679 results about "Hyper parameters" patented technology

The technical scheme of the invention relates to the technical field of gas detection, in particular to a hazardous chemical storage leakage positioning and tracing method based on a gas array. The method comprises the following steps: collecting gas concentration, components and environmental physical field parameters through a three-dimensional heterogeneous sensor array, and generating a multi-modal data set; and matching the noise mode feature library in the knowledge base by using a transfer learning algorithm, dynamically correcting the baseline drift of the sensor, and outputting calibrated data. A closed-loop adaptive mechanism adjusts sensor parameters and network hyper-parameters through meta reinforcement learning, neural architecture searches and optimizes a model structure, and a differential evolution algorithm updates fluid mechanics boundary conditions. Through cooperation of physical constraint and data driving, the problem of signal distortion in a complex environment is solved, the leakage source positioning precision and robustness are improved, and the method is suitable for the field of hazardous chemical substance storage safety monitoring.

The invention discloses a vehicle intelligent driving decision optimization method based on deep learning. The method belongs to a vehicle intelligent driving decision technology. Firstly, various sensors are installed on a vehicle to collect data, and data sets are divided according to a proportion after cleaning and marking. A model containing an encoder-decoder and an attention mechanism is constructed, the weight is initialized, and a hyper-parameter and anti-overfitting strategy is set. And training the model by using a specific algorithm, calculating a loss value and evaluating and adjusting according to a verification set. After the model is integrated to the system, a preliminary candidate decision is determined according to a probability threshold, the decision is optimized in combination with a vehicle surrounding environment information set and a risk assessment function, and decision probabilities are redistributed in real time according to environment changes by means of an environment perception feedback mechanism. The method overcomes the defects of the traditional technology, utilizes the advantages of deep learning, effectively improves the driving decision precision, enhances the adaptability of the system to complex road conditions and environments, and guarantees the safe and efficient operation of intelligent driving.

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 discloses a lithium battery charge state estimation method based on a Bayes-TLCO optimization deep neural network, and belongs to the technical field of battery state monitoring. The method comprises the following steps: firstly, preprocessing a lithium battery charging and discharging data set; then, constructing a deep neural network model comprising a convolutional neural network, a long-short-term memory network and a multi-head attention mechanism, dynamically optimizing hyper-parameters of the model by using a Bayesian optimization-assisted termite life cycle optimization algorithm, introducing Bayesian optimization during iteration stagnation in a TLCO algorithm iteration process, and finally obtaining a termite life cycle optimization model; fitting historical data through a Gaussian process to construct a search empirical model, generating high-value sampling points, and accelerating model hyper-parameter convergence to a globally optimal solution; and finally, estimating the state of charge of the lithium battery. The method breaks through the limitation of a single algorithm, achieves the high-precision estimation of the state of charge of the lithium battery under a complex working condition, effectively improves the model training efficiency, is suitable for electric vehicles, energy storage systems and other scenes, and provides a key technical support for the intelligent upgrading of battery management.

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 relates to an advanced gastric cancer survival prediction system based on Lasso regression, Cox regression and an interpretable machine learning technology, and belongs to the technical field of medical artificial intelligence and intelligent decision support. According to the system, by collecting multi-modal clinical data (including demographic information, TNM staging, treatment modes, tumor grading and the like) of a patient, survival-related variables are screened by adopting Lasso regression and a Cox proportional risk model, and an optimized feature set is constructed. Based on the feature set, the system integrates various mainstream machine learning algorithms (such as XGBoost, Random Forest, SVM, Logistic regression and the like) to construct a prediction model, compares the performance of each model, and selects a model with an optimal effect as a main model. And hyper-parameter tuning is performed on the model through grid search and cross validation, so that the precision and generalization ability of the model are improved. An SHAP interpretability analysis method is introduced into the system, transparent interpretation is carried out on a model output result from the global level and the individual level, and the importance and directional effect of all variables in survival prediction are determined. Finally, the model is deployed on a terminal device, a doctor is supported to automatically output the survival probability and an explanation result after inputting patient information, and a reference basis is provided for clinical treatment decision and personalized management. The system has the advantages of high prediction precision, high interpretability, convenience in use, sustainable optimization and the like, is suitable for clinical aid decision-making scenes, and has good application prospects and popularization values.

