430 results about "Hyper parameters" patented technology

The invention discloses a life prediction method based on health index construction and neural network fusion, and belongs to the technical field of equipment state monitoring and predictive maintenance. According to the method, through multi-source degradation feature extraction, common dynamic principal component analysis (CDPCA) dimensionality reduction, health index construction and normalization, deep learning multi-model modeling, integrated learning fusion and Bayesian optimization hyper-parameter optimization, online health assessment and residual life prediction of the equipment part degradation process are realized. Specifically, the method comprises the following steps: firstly, extracting time domain, frequency domain and time-frequency domain features from a sensor acquisition signal, and performing dimension reduction through CDPCA to obtain effective degradation characterization; then, weighting the main features to construct a health index (HI) curve, optimizing the weight through a genetic algorithm, and then performing normalization; a plurality of neural network models such as CNN, Bi-GRU, Bi-RNN, Bi-LSTM and SRNN are constructed based on the normalized HI sequence, and degradation trend modeling is realized; inputting the output results of the neural networks into an integrated learning module for fusion optimization; and finally, carrying out automatic optimization on the key hyper-parameters of the model by utilizing Bayesian optimization. In the equipment operation process, a normalized HI curve can be calculated in real time and input into the fusion model, and the residual life estimation value of the part is dynamically output. According to the method, high-precision, high-robustness and online life prediction can be provided under complex working conditions, the safety and reliability of equipment operation and maintenance are improved, and the method has wide engineering application value.

The invention provides a tunnel excavation ground surface settlement prediction method and system based on a machine learning hybrid model, and relates to the technical field of tunnel engineering and machine learning crossing, and the method comprises the steps: obtaining the multi-source heterogeneous information of a target tunnel, and constructing a ground surface settlement data set; a Transform-BiLSTM hybrid model is constructed, the robustness of the algorithm in a noise environment is enhanced based on a VMD (variational mode decomposition) algorithm, hyper-parameters are adaptively adjusted and optimized by using a PSO (particle swarm optimization) algorithm based on a ground surface settlement data set, the model prediction precision is maximized, and a ground surface settlement prediction model is obtained; and analyzing decision logic of the ground surface settlement prediction model through the SHAP value, and outputting interpretable engineering guidance suggestions. By constructing a machine learning hybrid model, high-precision and real-time prediction of ground surface settlement in the whole process of tunnel excavation is realized. The precision and generalization ability of the model are improved, the characterization ability of complex spatial-temporal characteristics is enhanced, and overfitting is avoided; and the interpretability is optimized, and the influence of key parameters on a prediction result is quantified, so that construction parameter adjustment is guided.

The invention discloses a tight reservoir three-dimensional crustal stress field modeling method based on an improved neural network. The tight reservoir three-dimensional crustal stress field modeling method comprises the steps that S1, a unified-format multi-source geological physical data tensor set is constructed; s2, constructing a frequency domain hierarchical enhancement-SIREN implicit neural network structure based on the unified format multi-source geological physical data tensor set; s3, inputting the candidate hyper-parameter configuration into the frequency domain hierarchical enhancement-SIREN implicit neural network to complete one-time model training; s4, aiming at each candidate hyper-parameter configuration, initializing an inner-layer population of the black widow optimization algorithm, completing second model training, and obtaining an optimal model parameter of the frequency domain hierarchical enhancement-SIREN implicit neural network; s5, tight reservoir fracturing parameter optimization and real-time safety window adjustment are achieved. According to the method, the continuous stress field can be quickly generated at the resolution of 1 m, real-time well section updating and fracturing scheme optimization are supported, and the fracturing transformation effect, the fracturing safety margin and the reliability of economic productivity prediction are remarkably improved in practical application.

