7829 results about "Test set" patented technology

The invention discloses a maritime accident prediction method and device based on interpretable integrated machine learning, and relates to the technical field of maritime affair safety risk analysis, and the method comprises the steps: obtaining accident investigation data, carrying out the preprocessing, balancing the data through a ten-fold layered oversampling method, and carrying out the cross verification training, and determining a performance optimal model by using the test set and carrying out interpretable analysis to explain the influence of the characteristics on the accident prediction result. By constructing a closed-loop'data processing-model optimization-explanation output 'process and adopting SMOTE oversampling and ten-fold layered cross validation training and a heterogeneous base model ensemble learning strategy, the processing capacity of the data imbalance problem of accident categories is improved, the data leakage problem of oversampling is avoided, and the possible bias of a single model is overcome. The interpretability analysis of the model prediction result can quantitatively display the contribution degree of each feature to prediction globally and locally, reveal the nonlinear relationship and interaction effect between the features, and provide transparent interpretation of model decision.

The invention discloses a multi-scale and mixed attention high-robustness motor imagery recognition method, which comprises the following steps: S1, acquiring motor imagery electroencephalogram signals, preprocessing the motor imagery electroencephalogram signals, dividing a training set and a test set, segmenting the training set, recombining the training set and expanding a training data set; s2, multi-scale feature extraction is conducted on the motor imagery electroencephalogram signals through a multi-scale convolution embedding module, and time dynamic and space cooperation features of different frequency bands are captured; s3, inputting the multi-scale features into LG-KAT, and respectively modeling a local fine-grained feature and a global time sequence dependency relationship through a local attention branch and a global attention branch; s4, features output by LG-KAT and low-layer embedded features are fused and flattened, a classification layer based on GR-KAN is input for nonlinear transformation and category mapping, model parameters are trained and optimized, and motor imagery task classification is achieved. The invention further discloses a multi-scale and mixed attention high-robustness motor imagery recognition system.

The invention discloses a viscous coarse-grained soil permeability coefficient prediction method based on a physical constraint neural network, and the method comprises the following steps: carrying out an indoor viscous coarse-grained soil seepage test, and establishing a viscous coarse-grained soil permeability coefficient formula considering porosity and grain composition characteristics, further constructing a mixed model containing a physical driving item and a neural network data driving item, forming a complete data set through a numerical simulation technology and literature investigation on the basis of a seepage test, complementarily collecting porosity, grain composition characteristics and corresponding permeability coefficient data of the viscous coarse-grained soil sample, and dividing the complete data set into a training set and a test set; according to the method, optimal hyper-parameters are dynamically searched in combination with Bayesian optimization for model training, a loss function curve and permeability coefficients of the viscous coarse-grained soil under different porosity and grading characteristics are obtained, tests show that high-precision prediction of the permeability coefficients of the viscous coarse-grained soil is achieved, and the problems that a traditional method is insufficient in physical constraint and low in prediction precision are solved.

The invention provides an underground water pollutant concentration prediction method and system based on machine learning, and relates to the technical field of underground water pollutant concentration prediction.The method comprises the steps that historical data, hydrogeological parameters, meteorological data, human activity data and geochemical parameters of underground water pollutant concentration of a target area are preprocessed; dividing a training set, a verification set and a test set; constructing a preset resolution feature set based on a geochemical mechanism; selecting an adaptive machine learning model according to data characteristics and coupling a physical mechanism; performing hyper-parameter tuning by adopting Bayesian optimization, and supplementing small sample data in combination with transfer learning to complete model training; predicting the underground water pollutant concentration of the target area by using the trained model, and outputting a pollutant concentration prediction result with an uncertainty interval; the invention provides a technical scheme for predicting the concentration of underground water pollutants, which is efficient, accurate and high in adaptability.

The invention belongs to the technical field of bearing fault diagnosis, and discloses a CNN-MFKAN-based bearing fault diagnosis method and system, and the method comprises the steps: obtaining a bearing fault signal, dividing data through a sliding window, and generating a two-dimensional time-frequency image through continuous wavelet transform, and storing the two-dimensional time-frequency image; constructing a bearing fault diagnosis model in combination with CNN and MFKAN; dividing the data into a training set, a verification set and a test set; inputting the training set into a bearing fault diagnosis model for training; optimizing the model parameters and judging whether convergence occurs or not, if not, returning to the training set, and if yes, completing training and storing the optimal model parameters; and calling the optimal model parameter to carry out bearing fault judgment to obtain a fault classification result. According to the bearing fault diagnosis model, the MFKAN module is innovatively designed, the extraction capability of the model for different frequencies and different scale features is effectively enhanced, and the recognition precision and robustness of bearing faults under complex working conditions are remarkably improved.