The invention discloses a drug target activation and inhibition relation prediction method based on a depth map neural network, and aims to improve the modeling precision and prediction performance of an activation or inhibition action mechanism between a drug and a target. According to the method, on the basis of a fine-grained graph interaction modeling mechanism, multi-scale structural characteristics of drug molecules and three-dimensional space structural information of protein residue levels are fused, and a heterogeneous interaction graph between drugs and proteins is constructed. The method comprises the following steps: firstly, acquiring a drug-target sample with an activation / inhibition tag through a public database, predicting a protein structure by utilizing AlphaFold2, and constructing a protein residue map and a drug molecular map; multi-scale structure semantic representation is obtained through sub-graph decomposition, atomic-scale feature extraction and graph neural network coding of drug graph features; protein graph node features are combined with context embedding generated by a pre-training language model, DSSP coding, secondary structure spectrum and atomic structure features are constructed, and edge features are designed based on the geometrical relationship between residues. Then, based on constraints such as spatial distance and biochemical similarity, a fine-grained mapping relation between drug atoms and protein residues is established, an interaction graph is constructed, and coding is carried out through a GraphSAGE network; and finally, fusing the interacted multi-source embedding, and completing the prediction of the activation / suppression relationship through a multi-layer perceptron. A cross entropy loss function, an Adam optimizer and hyper-parameter grid search are adopted in model training; in the evaluation stage, five-fold cross validation and an independent test set are adopted, and indexes such as the accuracy rate, the recall rate, the F1 score, the specificity and the Morse correlation coefficient are used for comprehensively evaluating the performance of the model. Experimental results show that compared with an existing method, the method has the advantages that the prediction accuracy and mechanism interpretability are remarkably improved, and the method has good generalization ability and application prospects and is suitable for multiple fields of drug action mechanism research, new drug discovery and the like.

The invention provides a reinforcement learning path planning method for multiple unmanned vehicles in an intelligent factory. The method comprises the following steps: building a feeding path planning simulation environment; taking each unmanned vehicle as an intelligent agent, interacting the intelligent agent with the environment to extract information, and constructing a state space, an action space and a reward function of the Markov decision model according to the information; building an IPPO algorithm network, and training the strategy of the Markov decision model based on the IPPO algorithm network and the reward function; during training, a strategy network loss function with a rollback strategy is obtained through improvement by combining cutting loss with KL divergence in a mode of introducing hyper-parameters; and determining the current state at each moment according to the actual current position of the unmanned vehicle, inputting the current state into the strategy network, and outputting the current action to be executed by the unmanned vehicle. The method is high in convergence speed, stable in strategy updating, high in exploration capacity, capable of effectively reducing path blockage and high in environment adaptability.

The invention discloses a quasi-brittle material damage field inversion method based on a physical information neural network. The method comprises the steps of data acquisition and processing; presetting initial damage field data, and taking the modulus of each unit in the initial damage field data as an independent to-be-inverted parameter; taking the position information of each node in the space as the input of a neural network, processing through a hidden layer of the neural network, and taking displacement data corresponding to the position information as network output data; constructing a loss function of a neural network according to the preprocessed displacement data, network output data and a mechanical law; and performing training optimization on the initial damage field data by using a loss function of a minimized neural network, optimizing hyper-parameters of the neural network by adjusting weights of control item loss, boundary item loss and data item loss until a preset convergence condition is met, and determining target damage field data. According to the method, the dependence of a neural network model on a data set is remarkably reduced, and the inversion result is ensured to have relatively high physical interpretability.

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 discloses a water environment dynamic pollution monitoring and atmospheric diffusion prediction method based on deep learning, and the method comprises the following steps: S1, collecting multi-source monitoring data of a water environment and an atmospheric environment, and constructing a pollution time series data set and an atmospheric meteorological data set; s2, constructing a variational auto-encoder model; s3, jointly optimizing a variational auto-encoder model structure and hyper-parameters by using a hosting crab optimization algorithm; s4, the optimized variational auto-encoder model is used for training, and potential variables of the pollution state are extracted; s5, constructing a diffusion modeling network, and outputting a diffusion prediction result; and S6, comparing a diffusion prediction result with a pollution threshold value, and triggering early warning information output when the diffusion prediction result exceeds the standard. According to the invention, intelligent monitoring of the water environment pollution state and accurate prediction of the diffusion behavior of pollutants to the atmosphere are realized, and efficient decision support is provided for environment early warning and treatment.

