688 results about "Hyperparameter optimization" patented technology

A system is provided for managing Internet of Things (IoT) networks. The system includes a learning module configured to employ machine learning models with hyperparameters optimized through a hyperparameter optimization process; wherein the process includes evaluating a set of hyperparameters against a performance metric to select optimal hyperparameters that enhance the adaptability and efficiency of dynamic membership functions within an adaptive fuzzy logic engine (AFLE).

The invention relates to an automatic driving large model training optimization method based on multi-scene data balance. Comprising the following steps: (1) constructing a real vehicle high-speed driving scene library; (2) constructing a visual language automatic driving large model, training by adopting an iterative training framework based on a real vehicle high-speed driving scene library, designing a multi-task joint loss function and a weight adaptive adjustment strategy, realizing multi-task target balance, and obtaining a trained visual language automatic driving large model; (3) dynamic simulation is carried out for the automatic driving working condition, and a high-fidelity simulation data test set is constructed based on simulation data; and (4) performing hyper-parameter optimization and lightweight processing on the trained visual language automatic driving large model according to the high-fidelity simulation data test set to obtain a scene data balanced automatic driving large model. According to the method, the large model reasoning speed is increased, and resource occupation is reduced, so that the judgment capability of the large model on dynamic working conditions and high-risk scenes is remarkably improved.

The invention relates to a spinel multi-objective reverse design method based on machine learning and a Bayesian optimization algorithm, electronic equipment and a storage medium, and the design method comprises the steps: firstly extracting related data of a spinel material from a database, and constructing a balanced data set through preprocessing; feature engineering is carried out, a comprehensive feature set is constructed, and key features are reserved; then training a multi-target prediction model through hyper-parameter optimization by using a multi-task gradient elevator model; and finally, integrating to a Bayesian reverse design framework, expanding a design space through a specific encoder, and combining a Gaussian process proxy function and an expected hyper-volume improvement criterion to screen candidate materials meeting conditions and verify performance, thereby completing reverse design optimization. Compared with the prior art, the intelligent and efficient spinel novel multi-target reverse design method can be used for solving the problem of data scarcity, and development of high-performance spinel solar cell materials is accelerated.

Techniques for increasing the quality of output from large language models using reinforcement learning to select inference-time hyperparameters are disclosed. The large language model is configured with a set of values corresponding to a set of inference-time hyperparameters that are used to influence the output of the machine learning model after the model has been frozen. After obtaining a set of performance metrics that indicate the quality of the output, a reinforcement learning agent computes an adjustment for one or more of the hyperparameters, resulting in a modification of the hyperparameter values. Applying the new hyperparameter values, the large language model is then applied to a new set of input to generate a second output. The process iterates until the performance metrics associated with the output are satisfactory.

The invention relates to the technical field of typhoon prediction, and discloses a typhoon rapid enhancement prediction method based on time-space sequence and multi-modal feature fusion, and the method comprises the steps: constructing a multi-modal time-space sequence data set and an auxiliary data set based on typhoon optimal path data and multi-source satellite observation data; a unified manifold approximation and projection method is adopted to carry out dimension reduction preprocessing on the high-dimensional multi-modal space-time sequence data, and one-dimensional time sequence embedding representation of the typhoon observation sequence is generated; taking the one-dimensional time sequence embedded representation and the auxiliary data as independent input channels, and inputting a trained typhoon observation network model to predict a typhoon rapid enhancement probability; wherein the typhoon observation network model is a multi-mode time-space fusion deep learning architecture, the core of the typhoon observation network model is composed of a variational attention recurrent neural network, and hyper-parameter optimization is carried out through an improved Harris eagle optimization algorithm. According to the invention, accurate and robust identification of the typhoon rapid enhancement process is realized.

The invention discloses a multi-target intelligent optimization method and system for blasting parameters of a strip mine in a high-altitude cold region. The method comprises the following steps: carrying out data acquisition to obtain a parameter data set; performing data preprocessing on the parameter data set to obtain a feature sample set; constructing an initial blasting parameter model based on a machine learning algorithm, and performing hyper-parameter optimization on the model to obtain a blasting parameter model; a multi-objective optimization function is constructed: based on the multi-objective optimization function and the blasting parameter model, solving is carried out in combination with environmental condition constraints, and a pareto optimal solution set is obtained; according to the pareto optimal solution set, a representative solution is selected, a visual scheme is generated, and blasting parameter optimization of the strip mine in the high-altitude cold region is completed. According to the method, temperature, oxygen and frozen soil constraint conditions of the high-cold and high-altitude environment are introduced, blasting safety, lumpiness uniformity and the explosive utilization rate are considered at the same time through multi-target collaborative optimization, the method can adapt to the extreme environment, meanwhile, the one-sidedness of single-target optimization is avoided, and the intelligent level of blasting design and implementation is greatly improved.