The invention provides a sequential network traffic prediction method and system based on swarm intelligence parameter optimization, and relates to the technical field of network traffic prediction. The method comprises the following steps: acquiring indexes such as throughput packet loss rate and round-trip delay of a target link by using a network probe, and performing deletion filling normalization and multi-scale decomposition to obtain a standardized traffic sequence; calculating information entropy, constructing a traffic complexity feature vector, and dividing a training set and a verification set; constructing a hybrid depth prediction model composed of a one-dimensional convolutional network and a gating cycle unit, and establishing a hyper-parameter search space; using particle swarm optimization and entropy-driven inertia weight adjustment and mutation probability mapping to reconstruct a speed and position updating strategy, and iteratively outputting a global optimal hyper-parameter; and generating a benchmark prediction result according to full-amount training, extracting a residual error, training a nonlinear residual error compensation model to carry out superposition correction and reverse normalization, obtaining a final flow prediction result, and improving prediction precision and generalization ability.

The invention relates to the crossing field of hydraulic engineering hydrological monitoring technology and machine learning prediction technology, discloses a sediment concentration prediction method based on deep learning, and aims to solve the problems that in existing sediment concentration prediction, hyper-parameter manual tuning is low in efficiency, key feature attention is insufficient, local and time sequence information is difficult to consider by a single model and the like. Accurate prediction is realized through five core modules: a data preprocessing module performs missing value filling, abnormal value processing and derivative feature generation on hydrological data; the feature selection module screens key features based on mutual information; the time sequence construction module generates time sequence data through a sliding window; the hyper-parameter automatic optimization module adopts Bayesian optimization iteration to obtain an optimal hyper-parameter; the CNN-LSTM-attention prediction module fuses CNN local feature extraction, bidirectional LSTM time sequence dependence capture and multi-head self-attention mechanism key feature focusing capability, is suitable for scenes such as river channels and channels, and provides efficient decision support for hydrological regulation and control.

The invention relates to the technical field of tunnel engineering and intelligent construction, in particular to a shield tunneling real-time control method based on a random forest and a particle swarm optimization algorithm. The method comprises the steps that initial tunneling parameters are generated through a parameter recommendation random forest model according to geology and tunnel geometric parameters, and model hyper-parameters are optimized through a sparrow optimization algorithm; carrying out settlement prediction by utilizing the settlement prediction random forest model; if the predicted value exceeds the limit, carrying out iterative optimization by adopting a particle swarm optimization algorithm and taking the initial parameter as a starting point, and searching a global optimal tunneling parameter combination meeting the settlement requirement; finally, the optimized parameters are issued to the shield tunneling machine to be executed, the model is continuously updated based on real-time construction data, and closed-loop control is formed. According to the method, intelligent recommendation and real-time optimization of tunneling parameters can be realized, the ground surface settlement control precision and the system response speed are improved, the dependence on artificial experience is effectively reduced, and the self-adaptive capability and the intelligent level of shield construction under complex geological conditions are enhanced.

The invention relates to the technical field of soil detection and multi-source information fusion, in particular to a soil moisture content cooperative detection method and system.The method comprises the steps that a target area is determined, and a target thermal infrared image of surface soil of the target area is obtained; extracting target characteristic parameters related to the moisture content from the target thermal infrared image, inputting the target characteristic parameters into the trained BP neural network, and predicting to obtain a surface soil moisture content distribution diagram; based on the surface soil moisture content distribution diagram, determining a target range region with abnormal moisture content through threshold comparison; after the air coupling stepping radar is driven to be aligned with the thermal infrared imaging system in a space-time mode, scanning is conducted in a target range area, and a radar reflection coefficient extracted from an obtained radar image and phase difference information serve as radar characteristic parameters; and performing modeling analysis on the radar characteristic parameters based on a support vector regression (SVR) model, and obtaining soil profile moisture content distribution of the moisture content abnormal region by constructing a nonlinear mapping relation and optimizing model hyper-parameter inversion.

The invention relates to the field of data processing, and provides a cross-border e-commerce intelligent product selection system and method based on multi-platform data fusion, and the method comprises the steps: carrying out the data collection and preprocessing of a plurality of cross-border e-commerce platforms, constructing a neural network model through optimal hyper-parameters, training, and generating a selected product recommendation list. Meanwhile, the structural parameters and the search direction of the selected product model are continuously updated, and the selected product recommendation list is dynamically updated. According to the system, multi-platform commodity data are integrated, versioning feature snapshots are constructed, hyper-parameter selection of a multi-snapshot version evaluation strategy optimization model with a consistent time sequence is adopted, the generalization ability of a commodity selection model to a time-varying data environment is enhanced, the possibility that the accuracy of the model is reduced due to the change of data distribution is reduced, and the accuracy of the commodity selection model is improved. Meanwhile, dynamic updating of the selected product recommendation list is achieved, and it is ensured that the recommendation strategy can be fully matched with preference migration of the selected product trend.