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 field of bearing fault detection, in particular to a bearing fault diagnosis method based on time-frequency enhancement CNN-Transformer, which comprises the following steps: acquiring a bearing vibration signal, and preprocessing the bearing vibration signal; selecting a window size, an interval step length and an overlapping rate, and dividing the preprocessed bearing vibration signal data into a training data set, a verification data set and a test data set by adopting a sliding window; an FFT-CNN-Transform network model is constructed, and the FFT-CNN-Transform network model is constructed; an Adam optimizer and a cross entropy loss function are adopted to train an FFT-CNN-Transform network model, hyper-parameters of the FFT-CNN-Transform network model are adjusted through a verification data set, and the performance of the FFT-CNN-Transform network model is evaluated through the test data set; and by drawing a confusion matrix, the classification effect of the FFT-CNN-Transform network model on different fault categories is displayed. According to the method, key features of a time domain and a frequency domain can be captured at the same time, and data features are compared with fault features, so that more accurate fault prediction is realized, and the accuracy and reliability of bearing fault diagnosis under complex working conditions are remarkably improved.

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 a rolling bearing fault diagnosis method and device based on a composite multi-scale attention entropy and an optimized SVM, and a medium, and the method comprises the steps: collecting vibration signals of a rolling bearing in different states, obtaining the damage size grade in each state according to the vibration signals, and obtaining a fault data set; performing coarse graining processing on the vibration signal to obtain a coarse grain sequence, and calculating an entropy sequence of the coarse grain sequence under different scale factors by adopting a composite multi-scale attention entropy algorithm improved by a fractional order algorithm; entropy values of a plurality of first scale factors in the entropy sequence are selected as bearing fault feature vectors; optimizing hyper-parameters of the support vector machine by using a collaborative group optimization algorithm; and constructing a support vector machine classification model based on the optimal hyper-parameter, and performing fault prediction and judgment on the test sample. Compared with the prior art, the method has the advantages of high noise robustness, high global convergence, high adaptability and the like.

The invention discloses a coal mine earthquake time sequence feature prediction method based on a multi-source data space-time diagram convolutional network, and belongs to the field of mine dynamic disaster prevention and control. The method comprises the following steps: firstly, fusing micro-seismic data and charge data, and extracting and integrating features of two data sources by using a feature fusion technology so as to more comprehensively capture dynamic features of an underground environment; then, constructing a graph structure, taking the sensors as nodes, and defining edges among the nodes by using Euclidean distances among the sensors so as to reflect spatial correlation of geological activities; and dynamically capturing the correlation between the time dimension and the space dimension through the space-time diagram convolutional network. And then, optimizing hyper-parameters in the model by using WOA to obtain an optimal parameter combination, and improving the performance and accuracy of the model. And finally, introducing a space-time attention mechanism, and dynamically adjusting data input by calculating a time attention matrix and a space attention matrix so as to better capture space-time characteristics and improve the accuracy of model prediction.

The invention discloses a big-data-oriented real-time analysis and fault positioning system, and the system comprises a data collection module which is used for collecting a multi-source data stream, and constructing a high-dimensional feature data set; the confrontation feature compression module is used for compressing high-dimensional features through multi-layer nested mapping and generating initial potential feature representation; the sparrow population module is used for constructing a sparrow individual population containing a plurality of hyper-parameter combinations; the feature evolution analysis module is used for deploying the optimized analysis confrontation model to a big data platform; the abnormal event detection module is used for carrying out sliding window analysis based on the change amplitude and the deviation trend of the potential characteristic track in the time sequence and judging whether a real-time abnormal event is formed or not; the historical alignment matching module is used for marking a specific time index and a source channel of fault occurrence; and the label identification module is used for completing fault type identification and fault type label output based on the fault occurrence time index and the source channel. According to the invention, an accurate big data real-time analysis and fault positioning scheme is provided for the user.

The invention discloses a robot grinding surface roughness prediction method considering dynamic factors based on deep learning, and relates to the technical field of industrial robots. The method comprises the following steps: carrying out self-extraction on grinding machining dynamic factor spatial features by adopting a convolutional neural network; performing time sequence feature extraction on the extracted space by adopting a bidirectional long-short-term memory network; carrying out standardization and normalization processing on the extracted space and time sequence features and static factors; utilizing an improved whale algorithm to optimize hyper-parameters of the bidirectional long-short-term memory network, and further introducing an attention mechanism to realize feature automatic weight distribution; the steps are integrated, and an IWOA-CNN-BiLSTM-Attention surface roughness prediction model is constructed; and inputting the static factors, the extracted features and the surface roughness measurement value into a prediction model for model training, and outputting a surface roughness prediction value to realize a surface roughness prediction function. The method can solve the problem of difficulty in dynamic factor feature selection, and improves the model prediction precision.