The invention relates to the technical field of meteorological prediction, in particular to a meteorological prediction precision improvement method and device, equipment, a storage medium and a computer program product. The method comprises the following steps: constructing a plurality of initial weather prediction models based on a Transform structure in combination with a PIDL (Precision Independent Design Language) algorithm; taking the power curve of the wind turbine generator and the performance characteristic curve of the photovoltaic module as constraint conditions, and integrating the constraint conditions into each initial meteorological prediction model to obtain a plurality of basic meteorological prediction models; based on a preset automatic hyper-parameter tuning tool, performing hyper-parameter optimization on each basic meteorological prediction model in combination with a cross validation algorithm; fusing the plurality of basic meteorological prediction models after hyper-parameter optimization by adopting an integrated learning algorithm to obtain a target meteorological prediction model; and according to the meteorological characteristic data and the target meteorological prediction model, the target meteorological prediction result is determined, and the meteorological prediction precision is improved.

The invention discloses a settlement amount time sequence prediction method and system for deep foundation pit excavation adjacent buildings. The method comprises the steps that original monitoring data of on-site building settlement are acquired; performing data preprocessing on the obtained original monitoring data; constructing a recurrent neural network; model input and output parameters are determined through principal component analysis; optimizing the recurrent neural network based on an optimizer; performing hyper-parameter optimization based on an optimization result; performing settlement time sequence prediction by using the optimized recurrent neural network; the system comprises a data acquisition module, a preprocessing module, a model construction module, an analysis module, an optimization module, a parameter optimization module and a prediction module. By constructing the settlement prediction model, dynamic modeling and accurate prediction of the settlement trend of the building in the excavation process of each stage of the foundation pit are realized; historical settlement monitoring data and multi-layer soil body excavation information are combined, and multi-source input parameters are introduced, so that the adaptability of the model to complex working conditions is enhanced.

The invention discloses a construction parameter optimization method and device based on dessert prediction, a medium and equipment, and the method comprises the steps: obtaining a data set of a shale gas well production process, and determining a dessert indication factor; training a network model by taking the data set as input and the dessert indication factor as output to obtain a dessert indication factor prediction model; optimizing the dessert indicator factor prediction model by using a Bayesian hyper-parameter optimization algorithm, and inputting a data set obtained in real time into the optimized dessert indicator factor prediction model to obtain a prediction result; inverting a prediction result into three-dimensional space distribution characteristics of desserts in the shale reservoir; and determining contribution values and partial dependency relationships of the plurality of geological parameters and the plurality of engineering parameters to a prediction result based on the three-dimensional space distribution characteristics, deploying construction parameters according to the contribution values and the partial dependency relationships, and synchronously updating a sweet spot indication factor prediction result to realize spatial dynamic optimization and adjustment of the construction scheme. Through the scheme, the construction parameters can be scientifically optimized.

The invention discloses a space-time carbon emission prediction method, system and device and a medium. The method comprises the steps of obtaining load historical sequence data; carrying out load flow calculation by adopting a first load flow analysis algorithm to obtain a load flow calculation result; based on the load flow calculation result, carbon flow analysis is carried out through power transmission between the nodes, and the carbon emission coefficient of each node is obtained; constructing a time sequence diagram neural network model, and performing preliminary training on the time sequence diagram neural network model; performing hyper-parameter optimization on the preliminarily trained time sequence diagram neural network model by adopting a first hyper-parameter optimization technology to obtain an optimized time sequence diagram neural network model; and inputting load sequence data and unit power output data at the current moment into the optimized sequence diagram neural network model, and carrying out online prediction on a carbon emission coefficient. According to the method, the real-time prediction of the carbon emission coefficient is realized based on the characteristic learning of the historical operation and monitoring data of the power system.