The invention belongs to the field of industrial thermal engineering and intelligent modeling, and discloses a heat exchanger heat exchange coefficient prediction method and system based on a physical information neural network. The method comprises the following steps: acquiring multi-dimensional operation data through a signal acquisition system, cleaning abnormal and blank values, standardizing, and segmenting into time sequence samples by adopting a sliding window method; a double-layer physical information long-short-term memory network is constructed, and a time sequence feature and a physical equation residual error are combined to generate a space-time fusion feature matrix. And a composite loss function including data loss, physical equation loss and physical consistency loss is designed, physical and data driving influences are balanced through hyper-parameter tuning, and accurate prediction of the heat exchange coefficient is achieved based on a gradient descent optimization model. The method combines field physical laws and data features, improves the reliability and physical interpretability of prediction, and is suitable for operation optimization of the heat exchanger of the desulfurization wastewater treatment system of the thermal power plant.

The invention discloses a deep foundation pit multi-source monitoring data generative adversarial network anomaly diagnosis method, and relates to the technical field of civil engineering, and the method comprises the following steps: collecting multi-source heterogeneous monitoring data from a deep foundation pit automatic monitoring system, and carrying out the normalization processing; based on the preprocessed normalized multivariate time sequence, constructing a condition vector containing static and dynamic context information for each sample, and forming an input form which can be directly processed by a network model; designing a generator and discriminator network for the constructed sample fragments and condition vectors; integrating a space-time constraint loss function into a total loss function of the generator, and physically constraining generated data; initializing network parameters and hyper-parameters, and alternately updating discriminator and generator parameters to obtain a generator model; and searching an optimal noise vector for a to-be-detected sample through an optimization process by utilizing the generator model for training convergence, calculating a comprehensive anomaly score, and judging anomaly according to a threshold value.

The invention discloses a light energy power station fault prediction system based on deep learning. The system comprises a data acquisition module used for reading equipment operation data from a sensor; the data preprocessing module is used for denoising, interpolating and standardizing the equipment data; the graph convolutional network construction module is used for constructing an equipment data graph structure and extracting features; the Lemap dimension reduction module is used for mapping the high-dimensional equipment features to a low-dimensional space; the time sequence modeling module is used for constructing a time sequence prediction model based on the low-dimensional features; the hyper-parameter optimization module is used for optimizing hyper-parameters of the time sequence model; the model verification module is used for evaluating the precision and response time of the fault prediction model; the model deployment module is used for deploying the prediction model to a monitoring system; the fault prediction and early warning module is used for monitoring in real time and generating fault early warning; and the continuous optimization module is used for regularly optimizing and retraining the fault prediction model. The method achieves the high efficiency of fault prediction of the light energy power station, remarkably improves the prediction precision and the reliability of equipment operation, and is widely suitable for equipment monitoring and early warning.

The invention provides a novel intelligent rotating machine fault diagnosis method, and belongs to the technical field of rotating machine fault diagnosis, and the method comprises the steps: obtaining vibration signals of a rotating machine under normal and different fault types; a variation mode decomposition method is adopted to pre-process and decompose the vibration signal of the rotating machine, noise is removed, and then a new signal is reconstructed and generated; the new vibration signal is converted into a two-dimensional time-frequency image through short-time Fourier transform; optimizing hyper-parameters of a convolutional neural network (CNN)-long and short term memory neural network (BiLSTM) by adopting a Sharla silver ant optimization algorithm (SSAO), wherein the hyper-parameters comprise the number of neurons of a convolutional layer, the size of a convolution kernel and the number of neurons of a BiLSTM layer; and inputting the two-dimensional time-frequency image into the CNN-BiLSTM model after the hyper-parameter optimization to realize fault diagnosis of the rotating machine. The method has very important practical significance for improving the rotating machine fault diagnosis accuracy and guiding equipment maintenance and repair.