The embodiment of the invention provides a hyper-parameter adjustment method based on safety reinforcement learning, and the method comprises the steps: firstly converting an optimization problem of a control strategy into a Lagrange problem with a constraint condition, defining a primary loss function and a secondary loss function, and setting a safety threshold value; secondly, combining with a Lagrange multiplier to construct an initial Lagrange function so as to minimize main loss and meet security constraints; secondly, setting an initial value of a Lagrange multiplier by solving a dual problem, and then introducing a damping factor to construct an enhanced function; and finally, after model parameters are initialized, updating the model parameters by using a gradient descent method and updating the multiplier by using a gradient ascent method in iteration, and substituting into an enhanced function to evaluate whether the strategy is optimal, namely, the main loss is minimum and the security constraint is met, until a global optimal solution is found. Therefore, the optimization of the control strategy is realized, the system performance can be improved, the system safety is guaranteed, the oscillation of the learned strategy when the constraint is satisfied is prevented, and the stable proceeding of the reinforcement learning process is ensured.

The invention discloses a Chinese named entity recognition system, method and equipment based on multi-scale features and a medium, and the method comprises the steps: decomposing an original text into a character sequence through a preprocessing module, and preprocessing characters, including removing punctuation marks and uniformly converting the characters into lower letters; a RoBERTa-WWM sub-module of a feature extraction module is responsible for converting an input text into a high-dimensional feature vector, a CNN sub-module effectively extracts local features through a sliding window mechanism, and a BiLSTM sub-module finally utilizes the advantage of bidirectional processing to obtain complete context information; decoding the hidden state representation by using a dynamic conditional random field (CRF) through a sequence tagging module to determine an optimal tag sequence; according to the method, pre-training, multi-scale feature fusion and dynamic decoding technologies are integrated, and efficient and accurate Chinese entity recognition is realized; through a unique preprocessing rule, a modular architecture design and optimized hyper-parameter configuration, the performance and robustness of the system in a complex scene are ensured.

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 discloses an attention mechanism-based operation area construction behavior intelligent identification method, which comprises the following steps of S1, collecting and preprocessing multi-modal data, and generating a standardized construction action time sequence data set; s2, constructing a Transform network model, and extracting spatial features and time features; s3, the Transform network model is optimized based on a parrot optimization algorithm, and optimal hyper-parameter configuration is obtained; s4, carrying out training by utilizing the optimized Transform network model, and carrying out supervised learning by adopting a cross entropy loss function; s5, performing construction behavior intelligent identification, and outputting a construction action category and an identification confidence coefficient; and S6, triggering a real-time early warning mechanism, and pushing early warning information to a construction safety management platform. According to the method, efficient and accurate construction behavior recognition and high-risk action detection are realized by optimizing the Transform network model and dynamically adjusting the hyper-parameters, and the safety and management efficiency of a construction site are improved.

The invention discloses an in-situ ultrasonic crack detection and defect identification system for an in-service fan main shaft, which is characterized in that original acoustic signals of the fan main shaft in different operation states are acquired and are subjected to noise reduction and normalization processing by a signal acquisition and preprocessing module; and extracting voiceprint feature maps of the principal axis individuals by using improved zero-ratio adaptive threshold segmentation, and generating voiceprint feature maps of the crack individuals in different states. And dividing the normalized voiceprint feature map into a training set with known crack defect types, a test set and a test set with unknown operation states. An MSCNN model structure is designed, relevant parameters are initialized, forward propagation and back propagation iterative calculation are repeatedly executed by utilizing known crack defect types, model hyper-parameters and training parameters are adjusted through a loss function, and an optimal model is obtained. And inputting unknown crack data into the trained MSCNN model, and finally outputting an identification result of the unknown crack of the fan main shaft, thereby realizing crack detection and classification.

The invention discloses a method for predicting the residual SOC of a rechargeable battery of an electric vehicle based on an improved sodat swarm algorithm, and the method comprises the steps: firstly collecting historical charging data, and carrying out the preprocessing of the data; analyzing and knowing that the residual SOC of the charged electric vehicle is mainly influenced by the current, the charging electric quantity, the charging efficiency, the charging time and the rated capacity of the battery; constructing a feature matrix according to the influence factors, and taking the feature matrix as the input of an electric vehicle rechargeable battery residual SOC prediction model; then, building a prediction model of a dual-channel feature extraction module formed based on a convolutional neural network and a long-short term memory neural network, and optimizing hyper-parameters of a hybrid neural network model by using a salat swarm optimization algorithm; secondly, a self-attention mechanism is fused to strengthen the relation among the features, so that the extraction capability of the model on spatial-temporal features is improved; and finally, inputting the feature matrix into the optimized prediction model, and predicting the residual SOC of the rechargeable battery.