The invention discloses a coal gangue concrete stress response prediction method based on a physical information neural network. The method comprises the following steps: constructing a stress-strain curve database of coal gangue concrete; acquiring damage variables of the coal gangue concrete in a compaction stage, a yield stage and a fracture stage under uniaxial compression; deducing a mesoscopic statistical damage constitutive model of the coal gangue concrete according to the damage variables: constructing a physical information neural network, and embedding the mesoscopic statistical damage constitutive model and boundary conditions into a loss function of the neural network; performing normalization processing on the coal gangue concrete stress-strain curve database to obtain a data set; training a physical information neural network model according to the training set, and performing hyper-parameter optimization according to the verification set to obtain a trained model; and predicting the stress of the coal gangue concrete by using the trained model according to the test set. According to the method, the damage constitutive equation and the deep learning algorithm are fused, so that high-precision and low-data-demand mechanical property prediction is realized.

The invention provides a comprehensive energy consumption prediction method for a tilting type fuel gas aluminum melting furnace based on CPO-ITCN-GRU. The method comprises the steps that original energy consumption data generated in the running process of the tilting type fuel gas aluminum melting furnace and auxiliary characteristic data related to energy consumption are collected; comprehensive energy consumption conversion is carried out on the original energy consumption data, and denoising smoothing is carried out on the comprehensive energy consumption data obtained through conversion and auxiliary feature data related to energy consumption; dividing the denoised and smoothed data into a training set and a test set according to a preset proportion; constructing an ITCN-GRU comprehensive energy consumption prediction model, training the ITCN-GRU comprehensive energy consumption prediction model by using the training set, and performing hyper-parameter optimization by using a crown porcupine optimization algorithm in the training process; and performing performance evaluation on the trained ITCN-GRU comprehensive energy consumption prediction model by using the test set. According to the method, the capturing capability of the model on the local space-time characteristics and accurate hyper-parameter optimization are improved, so that the comprehensive energy consumption prediction accuracy of the tilting type fuel gas aluminum melting furnace is improved.

The invention discloses a hyper-parameter optimization method and system of an icing galloping prediction model based on meta-learning. The method comprises the following steps: collecting original data from meteorological monitoring equipment, a line state sensor and historical icing galloping records; uniformly aligning the collected original data according to time granularity to obtain multi-modal time sequence data; performing internal and external double-loop training on hyper-parameters of the constructed icing galloping time sequence prediction model by adopting the obtained multi-modal time sequence data, performing iterative training on internal loops of the internal and external double-loop training under the given hyper-parameters, and after model parameters converge, obtaining the icing galloping time sequence prediction model. And performing meta-learning-level hyper-parameter optimization on the basis of converged model parameters in the outer loop, and performing adaptive search on hyper-parameters on the premise of fixing an inner loop training normal form to obtain optimal hyper-parameters. According to the method, the key hyper-parameters of the model can be quickly and adaptively optimized under different meteorological scenes and line working conditions, the prediction precision and the convergence speed are improved, and the icing galloping risk of the power transmission line is warned in real time.

The invention belongs to the technical field of wind power prediction, and discloses an ultra-short-term wind power prediction method based on Bayesian optimization XGboost-LSTM, and the method comprises the following specific steps: 1, inputting wind power data collected by a wind power plant and numerical weather prediction data of a corresponding time sequence; 2, data preprocessing, wherein missing value interpolation and data deduplication are carried out on input data; according to the method, an XGBoost feature optimization method is used for selecting key features influencing the wind power, the influence of irrelevant feature noise on the model prediction precision and accuracy is eliminated, then a Bayesian optimization algorithm is used for carrying out hyper-parameter tuning on an LSTM model, and finally an XGBoost-LSTM-BO model is constructed. XGboost feature optimization and Bayesian hyper-parameter optimization can obviously improve the prediction effect of the LSTM on future data and improve the model prediction precision, and compared with a traditional prediction model, the wind power data generalization ability of the model can be improved while the high prediction precision is kept, and higher prediction performance is achieved.

The invention discloses a short-circuit current zero crossing point multi-step prediction method based on a double-breakpoint circuit breaker, and belongs to the technical field of power system phase selection breaking. The method comprises the following steps: S1, data preprocessing: carrying out normalization processing on original short-circuit current data; s2, hybrid model construction: constructing a CNN-SE-GRU hybrid model based on the current data processed in the S1 in combination with a convolutional neural network, an attention mechanism and a GRU network; s3, performing CEO algorithm optimization: performing hyper-parameter optimization on the constructed hybrid model by using a CEO algorithm to obtain a CEO-CNN-SE-GRU hybrid prediction model; s4, model training: training the hybrid prediction model to obtain a stable model; and S5, performing prediction execution: calling the trained hybrid prediction model to realize multi-step prediction of the short-circuit current, and predicting a zero crossing point of the short-circuit current. Compared with the prior art, the short-circuit current zero crossing point multi-step prediction speed and precision are improved, and meanwhile better generalization is achieved.