The invention relates to a PINN-CNN target impact damage nephogram prediction model establishment method based on MHA enhancement. Comprising the following steps: acquiring data: acquiring a target impact condition data set and a damage cloud picture data set, and dividing the data into a training set, a verification set and a test set after preprocessing; building a network model based on a CNN-MHA neural network algorithm; integrating a PINN physical constraint module into the established network model to obtain a depth prediction network model; updating hyper-parameters of the deep prediction network model to obtain an optimal model; and performing impact damage prediction. According to the method, hierarchical characterization of spatial features of the damage cloud picture is realized through CNN, time sequence correlation and cross-regional coupling effect of damage evolution are captured by means of an MHA mechanism, and generalization ability of a physical constraint enhancement model introduced by PINN is combined, so that the damage cloud picture can be identified in a complex impact condition scene with large data noise or missing data. The high-precision prediction from the current damage state to the future multi-moment damage cloud atlas has important engineering application value.

The invention discloses a weld defect identification method based on a dense connection convolutional network model, and the method specifically comprises the following steps: S1, constructing a dense connection convolutional network model, and embedding a coordinate attention module behind a transition layer of a convolutional network; s2, data acquisition and processing: acquiring an RGB image of the welding seam through an industrial camera, constructing a data set of the image, and performing image enhancement and standardization processing; s3, performing hyper-parameter optimization, and performing global optimization on the constructed model by adopting a Bayesian optimization algorithm; s4, performing model training and verification, and training a dense connection convolutional network model by using the optimized hyper-parameter combination; and S5, defect identification: inputting a to-be-detected welding seam image into the trained dense connection convolutional network model, and outputting a defect category and a positioning result. According to the method, the transition layer of the convolutional network is embedded into the coordinate attention module, so that the convolutional network model more accurately positions the welding seam position, and the detail features of the welding seam are extracted.

The invention relates to the technical field of landform classification, and discloses a complex mountain landform intelligent classification method and system based on dual-scale TPI optimization, and the method comprises the steps: firstly obtaining ASTER GDEM data of a research area, dividing a landform analysis unit, and extracting initial landform factors (large / small scale landform position indexes, gradients and elevations); after standardization processing, calculating a Pearson correlation coefficient and a variance expansion factor to diagnose factor colinearity, and then determining an optimal terrain factor combination through screening calibration; then matching the data with historical landform classification data of a research area, judging whether to adjust input parameters of a machine learning model, if so, determining primary and target influence coefficients of the model, and optimizing hyper-parameters of a random forest, extreme gradient lifting and a deep neural network according to the primary and target influence coefficients; and finally, finishing the landform classification of the research area by utilizing the optimized model and outputting a result. By optimizing terrain factor combination and machine learning model parameters, the accuracy and efficiency of landform classification of the research area are improved.

The invention provides a near-infrared model cross-device application method based on multi-source fusion optimization, and aims to solve the problem that various neural network models constructed by a high-precision near-infrared spectrometer cannot be applied to portable devices when being migrated to the portable devices. And the model performance is reduced due to differences of an optical system structure, a wavelength range, a signal-to-noise ratio, an instrument response characteristic and the like. According to the method, the near infrared spectrum data and the conventional quality parameters are subjected to feature level fusion, and the cross-equipment spectrum deviation is calibrated and compensated by using the stability of the conventional quality parameters, so that efficient knowledge migration from high-precision equipment to portable equipment is realized in the fused feature space. The problem of cross-device feature dimension mismatching is solved through a stable feature importance evaluation-based wave band alignment method; a small sample optimization strategy and a hyper-parameter automatic search technology are combined, so that the transfer learning effect is improved, the performance of a portable equipment detection model is remarkably enhanced, and meanwhile, the modeling cost is greatly reduced.