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 discloses a photovoltaic power generation power prediction method based on Bi-LSTM, and relates to the technical field of photovoltaic power generation power prediction methods. The method comprises the following steps: constructing a Bi-LSTM recurrent neural network model; historical data of photovoltaic power generation power and related meteorological data are obtained to serve as model data and are aligned into a time sequence; preprocessing the model data and training a Bi-LSTM recurrent neural network model; and optimizing hyper-parameters of the Bi-LSTM recurrent neural network model by using a genetic algorithm to obtain an optimal Bi-LSTM recurrent neural network model to predict photovoltaic power generation power. According to the method, the Bi-LSTM model is utilized, and past and future information in the time sequence data can be captured at the same time, so that the accuracy of photovoltaic power generation power prediction is improved; meanwhile, effective features can be screened and utilized more effectively, the defects that a traditional prediction model is poor in feature screening capacity and prone to noise interference are overcome, and then the prediction precision of the photovoltaic power generation power is remarkably improved.

The invention provides a micro-milling machining parameter identification method considering the random tool wear influence. The method comprises the steps that a cutting force model under the tool wear influence is established by considering tool bounce and a chip separation mechanism; updating the rotating radius of the tool according to the influence of jumping and abrasion on the edge radius value of the tool, and obtaining a tool nose trajectory equation; tool wear data in the actual machining process are collected, the neural network model is trained, and hyper-parameters in the neural network model are optimized through a bidirectional long-short-term memory network; identifying processing parameter values in the neural network model by adopting a particle filtering algorithm; and simulating and calculating cutting forces and wear values under different working conditions by using a neural network model, comparing experimental results, and evaluating the accuracy of machining parameter identification. According to the method, the randomness of tool wear is fully considered in the modeling process, the prediction precision is remarkably improved, and the method has higher practical application value.

The invention belongs to the technical field of water supply network water quality monitoring, and provides a water supply network trihalomethane prediction method based on a fusion model. Comprising the steps of original water quality data set acquisition, data preprocessing, CNN-BiLSTM-MultiHead Attention fusion prediction model construction, hyper-parameter optimization space and model evaluation function definition, model prediction and output evaluation, model parameter iteration and verification evaluation, model verification, sample importance display, to-be-detected water quality data set collection and trihalomethane prediction. According to the method, the convolutional neural network, the bidirectional long-short-term memory network and the multi-head attention mechanism are fused, so that the change characteristics of a plurality of conventional water quality indexes in the water supply network are efficiently learned and extracted, and better prediction precision can still be kept under the condition of less training data; and the prediction performance and generalization ability of the model in the aspect of predicting a plurality of target variables by using a plurality of input variables are remarkably improved.

The invention relates to the technical field of data simulation, in particular to a bearing fault simulation method and system. The method comprises the following steps: collecting multi-modal bearing data so as to construct a bearing distributed edge data set; extracting contact power data of the bearing distributed edge data set, and calculating a bearing rigidity change curve according to the contact power data; carrying out random parameter modeling based on the bearing distributed edge data set, and carrying out physical modeling benchmark reference on the bearing rigidity change curve to obtain a bearing fault physical-data hybrid model; therefore, by integrating multi-modal data acquisition, physical-data hybrid modeling and a dynamic hyper-parameter adjustment mechanism, the defects of a traditional bearing fault diagnosis method in the aspects of accuracy and real-time performance are overcome, and the fault prediction and early warning precision and the response speed are improved.

The invention discloses an automatic machine learning collaborative optimization method based on hardware perception. The method is used for design of an integrated learning field programmable gate array accelerator in edge artificial intelligence. The method aims at solving the balance challenge between algorithm performance and hardware resource consumption. The method comprises the following steps: defining a joint optimization space containing algorithm hyper-parameters and FPGA hardware configuration parameters; a collaborative optimization objective function is established, the function is determined by algorithm performance indexes and predicted FPGA hardware resource occupancy, and resource prediction quantifies hardware influence according to algorithm hyper-parameters; and searching according to the objective function in the joint optimization space by utilizing an AutoML optimization algorithm so as to identify the optimal configuration of the balance performance and resources. According to the method, the problems of low resource efficiency and high development cost caused by separate optimization of an algorithm and hardware in the prior art are effectively solved, balanced design is realized, automation is enhanced, and complexity is reduced.