The invention belongs to the technical field of intelligent manufacturing, and particularly relates to a manufacturing quality prediction method and system based on a multi-mode sequential network and application, and the method comprises the steps: carrying out the preprocessing of the sequential data of a process manufacturing production line, obtaining a sample set, and carrying out the sequential division into a training set, a verification set and a test set; on the basis of the sample set, key features are enhanced through a frequency domain enhanced channel attention mechanism, a multi-period mode of a time sequence dependence and period sensing module is captured in combination with a multi-layer expansion convolutional network structure, and a multi-mode time sequence network model is constructed; and sequentially carrying out training set training, verification set parameter adjustment optimization and test set performance verification on the multi-modal sequential network model, and outputting a prediction result. According to the method, the deep dynamic association among the multivariable time series data can be mined, the accuracy and robustness of manufacturing quality prediction are improved, and an efficient and reliable technical scheme and an implementation path are provided for process industry quality control and intelligent optimization.

A system for accidentology-based measuring of accident risk-indexing score values for a motor vehicle to be tested providing a measured accident probability value for an occurrence of an accident event having a physical impact to the tested motor vehicle. The system includes a driving scenario module, a test setting module, and a scoring module. The driving scenario module is configured for determining various driving scenarios for the motor vehicle by defining a set of measurable scenario characteristics, which include at least one ADAS variable. The test setting module is configured for determining a test setting in form of a multi-dimensional test matrix, which includes testing protocols for each of the measurable scenario characteristics for providing measured values of measurable scenario characteristics of the various driving scenarios. The scoring module is configured for generating the risk-indexing score by receiving the multi-dimensional test result signal and a historical data information signal.

The invention discloses an abdominal multi-organ CT image automatic segmentation method based on deep learning. The method comprises the following steps: establishing a training sample set; constructing an improved encoder; an improved decoder is constructed; a PCE-TransUNet segmentation network model is established, and the PCE-TransUNet segmentation network model is Training the PCE-TransUNet segmentation network model by using the training set, and optimizing by using a joint loss function of cross entropy loss and Dice loss to obtain a trained PCE-TransUNet model; and inputting the test set into the trained PCE-TransUNet model, and outputting a segmented image by the PCE-TransUNet model. According to the method, partial convolution and an efficient channel attention mechanism are introduced, the ability of the model to extract image details is enhanced, the problem that feature extraction is insufficient in a traditional method is solved, and especially when small organs and complex boundaries are processed, the segmentation precision is remarkably improved.

The invention relates to the cross technical field of computer vision and civil engineering, and discloses a deep learning-based bridge disease image segmentation method, which comprises the following steps of: establishing an image data set containing crack and spalling diseases and performing online enhancement; constructing a segmentation network model comprising a frequency dynamic convolution encoder branch, an edge enhancement Transform encoder branch, a gating cooperation unit, a decoder and a depth supervision module; training the model by using a weighted mixed loss function; and inputting the test set to obtain a final segmentation mask. Self-adaptive fusion of local texture features and global context information is realized through a dual-encoder architecture and a gating cooperation mechanism; a frequency dynamic convolution and edge enhancement module is utilized to enhance the anti-noise capability and micro-disease perception under a complex background; and in combination with a category weighting strategy, the problem of pixel category imbalance is effectively solved, and high-precision automatic segmentation of concrete bridge diseases is realized.

The invention discloses an island reef shoreline remote sensing extraction method based on a double-branch network, and belongs to the technical field of island reef shoreline remote sensing extraction. Comprising the following steps: S1, acquiring an original satellite remote sensing image of a coral island reef, and cutting to obtain a reef remote sensing image; s2, a shoreline vector label is drawn in the island remote sensing image, and rasterization processing is carried out; s3, cutting the rasterized label and the research area image to obtain tile data; s4, dividing the tile data into a training set and a test set; s5, performing data enhancement on the training set tile data to obtain a processed image data set; and step S6, constructing an SDSPNet model based on the image data set. According to the method, the overall semantic expression ability of the broken shoreline and the lagoon face area is enhanced, and the problems of erroneous judgment of the broken shoreline of the coral island and fuzzy lagoon face boundary in an existing method are solved.