The invention is suitable for the technical field of health management of lithium ion batteries, and provides a method comprising the following steps: extracting a relaxation voltage sequence after full charge; extracting deep-level features related to battery aging from the relaxation voltage sequence by adopting CNN (Convolutional Neural Network); processing the extracted features based on LSTM to obtain a preliminary battery SOH estimation result; and adopting a war strategy algorithm to optimize the deep learning model, obtaining an optimal hyper-parameter and an optimal SOH estimation model, and obtaining an optimal battery SOH estimation result. According to the method, deep aging features are extracted by using multilayer convolution, a complex mode and a nonlinear relation hidden in relaxation voltage reflecting battery aging are found, and time dependence is established through LSTM; wSO is used for optimizing related hyper-parameters, the solution space is searched more comprehensively and quickly, local optimum is not likely to happen, accurate hyper-parameter selection is achieved, the neural network prediction precision is effectively improved, and the lithium ion battery SOH estimation accuracy is improved.

The invention discloses a multi-modal data drawing logical relationship analysis method, electronic equipment and a medium, and the method comprises the steps: generating a node set based on drawing image data and text data; generating a cross-modal hyperedge set based on the spatial proximity relationship, the visual feature similarity and the semantic correlation between the node sets; generating a hypergraph embedding input representation based on the node set and the cross-modal hyperedge set; the hypergraph is embedded into the input representation input improved hypergraph self-attention network model, and a hyperedge logic relation type and a corresponding hyperedge confidence coefficient are generated; generating a graph structure result based on the hyperedge logic relationship type and the node set, wherein the graph structure result meets the structure legality requirement; and performing hyper-parameter automatic adjustment and convergence control on the atlas structure result based on hyper-edge confidence, and generating an optimal atlas analysis model and a structured output result. According to the method, the reliability and the quality of analysis of component nodes, logic edge relationships and semantic structures in the drawing are improved.

The invention belongs to the technical field of deep neural networks, and discloses a short-term power load prediction method based on a hybrid neural network model, and the method comprises the steps: 1, obtaining an original power load time sequence; 2, performing adaptive decomposition on the original power load time sequence to obtain an intrinsic mode function (IMF) component; step 3, for the plurality of IMF components obtained after decomposition, fusing meteorological features and peak period features to obtain a fusion time sequence, then performing feature extraction and joint prediction, and constructing a multi-modal prediction model; and step 4, intelligently optimizing the key parameters of the multi-modal prediction model, outputting optimal hyper-parameters, and performing optimal parameter setting on the multi-modal prediction model to obtain a final prediction result. According to the method, the accuracy and robustness of prediction are effectively improved, and the adaptability of the model to non-stationary and multi-scale energy time sequences is effectively improved.

The invention discloses a fruit and vegetable mature period prediction method based on deep learning. The method comprises the following steps: S1, obtaining a preprocessed fruit and vegetable growth environment and state data set; s2, constructing multi-scale time window statistical features based on the preprocessed fruit and vegetable growth environment and state data set, and performing feature fusion on the multi-scale time window statistical features and the preprocessed fruit and vegetable growth environment and state data set to generate a fusion feature vector; s3, constructing a prediction model structure; s4, initializing a grey wolf optimization algorithm search space, taking the preprocessed fruit and vegetable growth environment and state data set as a training sample, taking a prediction error index as a fitness function, and iteratively searching the search space to obtain an optimal hyper-parameter set, so as to obtain a trained prediction model; and S5, inputting fruit and vegetable growth environment and state data collected in real time into the trained prediction model, outputting a fruit and vegetable mature period prediction result, and generating a mature period prediction time sequence. According to the invention, it is ensured that the prediction result is accurate and stable, so that high-reliability support is provided for agricultural picking management.

The invention relates to the technical field of intelligent retrieval, and discloses an intelligent dynamic K value retrieval optimization system and method, and the system comprises a vector retrieval and storage module, a dynamic K value generation module and an adversarial training optimization module. The system introduces a problem multi-dimensional feature library, wherein multi-dimension of the problem comprises a domain label, a complexity level and a candidate result cardinal number of the problem; the system adjusts the top-k value during retrieval through the dynamic K value generation module, and optimizes the performance of the generator in combination with the adversarial training optimization module. According to the intelligent dynamic K value retrieval optimization system and method, multiple technologies of vector database storage, embedded vector caching, GAN model training, dynamic temperature adjustment, multi-index fusion optimization, incremental updating and hyper-parameter automatic tuning are integrated. The method is characterized in that a k value selection problem is converted into a learnable generation task, a generator can predict the number of optimal retrieval results for a specific problem through adversarial training, and the limitation that the k value is fixed in a traditional retrieval system is broken through.