The invention provides an unmanned aerial vehicle radio frequency signal identification method and a related device, and relates to the technical field of deep learning. Signal data are loaded from an unmanned aerial vehicle radio frequency signal data set, and the data set comprises different unmanned aerial vehicle types, detection distances and signal fragment labels; dividing the signal data into a plurality of sections of continuous sampling point sequences which are not overlapped with one another; adding Gaussian additive white noise with different powers into the signal data, and constructing a training data set comprising a plurality of signal-to-noise ratios; performing short-time Fourier transform on the signal data to generate a time-frequency graph; performing principal component analysis and dimension reduction on the time-frequency graph, and extracting key time-frequency features; performing hyper-parameter optimization on the deep neural network model by adopting a k-fold cross validation strategy; and the optimized deep neural network model is used to classify and identify the time-frequency graph. The technical effect of accurately identifying the radio frequency signal of the unmanned aerial vehicle under different signal-to-noise ratio conditions is realized.

A system for classifying apple leaf diseases using deep learning and feature fusion, consisting of: A data input module, which includes a data storage module, is configured to store a data set created using various data sources, with the data set containing apple leaf images being derived from the data sets "Apple Leaf 9", "Kashmiri Apple Plant Disease" and "Plant Village Apple Leaf"; a data preprocessing module configured to perform preprocessing of the newly prepared dataset of apple leaves, wherein the data preprocessing module is configured to perform data decoding, expansion, resizing, segmentation, scaling and color conversion of the input image data; a feature extraction module that is operationally connected to the data processing module and is configured to receive preprocessed data and transfer the preprocessed data to one or more convolutional neural network models for feature extraction; a feature fusion module configured to combine the extracted features from the Convolutional Neural Networks to develop a fused feature vector; a hyperparameter optimization module configured to optimize the hyperparameters of the feature extraction models by implementing a particle swarm optimization algorithm; a classification module configured to classify the fused trait vector into 13 apple leaf disease classes using a random forest classifier; and a user interface connected to the classification module, configured to display the classification results.

Methods and systems are described herein for minimizing development time in artificial intelligence models by automating model selection based on dataset fittings of time-series data prior to hyperparameter optimization. For example, the system may apply a profiling model using a time-series embedding of the dataset combined with the aggregate statistical profile. In either case, the profiling model may be trained on the scoring policy and / or a time-series embedding of the dataset combined with the aggregate statistical profile to determine a likelihood of the effectiveness of a given model on the given dataset and / or likely hyperparameters for the given model.

The invention provides a lithium ion battery health state lightweight detection method based on a physical information neural network, and the method comprises the steps: collecting the time, voltage, current, temperature and state-of-charge data of a battery in a takeoff and landing stage discharge process, processing the data into takeoff and landing stage discharge time sequence data, and carrying out the detection of the lithium ion battery health state based on the takeoff and landing stage discharge time sequence data. The method comprises the following steps: designing characteristic factors related to battery aging, screening the characteristic factors by utilizing a Pearson's correlation coefficient and a grey relational degree algorithm to obtain optimal characteristic sequence data, inputting the optimal characteristic sequence data into a physical information neural network model constructed by two serially connected neural networks for training, and in the training process, obtaining the optimal characteristic sequence data. And performing hyper-parameter tuning on the two neural networks by adopting a Bayesian optimization algorithm, then performing fine tuning on the second neural network by adopting a hierarchical transfer learning strategy, and finally applying the trained physical information neural network model to battery health state detection. The method improves the quality of feature data, reduces the calculation complexity of features and models, and achieves the accuracy and reliability of the detection of the health state of the battery under the airborne condition.