The invention discloses a re-parameterization unmanned aerial vehicle target detection method based on multi-core fusion and omnidirectional connection, and the method comprises the following steps: obtaining an unmanned aerial vehicle aerial image data set, and constructing a detection model comprising a backbone network, a feature fusion network and a deformable task decoupling detection head; the backbone network extracts multi-scale and multi-direction features by six parallel paths in a training stage through a wide-branch re-parameterization convolution module, and re-parameterization is carried out in a reasoning stage to obtain single-path convolution; the feature fusion network performs down-sampling through a spatial deep convolution module and reduces spatial information loss, and realizes cross-stage omnidirectional feature interaction and double attention enhancement in combination with an omnidirectional kernel cross-stage partial connection module; and the deformable task decoupling detection head decouples the classification and regression features, optimizes feature expression, weights the classification features and then performs aggregation decoding. The model is trained to be used for a test set to output a detection result, detection precision and reasoning efficiency can be balanced, and the model adapts to a complex aerial photography scene of an unmanned aerial vehicle.

The invention discloses a 3D printing defect detection method based on improved YOLOv10n, and the method specifically comprises the steps: constructing a data set, and dividing the data set into a training set and a test set; processing the training set data; the method comprises the following steps of: constructing a DM-YOLO network model by taking YOLOv10n as a basic algorithm; the DM-YOLO network model comprises a trunk feature extraction network, a neck feature fusion network and a detection head network; training the network model by using the training set data; and inputting test set data into the trained DM-YOLO detection model, outputting category information of defect positions in the FDM printed piece, and obtaining a defect detection result. The network is deeply optimized on the basis of YOLOv10n, DSMCAttn, a DySample dynamic up-sampler and a lightweight single-head self-attention mechanism are integrated, and the detection precision and reasoning efficiency of small target defects are remarkably improved.

The invention discloses a multi-target tracking method combining camera motion compensation and pseudo depth estimation, belongs to the field of computer vision, and is suitable for a complex automatic driving road environment. The method comprises the following steps: constructing a training set and a test set; detecting the image by using a deep learning detector and extracting features; a Kalman filter is adopted to correct a motion modeling state vector, and the target position and size prediction precision is improved; solving a homography matrix through feature point matching, performing global camera motion compensation, and reducing camera jitter and displacement interference; target pseudo depth information is calculated, hierarchical cascade matching is carried out, and association performance in dense and shielding scenes is optimized; a three-level cascade strategy is adopted to complete high confidence degree, low confidence degree and residual target matching in sequence; and finally, outputting a tracking result with a detection frame and identity information to obtain a trained model. According to the invention, accurate detection and stable tracking of multi-category targets can be realized in a complex environment, and identity switching is effectively reduced.

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 short-term power load prediction method, system and device based on multi-intelligent-model fusion and a medium, and belongs to the technical field of short-term power load prediction, and the method comprises the steps: obtaining regional historical load data and meteorological data; performing data cleaning on the obtained load data and meteorological data; measuring linear and nonlinear correlation between the power load and the meteorological factors, and screening meteorological data with high load correlation; decomposing the load data into a time sequence by using an empirical mode decomposition method based on combination of multi-scale permutation entropy to obtain a multi-scale sub-data sequence; respectively predicting the multi-scale sub-data sequences to obtain prediction results; carrying out weighted fusion on the prediction result through a long short-term memory network model to obtain a load prediction result, and optimizing model parameters to obtain a trained multi-model prediction model; and predicting the test set data by using the trained model to obtain a final load prediction result. According to the invention, the precision and adaptability of load prediction are effectively improved.

The invention discloses a multi-source heterogeneous anomaly detection method based on time correlation, and belongs to the technical field of water diversion engineering, and the method comprises the steps: S1, obtaining multi-source sensor time sequence data in multi-source heterogeneous data, carrying out the preprocessing of the multi-source sensor time sequence data, and dividing a training set and a test set; s2, constructing a double-branch depth feature extraction network model, training by adopting the training set, and testing through the test set to obtain a trained double-branch depth feature extraction network model; the double-branch depth feature extraction network model comprises a double-branch unit, an attention feature fusion unit, a classifier unit and an output layer unit which are connected in sequence; and S3, inputting to-be-detected data into the trained double-branch depth feature extraction network model, and finally outputting an anomaly diagnosis result. A double-branch depth feature extraction network is constructed, adaptive fusion is realized through an attention mechanism, and the problem of poor modal adaptability of heterogeneous data is solved.

