the global optimal path through speed updating mechanism optimization, adaptive inertia weight adjustment, asynchronous change learning factor setting and natural selection population iteration; the data volume and the calculation complexity of environment modeling are reduced, and the accuracy and the practicability of environment perception and modeling are improved.

The invention discloses a laser radar-vision tight coupling positioning method and system based on adaptive uncertainty modeling, and belongs to the technical field of automatic driving or robots. In order to solve the problem that the performance of an existing method is reduced in a dynamic and degraded scene, the method is realized through the following steps: quantitatively evaluating the data quality of visual and laser radars in real time; generating a covariance matrix for each observation dynamic based on the quality index, and adaptively selecting a robust kernel function; and finally, solving an optimal pose in a weighted robust factor graph optimization framework, and outputting a quantized credibility score. According to the invention, through intelligent perception and utilization of observation information, the precision, robustness and security of the positioning method in a complex environment can be significantly improved.

The invention belongs to pattern recognition and artificial intelligence, and particularly provides a multi-modal large language model optimization method and system based on adaptive resolution, and the method comprises the steps: dividing multi-modal question and answer data into a target task training set and a target task test set; constructing a resolution selection training set by using the recollected image data in the target scene and the paired text instruction; constructing a multi-modal large language model with a two-stage image coding mechanism, and training the multi-modal large language model by using the target task training set and the resolution selection training set to obtain a target model; the prediction accuracy of the target model is evaluated on the target task test set, and optimization of the multi-modal large language model is completed. According to the technical scheme, the optimal resolution is adaptively selected for reasoning through a two-stage image coding mechanism, so that the visual perception capability of the model is remarkably improved.

The invention relates to the technical field of data prediction, in particular to a flight guarantee time prediction method and device, a medium and equipment, by constructing a multi-dimensional initial feature set and fusing a channel attention mechanism, and by capturing a long-time sequence dependency relationship, the capturing capability of a preorder guarantee node delay conduction effect is remarkably improved; through dual-module adaptive selection based on node attributes, prediction demands of different types of guarantee nodes are accurately matched, and the overall precision of whole-process node prediction is improved; the prediction value of the ith guarantee node and the time sequence context feature are spliced to form the dynamic enhancement feature of the subsequent guarantee node, so that the transmission and fusion of the preorder prediction information to the subsequent node are realized, and the prediction of the subsequent guarantee node can adapt to the running state change of the preorder guarantee node in real time; the subsequent prediction deviation caused by the delay of the preorder guarantee node is effectively reduced, and the dynamic adaptability and timeliness of the prediction result are improved.

The invention provides an efficient single-stage super-resolution method and a remote sensing image super-resolution system for RGB remote sensing images, and the method integrates Fourier position coding guided geometric node modeling, hypergraph high-order relation modeling and a Mama long-range dependence modeling mechanism based on a state space model. And consistent modeling of a multi-scale structure is realized while the linear calculation complexity is kept. In the method, FOPE is introduced as explicit geometric priori to guide adaptive screening of key nodes on global, regional and local scales; then, an FOPE enhanced hypergraph network is constructed, and high-order space association among multiple nodes is modeled through hyperedge; according to the method, a two-stage Mama aggregation module is further designed, and intra-node feature consistency modeling and inter-node long-range spatial dependency modeling are completed with linear complexity; and finally, a high-resolution RGB remote sensing image is generated through a lightweight direct up-sampling module.