The invention provides a time series data acquisition method, system and device based on a large model and a medium, and belongs to the technical field of data acquisition. The method comprises the following steps: collecting time sequence data from a plurality of data sources, and sending the time sequence data to an edge computing node for data filtering, protocol conversion and time synchronization processing to generate structured time sequence data; preprocessing the structured time sequence data, and generating standardized time sequence data after time sequence segmentation processing; reading the standardized time series data, respectively extracting signal features and deep features in the standardized time series data, fusing the signal features and the deep features, and screening feature vectors from the signal features and the deep features; selecting a neural network model according to the task type, performing distributed training of the model by using the feature vector, optimizing and adjusting hyper-parameters of the model, and generating a target model; and deploying the target model in a production environment, collecting time sequence data in real time, preprocessing the time sequence data, inputting the preprocessed time sequence data into the target model for reasoning, and optimizing the target model according to a reasoning result.

The invention provides an intelligent power distribution room sensor fault early warning method and system based on multi-modal data fusion. The method comprises the following steps: step 1, realizing working condition adaptive acquisition of multi-modal data; step 2, eliminating an alignment scheme of physical contradictions; step 3, noise analysis and filtering parameter adaptive cooperation are realized; 4, performing primary decomposition on the denoised signal, and analyzing a non-stationary signal; step 5, realizing security sharing and weighted aggregation of cross-node features; step 6, constructing a multi-modal sensor fault diagnosis model based on the time consistency fracture field; and step 7, optimizing hyper-parameters of the multi-modal sensor fault diagnosis model based on the time consistency fracture field. According to the invention, early warning of faults is realized, fault traceability and propagation path analysis capability are realized, deep support is provided for operation and maintenance decision, and the overall operation reliability and intelligent operation and maintenance level of the power distribution room are effectively improved.

The invention belongs to the field of integrated circuit testing, and particularly relates to a glass through hole defect detection method based on WOA-Light GBM. Aiming at the problems that the existing detection means is low in efficiency and high in cost and the precision depends on human experience, three-dimensional models of a TGV structure under different defect conditions are established through electromagnetic simulation software, the three-dimensional models comprise defect models of uneven through hole contours and multi-stage side wall roughness characteristics, and corresponding S parameters are extracted as judgment bases; and constructing a feature data set. On this basis, a LightGBM classification model is constructed, a whale optimization algorithm is adopted to carry out global search and optimization on key hyper-parameters, an optimal parameter combination is obtained, and automatic classification and identification of the defect TGV are realized. Therefore, according to the glass through hole defect detection method based on the WOA-LightGBM, high-precision detection of various types of defects can be achieved in a lossless mode, the algorithm training efficiency is high, the method is suitable for the large-scale production detection process, and flexibility is provided for manufacturing of high-standard and high-reliability glass through holes.

The invention discloses a biped robot gait network training method. The method comprises the following steps: constructing a dual-channel deep reinforcement learning architecture; the running states of the X biped robots in the simple terrain of the simulation environment are collected; obtaining a current reward according to the current running state, merging each piece of information into a Markov decision process, and storing the information into an experience playback area; when the number of the Markov decision-making processes in the experience playback area is greater than a preset threshold value n, randomly taking a preset number of Markov decision-making processes from the Markov decision-making processes, and updating parameters of the main network and the opponent network; disturbance is carried out on main network parameters, and a human memory curve is simulated to carry out continuous adjustment on main network hyper-parameters clip; the biped robot with the stable walking duration reaching the preset duration is moved to the terrain with the higher difficulty level, and the network parameter updating process is repeated; and course learning is continuously carried out until the accumulated reward information and the stable walking duration of all the biped robots reach preset values.

The invention provides an AI large model reinforcement learning hyper-parameter dynamic adjustment method and device, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a first performance evaluation value under a first time window and a second performance evaluation value under a second time window in a training process of an AI large model; determining the performance variation of the AI large model from the first time window to the second time window according to the difference between the first performance evaluation value and the second performance evaluation value; according to the performance variation, the first performance evaluation value and the second performance evaluation value, calculating a comprehensive performance score of the AI large model; according to the comprehensive performance score and a configured performance threshold value, determining an adjustment direction of a hyper-parameter of the AI large model; and according to the configured adjustment factor and adjustment direction, adjusting hyper-parameters of the AI large model. According to the method, stable and efficient learning can be realized in the training process, and the performance of the AI large model is improved.