The invention provides a multi-source data fused SAR sea surface significant wave height deep learning inversion method, and belongs to the technical field of remote sensing ocean, and the method specifically comprises the steps: preprocessing buoy data, and obtaining sea wave significant wave height data observed by a buoy; acquiring dual-polarization single-view complex SAR data collected in an interference wide-width mode; preprocessing the SAR data, and taking significant wave height data obtained by buoy observation as a label of the SAR data; acquiring auxiliary data corresponding to the SAR data, wherein the auxiliary data comprises wind speed, wind direction data, rainfall data and OCN data; and constructing a multi-source data-fused SAR significant wave high-depth learning inversion model, training the multi-source data-fused SAR significant wave high-depth learning inversion model, and testing and verifying the trained model by using the test set and the verification set to obtain a final significant wave height inversion result. According to the method, the SAR data inversion capability of the model is improved.

Disclosed in the present invention is an artificial-intelligence-based performance prediction processing method for a carbon-fiber carbonization process. The method comprises: preprocessing experimental data under test, so as to obtain said experimental data that has been subjected to data cleaning; then, using a sliding window processing method to slide on time series data, extracting data within a window at each position and using the extracted data as an input sample, and determining an input feature and an output variable feature of each input sample, so as to convert the time series data into a plurality of experimental data samples under test in the format of a target model input; performing random data set division on said plurality of experimental data samples, so as to obtain some training sets and some test sets; and constructing a target model, and inputting said experimental data samples into the target model. The target model can implement a relatively accurate mechanical-performance prediction for a carbon-fiber-precursor carbonization process, and the model has an optimal performance in all aspects and has a relatively good generalization capability.

The invention provides a reinforcement corrosion electrochemical parameter inversion method based on LSTM (Long Short Term Memory) time sequence prediction, which comprises the following steps: S1, acquiring electrochemical time sequence data in a reinforcement corrosion process through an electrochemical workstation to form an original reinforcement corrosion electrochemical time sequence data set; s2, preprocessing is carried out to obtain a training set, a verification set, a test set and normalization coefficients of all parameters; s3, constructing and training an LSTM time sequence prediction model; s4, constructing and calibrating a steel bar corrosion electrochemical parameter forward modeling model; and S5, constructing an inversion framework fusing a particle swarm optimization algorithm, a simulated annealing algorithm and an Adam optimization algorithm, forming closed-loop cooperation by the particle swarm optimization algorithm, the simulated annealing algorithm and the Adam optimization algorithm so as to minimize an error between a target electrochemical response parameter and a theoretical electrochemical response parameter, and outputting an inversion result. According to the method, through organic combination of time sequence prediction and multi-algorithm cooperation, the problems that a traditional inversion method is low in precision and poor in stability are solved, and a reliable technical means is provided for reinforced concrete structure health monitoring.

The invention provides a hybrid CNN-Transform colon polyp image segmentation method combining edge guidance and a double attention mechanism, and is applied to the technical field of medical data processing. According to the method, a CNN encoder is adopted to extract multi-scale local features, a Transform encoder is adopted to extract global context, an edge probability graph is generated by introducing an edge guide branch based on shallow layer features, a gating coefficient is calculated according to the edge probability graph and / or statistics obtained by intermediate prediction, weighted fusion is performed on the local and global features, and the local feature and the global feature are integrated. And the decoder performs up-sampling step by step and outputs a segmentation result. Compared with the prior art, the method has the advantages in boundary integrity and small target detection. Experiments show that on a Kvasair-SEG data set, the optimal Dice of a verification set of the scheme is 0.892, the optimal HD95 of the verification set of the scheme is 12.2, the Dice of a test set of the scheme is 0.89, and the optimal HD95 of the test set of the scheme of the scheme is 12.3.