The invention relates to the technical field of material nondestructive testing, in particular to a CFRP wing skin damage positioning system and method based on multi-modal signal processing and Bayesian optimization DSCN, and the method comprises the steps: carrying out permutation entropy analysis and Higuchi fractal dimension analysis on a signal data set received by a sensor; performing time-frequency feature extraction on the sensor receiving signal data set deviating from the reference; performing damage feature screening on the signal data in the effective time period; training the DSCN structure to obtain an initial CFRP wing skin damage positioning prediction model; performing hyper-parameter optimization on the initial CFRP wing skin damage positioning prediction model according to a Bayesian algorithm; and performing damage positioning prediction on a to-be-detected sample according to the final CFRP wing skin damage positioning prediction model to obtain a corresponding CFRP wing skin damage positioning result. According to the method, effective damage features can be accurately and efficiently extracted from complex signals, and the accuracy and efficiency of CFRP wing skin damage positioning are remarkably improved.

The invention relates to the related field of underwater acoustic signal processing, in particular to an underwater acoustic target radiation noise classification method, which constructs an efficient and accurate underwater acoustic target radiation noise classification model through the technologies of lightweight model selection, feature fusion, transfer learning, hyper-parameter optimization, model pruning, quantification and the like. The model not only can realize real-time processing in an environment with limited resources, but also has good classification performance, low energy consumption and high flexibility, is suitable for various application occasions, and particularly has remarkable advantages in an underwater acoustic target classification and real-time monitoring system.

A train-track-bridge coupling response prediction method based on a sparrow optimization algorithm and a long short-term memory network comprises the steps that data such as train speed, axle load, track vibration acceleration, bridge strain and environment temperature are collected in real time through a multi-source sensor, and a multivariable time series data set is constructed after wavelet denoising and standardized preprocessing; and designing an LSTM network architecture on this basis, introducing an attention mechanism to dynamically allocate feature weights of each time step so as to enhance the ability to capture key signals in the track irregularity mutation and bridge resonance interval, and adopting a sparrow optimization algorithm to globally search an optimal combination of a hidden layer neuron number, a learning rate and a time step length in order to solve the problem of LSTM hyper-parameter optimization. Through the dynamic adaptive step length strategy balance algorithm, the early-stage global exploration and later-stage local development capabilities are balanced, the local convergence defect of a traditional grid search or genetic algorithm is avoided, the calculation efficiency can be remarkably improved, errors can be reduced, and the prediction precision can be improved.

The invention relates to the technical field of deep learning hyper-parameter optimization, in particular to a multi-target hyper-parameter joint optimization method and system for a deep learning model. The specific implementation process comprises the steps of obtaining a current hyper-parameter of a deep learning model, generating a learning track vector, and performing utility prediction to generate a learning utility projection; on the basis of the dominating relationship between the learning utility projection and the current Pareto optimal leading edge, pruning the disadvantage training task, updating the Pareto optimal leading edge by using non-dominating sorting, and constructing a Pareto strategy network; and carrying out topology congestion degree analysis on the updated Pareto optimal leading edge, generating a leading edge exploration bias vector, inputting the leading edge exploration bias vector into the Pareto strategy network to update hyper-parameter codes, and entering the next round of iteration. According to the method, through the multi-objective optimization algorithm and in combination with the dynamically updated Pareto strategy network, the problems that the hyper-parameter optimization process is long in time consumption, low in efficiency and prone to falling into local optimum are effectively solved, and the optimization efficiency and the solving quality are improved.

The invention discloses a copper pipe blank horizontal continuous casting process optimization method based on machine learning assistance, and relates to the technical field of copper and copper alloy pipe blank horizontal continuous casting. The method comprises the steps of S1, data collection, S2, model construction, S3, hyper-parameter optimization, S4, model training and verification, S5, effect evaluation and S6, prediction and optimization of process parameters. A new particle swarm optimization algorithm is used for optimizing a neural network prediction model, the neural network prediction model is applied to the horizontal continuous casting process, the accuracy and the reliability are remarkably improved after the model is optimized, an online rapid calculation system, the particle swarm optimization algorithm and a multi-layer neural network model are combined, crystalline region parameters are accurately predicted and controlled, and the continuous casting quality is improved. Data obtained by an existing rapid calculation system has certain limitation, process parameters obtained based on a production field and crystallization position parameters under a corresponding mold are continuously added into a model training database, and prediction data are more accurate and closer to the current production situation.

