308 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 multi-mode adaptive fusion automatic driving perception method. The method comprises the following steps: acquiring a public picture data set of a road scene; and inputting the public picture data set into a constructed multi-mode adaptive fusion model to obtain an automatic driving perception result. Through the adaptive selection and fusion strategy, the robustness of the automatic driving perception system under the condition of sensor fault or mode missing is improved, and the perception performance in a complex scene is optimized.

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 provides a method for solving the computing power resource utilization rate based on a genetic algorithm, and relates to the technical field of computing power resource utilization. Comprising the following steps of S1, performing population initialization, randomly generating a group of initial solutions, and representing a computing power resource allocation scheme by each solution; s2, adaptability evaluation: defining a fitness function to calculate the fitness value of each individual; s3, genetic operation: performing selection operation, crossover operation and mutation operation to generate a new offspring; s4, generating a new-generation population, and selecting the most excellent individuals to form the new-generation population; s5, performing iterative computation, iteratively executing the genetic operation and the new generation population generation step, and continuously updating the population until a termination condition is met; and S6, outputting a result, and identifying an optimal solution or a group of excellent solutions from the current population. By simulating natural selection and genetic mechanisms in the biological evolution process, allocation of computing power resources is optimized, so that the resource utilization rate and the system performance are improved.

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 relates to a base station layout method, device and system, equipment and a storage medium. The method comprises the following steps: generating an initial random layout for each base station in a set area; performing adaptability evaluation on each initial random layout by adopting a preset evaluation index to obtain an initial population; performing iterative optimization on the initial population by adopting a genetic algorithm model, gradually optimizing the base station layout until a preset condition is met, and taking the genetic algorithm model at the moment as a trained layout model; and performing layout for each base station in the target area by adopting the trained layout model. The layout scheme is automatically generated for the base station based on various factors by simulating natural selection and genetic mechanisms, so that the problem of unreasonable layout of the base station in the prior art is solved, and the overall performance of a communication network is improved.

The invention relates to an image enhancement method for constructing a spatial vision neural network model. The method comprises the following steps: firstly, carrying out normalization processing on a binarized noise-containing two-dimensional image to enable the binarized noise-containing two-dimensional image to meet small parameter limitation brought by activation of an FHN neuron adiabatic approximation condition; secondly, the space signal is subjected to convolution through an FHN neuron template, and a space neuron pulse potential sequence two-dimensional image coding signal is obtained; constructing an anisotropic spread function based on improved bidirectional adaptive selection as a visual receptive field model with a direction selection mechanism, and carrying out convolution on a neuron pulse coded signal and the constructed receptive field model; and finally, an optimal binary image result is found. According to the method, the direction selectivity and the neuron pulse issuing mechanism of the biological vision system are effectively combined, so that the biological vision system conforms to the physiological characteristics of real vision neuron pulse triggering and signal selectivity, and the ideal image denoising effect is achieved.

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 an unmanned surface vehicle formation path planning method, and relates to the technical field of formation path planning, and the method comprises the steps: carrying out the rasterization processing of a map, adjusting the initial unmanned surface vehicle formation, determining the starting point and terminal point information of a navigator, and determining the position of a dynamic obstacle and the information of a static obstacle. An attenuation coefficient is introduced into a heuristic function of the A-Star algorithm, and path planning is carried out by adopting the improved A-Star algorithm; path smoothing processing is carried out through a Bezier curve of a self-adaptive selection control point; introducing an improved dynamic window method to carry out dynamic local obstacle avoidance; and according to the initial unmanned ship formation pattern and the positions of followers in the formation, combining the global path of the navigator to generate a planned path of the followers, and keeping the followers at the same speed and deflection angle as the navigator. The method ensures the optimality of the path planning of the navigator, also ensures that the follower can adaptively adjust in real time, and effectively improves the efficiency, stability and practicability of the formation path planning.

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 discloses a multi-modal large model fusion training method and system based on a biological evolutionary algorithm, and relates to a method and system for performing fusion and model selection on a multi-modal large model by using a framework of the biological evolutionary algorithm, selecting characteristic parameters and capabilities to form different models, performing fusion training in a manner similar to biological evolution, and obtaining high-performance AI (artificial intelligence). According to the concept, natural selection, genetic variation and other mechanisms in biological evolution are mainly used for reference and applied to the optimization process of an artificial intelligence model, a self-adaptive training system is formed, and the AI performance is continuously improved in continuous iterative training.

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 discloses an apple leaf disease classification method based on a regional perception and lightweight hybrid Transform network. The method comprises the following steps: firstly, inhibiting interference of a low-frequency background and dynamic illumination by using a region extraction and fine-grained convolutional coding module, and adaptively sensing a leaf region and a disease region; secondly, a lightweight Transform block of a parallel cascade structure is designed to extract multi-scale texture and edge features, and adaptive selection of an optimal feature scale, mining of a long-distance dependency relationship and cross-scale information interaction and fusion are realized in a jump connection mode; then, extracting rich texture detail features of the apple leaf region by adopting feature weighting based on adaptive learning, an inverse residual mechanism and the attention degree of a channel attention enhancement network on region feature information; and finally, the generalization ability of the network is improved by jointly adopting a weighted cross entropy loss function and a focus loss function. Experimental results verify the effectiveness of the method.

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 relates to the technical field of AGV path planning in an intelligent manufacturing workshop, in particular to a multi-AGV path planning method with collision detection in a matrix workshop. Comprising the steps of initializing algorithm parameters; using a hybrid initialization strategy to initialize the population; in the hive bee employment stage, for each individual in the population, a vehicle merging strategy is adopted, and a local search operator is selected based on a self-adaptive selection strategy to carry out individual evolution; in the bee observation stage, enhanced evolution is carried out on individuals with good ranking, vehicle position information is updated, collision detection is carried out on the running process of the vehicle, and a vehicle running path is re-planned based on a collision avoidance strategy for possible collisions; in the reconnaissance bee stage, for the individuals failed in continuous evolution, a ranking-based restart strategy is used, and the global exploration ability is enhanced; and judging whether a termination condition is met, and finally outputting an optimal solution. On the premise of an actual production application scene, the production safety is improved, and the AGV operation cost is reduced.

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 invention provides a method, a device and equipment for distributing workers in a multi-stage hybrid flow shop. The method comprises the following steps: constructing a scheduling optimization model; initializing a population and an external population of the scheduling optimization model, and encoding individuals in the population and the external population through multiple layers of chromosomes; respectively carrying out crossover operation based on worker skill types and adaptive mutation operation based on various mutation operators to obtain a combined offspring set; performing local search on the combined offspring set through a local search strategy based on a mutation operator to obtain a target offspring set; performing individual selection according to an adaptive selection strategy based on the number of iterations, determining and updating a population and an external population of the next iteration, and adding one to the number of iterations; if the number of iterations is smaller than the preset number of iterations, returning to execute the step of obtaining the combined offspring set; and if the number of iterations is equal to the preset number of iterations, determining the Pareto frontier solution of the population as the output of the scheduling optimization model. And the worker allocation rationality is ensured.

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.