The invention discloses a multi-modal remote sensing visual positioning method and device based on scene knowledge enhancement and a medium, and relates to the technical field of remote sensing visual positioning. The method comprises the following steps: firstly, preprocessing a plurality of remote sensing images, generating scene knowledge enhanced text description, forming a visual positioning data set, and dividing the visual positioning data set into a training set, a verification set and a test set; constructing a visual positioning model, and obtaining an optimal model through training, verification and testing; inputting a to-be-queried text to obtain remote sensing image coordinates. According to the method, cross-modal fusion of knowledge enhancement is realized, scene knowledge is embedded into a visual positioning framework, and the problem of inference of implicit semantics in a remote sensing scene is solved; multi-scale image features and scene knowledge are fused layer by layer through multi-round cross-modal attention iteration, and semantic understanding from coarse granularity to fine granularity is achieved; a similarity threshold is introduced to screen a high-correlation image region, and background interference is reduced in combination with loss constraints. The LLaMA2 is subjected to efficient fine tuning in combination with the LoRA technology, end-to-end coordinate generation is supported, and both performance and calculation efficiency are considered.

The invention relates to the technical field of image target detection, and discloses an unmanned aerial vehicle aerial image small target detection model construction method comprising the following steps: preparing an aerial image data set, preprocessing the aerial image data set, and generating an aerial image sample set; the method comprises the following steps: establishing a basic model on the basis of a YOLOv8 network model, removing a P5 detection layer in a head network Head of the basic model, introducing a P2 detection layer, replacing a specified position of a Conv module in a backbone network Backbone by adopting an ACMConv feature enhancement module, replacing a specified position of a C2f module in a neck network Neck by adopting a C2fMixStructure mixed structure module, and constructing an improved model by adopting a lightweight enhanced detection head structure; and dividing the aerial image sample set into a training set, a verification set and a test set in proportion, training and verifying the improved model, and generating an unmanned aerial vehicle aerial image small target detection model based on AMLP-YOLOv8. According to the invention, the method has higher perception capability when extracting fine target features, and improves the stability and robustness of small target detection of the aerial image of the unmanned aerial vehicle.

The invention discloses a power system time domain simulation method based on a physical information depth operator network model. The method comprises the following steps: initializing an MATLAB environment; calling actual example data; setting a fault type and a scene; performing time domain simulation by using a PST tool box; recording the data of the simulation curve and the state variable; preprocessing the data; generating and storing a training sample; initializing parameters of the depth operator network model; calling a training set and a test set; training parameters are set; determining key parameters; calculating a loss function; storing model parameters; and setting the feasibility and the actual effect of the fault scene verification model. By applying a DeepONet innovative algorithm, the calculation time of time domain simulation of the power system is greatly shortened, the efficiency of the method is far higher than that of a traditional numerical integration method, and the method can meet application scenes with extremely high real-time requirements; good generalization ability is achieved, and stable performance can be kept in different fault scenes; by embedding the physical constraint, the physical interpretability and reliability of the simulation result are improved.

The invention relates to the technical field of image target detection, and provides a target lightweight detection method and system based on attention feature enhancement, and the method comprises the steps: collecting visible light target image data; constructing a target lightweight detection model: designing a lightweight backbone network and a special attention mechanism; designing an adaptive feature fusion module; designing a detection head; designing a loss function; designing a model lightweight strategy; and training the target lightweight detection model through the training set, inputting the test set into the trained target lightweight detection model after training is completed, and outputting a target lightweight detection result. According to the scheme of the invention, the method focuses on the design of an efficient lightweight network architecture, and through the introduction of a special attention mechanism, dynamic feature fusion and a model compression strategy, the detection precision is ensured, meanwhile, the demand for computing resources is remarkably reduced, and the real-time detection of a visible light image target is realized.

The invention discloses an active learning and physical loss-based fatigue crack growth rate prediction method, which comprises the following steps of: splicing and fusing a preprocessed stress intensity factor, a stress ratio, pre-strain and stress amplitude, and taking the fused characteristics as the input of a model; embedding the Jones model into a loss function of the neural network, constructing a physical information time sequence model based on the Jones model, setting a parameter set, selecting a most valuable training sample from a training set by using active learning, retraining the model by using the selected most valuable training sample, selecting an optimal model, and evaluating the optimal model by using a test set; fusing the actually measured stress intensity factor, stress ratio, pre-strain and stress amplitude of the to-be-predicted material, and inputting into the obtained prediction model to obtain a predicted value of the fatigue crack growth rate. By adopting the technical scheme of the invention, the fatigue crack growth rate of the material in the whole life period can be efficiently and accurately predicted at low cost.