The invention discloses a two-stage algorithm selection and hyper-parameter joint optimization method, which comprises the following steps of: in the first stage, processing a training set and a test set through row sampling operation and column dimension reduction operation to form a reduced data set; randomly sampling a certain number of configurations in the hyper-parameter space of each candidate algorithm, evaluating the performance of each candidate algorithm by using the reduced data set, and extracting an optimal performance score; in the second stage, a previous algorithm is screened according to the optimal performance score to form a candidate set, and a pruned hyper-parameter search space is formed so as to reduce the calculation complexity of processor hyper-parameter search; and performing hyper-parameter optimization on the pruned hyper-parameter search space by using the original data set, and outputting an optimal algorithm adaptive to the target technical task and hyper-parameter configuration thereof. Algorithm screening and hyper-parameter tuning adaptive to a specific scene are realized through a two-stage optimization strategy, and meanwhile, the method is suitable for a traditional table type dichotomy task and aims at improving the deployment efficiency and performance of a machine learning model.

The invention relates to the technical field of air conditioner load prediction, and provides an air conditioner load prediction method and system, and the method comprises the steps: extracting intra-day meteorological data features and intra-day air conditioner load data features, and forming multi-dimensional data features; performing dimension reduction processing on the multi-dimensional data features to form a comprehensive feature curve; clustering the comprehensive characteristic curve, dividing the air conditioner load data in the historical day into a data set according to a clustering result, and dividing the data set into a training set and a test set; a plurality of prediction models corresponding to different meteorological scenes are constructed, the prediction models are trained through the corresponding training sets, hyper-parameter tuning is conducted on the prediction models through an improved sodat swarm optimization algorithm, and a plurality of air conditioner load prediction models are formed; and inputting the test set corresponding to various meteorological scenes into the corresponding air conditioner load prediction model, and outputting an air conditioner load prediction result. According to the invention, air conditioner load curves with obvious boundaries in different meteorological scenes can be effectively separated, and air conditioner power load prediction errors in extreme weather are reduced.

The invention provides a cutting force prediction method and device based on a multi-target frost ice algorithm optimization model, and relates to the technical field of mechanical cutting machining. The method comprises the following steps: acquiring sensor signal data including cutting force data and vibration signals, extracting target features which are highly related to the cutting force from the vibration signals, constructing target feature vectors, constructing a cutting force prediction model based on SVR, and determining an SVR hyper-parameter range through initialization; the target feature vector is used as an input sample, the cutting force data is used as an output target value, the SVR hyper-parameter range is used as a constraint condition, a multi-target frost ice algorithm and a multi-target optimization mechanism are combined to optimize the SVR hyper-parameter, finally, the SVR is trained based on the optimized hyper-parameter to obtain a prediction model, and the cutting force of the numerical control machine tool is predicted. Efficient and high-precision cutting force prediction can be achieved without a large number of data samples, and the model has good generalization and feature interpretability.

The invention discloses a physically and data-driven composite material multi-parameter inversion identification method, which comprises the following steps of: performing non-contact full-field deformation measurement on a composite material test piece to obtain displacement distribution data; performing numerical differentiation on the displacement distribution data through a spatial difference method to obtain strain distribution data of the composite material test piece, and performing gridding processing on the strain distribution data of the surface of the composite material test piece; constructing a neural network model; constructing a multi-objective loss function; composite material parameters are preset, the composite material parameters are embedded into the neural network model to participate in gradient operation, and the gradient of a loss function is obtained; and carrying out training optimization on the gradient of the loss function, adjusting the weights of the control item loss function, the boundary item loss function, the constitutive relation item loss function and the data item loss function, optimizing the hyper-parameters of the neural network model until a preset convergence condition is met, and obtaining the target parameters of the neural network model. The defect that the constitutive parameters of the thick-section composite material are obtained through an experimental method is effectively overcome.