The invention discloses a corroded oil and gas pipeline residual strength prediction method considering a physical constraint loss function, and the method comprises the steps: collecting multi-source feature data of a corroded oil and gas pipeline, obtaining a residual strength measured value as a label, and constructing a training data set; an XGBoost regression model is combined with an SHAP interpretability analysis technology, and the influence degree and the influence direction of each feature on the residual intensity are quantified; constructing a neural network model, and determining an optimal architecture of a neural network by adopting a hyper-parameter optimization method; constructing a physical constraint term based on the influence degree and the influence direction of each feature, introducing the physical constraint term into a loss function of a neural network model, and forming a comprehensive loss function together with a data-driven loss term; and training the optimized neural network model by using a comprehensive loss function to obtain a final residual intensity prediction model. The method has the advantages that the prediction precision is improved, the model interpretability is enhanced, overfitting is prevented, and multi-source feature data are effectively integrated.

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 discloses a landslide disaster risk dynamic assessment method and system based on an improvement project progress, and relates to the technical field of natural disaster treatment. According to the invention, through integration of multi-source heterogeneous data, real-time dynamic assessment and a machine learning model, accurate and dynamic assessment of landslide disaster risks is realized, assessment precision and stability are improved, and timeliness and pertinence of decision making are enhanced; a feature disturbance stability mechanism is introduced into the SPY algorithm, and a pseudo-negative sample screening strategy with higher robustness and discriminability is constructed; an information entropy reduction amount is introduced, an acquisition function is improved, the efficiency of hyper-parameter optimization is improved, and a stacking generalization integration model is effectively trained; the influence of the regulation situation progress in the landslide disaster area is considered, the susceptibility correction coefficient is set, the landslide disaster susceptibility evaluation result is corrected, it is ensured that the landslide disaster susceptibility evaluation result can reflect the actual situation in time, and powerful technical support is provided for prevention and management of landslide disasters.

The embodiment of the invention discloses an air conditioner maintenance data classification method and system based on machine learning, and the method comprises the steps: integrating multi-source heterogeneous maintenance records generated in the maintenance process of air conditioner equipment, building a correlation index through a common identification field, and fusing dispersed data into a maintenance data set in a uniform format; performing hierarchical semantic analysis on unstructured texts in the set to generate structured semantic features, and performing time sequence feature extraction on structured data; then constructing a hybrid classification model training framework fusing semantic and time sequence features, and generating a maintenance data classification model through feature space alignment, dynamic weight distribution, hyper-parameter optimization and an early stop strategy; and finally, classifying newly-added maintenance records by applying the model, checking by combining an expert knowledge base, manually rechecking conflict results, and returning corrected data as an incremental sample back to the model to realize continuous optimization.

The invention relates to the technical field of water quality prediction, in particular to a river water quality prediction method and system based on a machine learning coupling hydrological model, and the method comprises the following steps: obtaining a data set, and dividing the data set into a training set and a test set; constructing a limit gradient lifting model, and performing hyper-parameter optimization on the model; performing correlation analysis on the water quality parameter set and the water quality index WQI value based on the trained limit gradient lifting model, and screening to obtain key water quality parameters influencing the water quality index WQI value; constructing an LSTM model and a soil and water evaluation tool model; simulating a future hydrological water quality process based on the soil and water evaluation tool model, and calculating and outputting a future water quality parameter result; and inputting a future water quality parameter result into the trained LSTM-WQI model to predict a future river WQI value so as to obtain a prediction result. According to the invention, the machine learning algorithm is coupled with the hydrological model to construct the water quality evaluation model which is convenient to use and adapts to local conditions, and efficient and accurate prediction of future water quality is realized.

The invention relates to the field of computers, in particular to an NIWPSO + CNN + LSTM + Attention-based power load prediction method, which comprises the following steps of: acquiring power data, and performing preprocessing and standardization operation on the power data; the preprocessed data are used as input, and a CNN-LSTM-Attention model is constructed; carrying out hyper-parameter optimization on the CNN-LSTM-Attention model by utilizing an NIWPSO (Non-International World Weighted Particle Swarm Optimization) algorithm; setting a residual error monitoring and dynamic updating mechanism, monitoring the residual error in real time during model prediction, and automatically triggering a retraining process once the residual error exceeds a preset threshold value; a nonlinear variation inertia weight strategy is innovatively adopted, dynamic adjustment is carried out according to an algorithm iteration process and a particle search state, a PSO algorithm is prevented from being caught in a locally optimal solution too early, meanwhile, a mutation thought of a genetic algorithm is used for reference, mutation operation is carried out on particles in a one-dimensional mode, the algorithm is assisted to efficiently obtain a globally optimal solution in a complex solution space, and model parameter optimization is guaranteed.