The invention discloses a high-efficiency time sequence optical flow method, system and device for fusing event and image information and a medium. The method comprises the following steps: constructing a time sequence feature extraction module; constructing a double-branch collaborative iteration optical flow module of the event and the image; constructing a fusion modeling and iterative optimization optical flow module based on cross attention; training a dual-branch collaborative iteration optical flow module of the event and the image and a fusion modeling and iterative optimization optical flow module based on cross attention on a training set of an MVSEC public data set to obtain weights, and testing on a test set of the MVSEC public data set to obtain an optical flow calculation result; the system, the equipment and the medium are used for implementing the method. The method has the advantages of high time resolution, high dynamic modeling capability, fine multi-modal fusion, high estimation precision and the like, the motion sensing performance in a complex scene is remarkably improved, and powerful technical support is provided for an intelligent visual system.

The invention discloses a tunnel or mine water gushing space-time prediction method and system coupled with a hydrodynamic numerical model, and the method comprises the steps: outputting multi-source data based on an identified and verified underground water numerical model, complementing the missing of measured data, quantifying the difference between the permeability characteristics of a fault and a normal stratum, and coupling the difference to a data system, and tunnel or mine excavation space data are merged. And constructing an LSTM-isolated forest-K neighbor regression coupling model, and configuring a multifunctional module to realize multi-scene data co-training. The preprocessed multivariate time series data is divided into a training set and a test set, hidden features are extracted through a coupling model, anomaly detection results are fused, a residual error correction model is synchronously trained, and hyper-parameters and weights are adaptively optimized according to multi-engineering prediction error feedback. And based on the trained coupling model, carrying out synchronous water gushing space-time prediction by adopting a window rolling strategy, and outputting prediction data meeting engineering precision in combination with residual correction. And reliable technical support is provided for safety prevention and control of engineering construction.

A green ammonia production hydrogen load prediction method based on sparse attention variational Bayes comprises the following steps: acquiring historical data of a green ammonia electrolysis hydrogen production industry through an acquisition sensor, and dividing the historical data into a training set, a verification set and a test set; performing standardization processing on the training set, and constructing an SVAE soft measurement training model; determining a final objective function suitable for SVAE; and performing real-time online prediction on the output target variable by using the current input characteristic variable by adopting the trained SVAE. The SVAE combines CNNs, PSSAM and a cross attention mechanism, and captures a long-term dependency relationship and a complex mutual relationship in industrial data. An optimized objective function is formulated in combination with variational reasoning and a Monte Carlo method and is used for offline training. The SVAE has remarkable advantages in the process of accurately predicting the hydrogen flow and optimizing the production process of the green ammonia, the production efficiency is improved, and the energy consumption is reduced.

The invention discloses a photovoltaic power interval prediction method based on a GRU-LSTM combined neural network, and belongs to the technical field of photovoltaic power interval prediction. The method comprises the following steps: S1, taking historical power generation data as original wind-solar power generation power prediction data, processing the data, and screening related meteorological characteristics by adopting a Pearson correlation coefficient; s2, a GRU-LSTM combination model is constructed, and related hyper-parameters are set; s3, taking the screened related meteorological features as input for training, calculating a photovoltaic point prediction result according to a weight coefficient, and performing related error evaluation; and S4, based on the photovoltaic power point prediction result, calculating a photovoltaic power interval prediction result by using a quantile regression technology, and detecting performance evaluation through a test set. According to the method, the minimum prediction error correlation index is taken as the target, the influence of different weathers on photovoltaic power processing is considered, the Pearson's correlation coefficient analysis is utilized to select more representative meteorological characteristics, and the combined model and the quantile regression technology are utilized to finally obtain the photovoltaic power interval prediction result.

The invention discloses a causal perception sentiment analysis method and system based on thinking chain reasoning, and belongs to the technical field of computer vision. The method comprises the following steps: acquiring and reading a multi-modal sentiment analysis data set; extracting video features from the video data in the multi-modal sentiment analysis data set, including voice, text and visual modal features; using the training set and the test set to train and verify the causal perception emotion polarity alignment model; inputting the test set into the trained causal perception emotion polarity alignment model to obtain an emotion state prediction result; video features are input into a causal perception emotion polarity alignment model, and emotion clues are extracted through thinking chain prompt and a self-supervision verification mechanism; then performing causal intervention and anti-factual reasoning on each modal feature by using an emotion clue to obtain a causal-related single-modal feature; and finally, obtaining joint feature representation from the causal-related single-mode features through cross-mode interaction by using a multi-mode representation learning method, and predicting an emotional state.