52 results about "Restricted Boltzmann machine" patented technology

The invention discloses a networking charging data cross-platform fusion and transmission method based on an intelligent scheduling algorithm. The method comprises the following steps: S1, collecting and preprocessing networking platform charging data; s2, feature similarity is calculated, and an undirected graph with samples as nodes and similarity as edge weight is constructed; s3, calculating a standardized Laplacian matrix, extracting feature vectors, and clustering to generate feature cluster tags; s4, constructing a modal mapping matrix, mapping the feature vectors, and inputting the mapped feature vectors into a first convolution restricted Boltzmann machine to extract fusion features; s5, inputting the fusion features into a second convolution restricted Boltzmann machine, and combining graph structure sorting and constructing a weighted directed graph; s6, initializing a virtual scheduling factor in the graph, and searching an optimal transmission path by an ant colony algorithm; and S7, distributing fusion features according to paths, and realizing cross-platform efficient transmission of charging data. According to the method, spectral clustering, modal mapping, convolution extraction and intelligent scheduling are fused, and the multi-platform charging data fusion accuracy and transmission efficiency are improved.

The invention discloses a distributed photovoltaic power prediction method and system, belongs to the technical field of renewable energy prediction, and solves the prediction precision and efficiency bottlenecks of a traditional model under complex meteorological conditions through fusion of deep feature learning and an adaptive optimization mechanism. Firstly, a time sequence sample set is constructed based on a sliding window mechanism, and non-stationary fluctuation characteristics of a power sequence are dynamically captured; eliminating the dimensional difference between the input features and the tags through minimum-maximum normalization; constructing a four-level restricted Boltzmann machine stacking structure, and extracting time-space coupling characteristics of power data by layer-by-layer unsupervised pre-training; and designing an improved extreme learning machine dynamic analysis architecture, optimizing the number of neurons in a hidden layer in combination with grid search, and realizing global optimal balance between model complexity and prediction precision. According to the method, an effective dynamic mode and noise interference are distinguished by using hierarchical feature abstraction capability of the RBM, prediction robustness in strong fluctuation scenes such as cloudy and rainy scenes is remarkably improved through analytical solution and parameter adaptive adjustment of the ELM, and efficient technical support is provided for intelligent scheduling and energy storage optimization in a high-proportion photovoltaic grid-connected background.

A method in an illustrative embodiment comprises configuring a machine learning system with a multi-layer network architecture comprising at least one neural network and one or more additional network layers, training the neural network at least in part utilizing quantum sampling performed by a quantum computing device, obtaining data characterizing a monitored system, processing at least a portion of the obtained data through at least a portion of the multi-layer network architecture of the machine learning system to generate a prediction of at least one characteristic of the monitored system from the obtained data, and executing at least one automated action relating to the monitored system based at least in part on the generated prediction. The neural network may comprise, for example, a deep belief network (DBN) that includes at least first and second restricted Boltzmann machines (RBMs) of respective first and second different types, or at least one conditional restricted Boltzmann machine (CRBM).

The invention relates to a cross-modal medical data hash retrieval method assisted by a large language model, and belongs to the field of artificial intelligence information retrieval in an intelligent medical system. Designing an image description type prompt template, a diagnosis report summary type prompt template and a combined modal reasoning type prompt template in combination with the semantic generalization ability of the large language model to perform modal data enhancement, and realizing deep semantic mining of medical image and diagnosis report data; a structured coding layer is utilized to ensure that the features of the image and the text can be accurately matched in a unified embedding space; finely adjusting the large language model by adopting a mode of combining soft prompt and hard prompt to realize alignment of image and text features; a Gaussian binary restricted Boltzmann machine is introduced to carry out probabilistic Hash mapping, data structure information is effectively reserved, and Hash codes are generated to carry out cross-modal Hash retrieval. Semantic similarity retrieval of medical cases among different modes can be realized, high retrieval efficiency is realized, and accurate and personalized diagnosis and treatment schemes are provided for patients.

Methods, systems, and apparatus for a bi-directional quantum annealing approach to Markov random field networks for machine learning in image analysis. In one aspect, a method includes obtaining training data comprising features extracted from a first set of images; training a deep quantum restricted Boltzmann machine (QRBM) comprising multiple layers using the training data, the training comprising layer-wise training of the multiple layers, wherein training each layer of the multiple layers comprises evaluating a restricted Boltzmann machine (RBM) probability distribution using bi-directional quantum annealing; and validating the trained deep QRBM using test data comprising features extracted from a second set of images.

This invention discloses a bearing remaining life prediction method based on BN-RBM and DA-BiLSTM. First, raw vibration signals from the bearing during operation are collected and preprocessed. Then, a bearing remaining life prediction model is constructed. This model includes a BN-RBM feature extraction network, a DA-BiLSTM feature fusion network, and a linear regression layer. The BN-RBM feature extraction network dynamically updates the prior weight distribution of the Restricted Boltzmann Machine (RBM) using posterior probabilities constructed from Bayesian BN, and extracts features using the trained RBM. The DA-BiLSTM feature fusion network performs bidirectional feature fusion, and the linear regression layer performs linear activation, outputting the predicted bearing remaining life. This invention effectively improves the accuracy of bearing remaining life prediction by providing timely and effective life prediction before failure, reducing the risk of downtime caused by failures.

The invention provides a dynamic modeling simulation method and system for the energy efficiency ratio of a solar seawater desalination system, and belongs to the technical field of seawater desalination and system modelling. Membrane surface resistance, selective permeability and direct-current bus voltage ripple data in the photovoltaic electrodialysis process are firstly obtained; secondly, Fourier transform is carried out on ripple data, then the ripple data and membrane parameters are spliced to generate an input matrix, the matrix is imported into a deep belief network, and a restricted Boltzmann machine is used for extracting an unsteady-state ion impedance vector reflecting the influence of voltage fluctuation; a nonlinear regression model of the vector and unit water production energy consumption is established through a least square support vector machine; finally, the water production rate and the energy efficiency ratio are calculated according to the predicted energy consumption and the photovoltaic power, and a dynamic simulation curve is generated. According to the method, the nonlinear influence of the photovoltaic voltage ripples on the membrane impedance can be quantified through deep learning, and the precision of predicting the energy efficiency ratio of the seawater desalination system under the fluctuating power supply working condition is remarkably improved.

The invention discloses a method for optically realizing a restricted Boltzmann machine. According to the invention, analog calculation of the restricted Boltzmann machine and optical Gibbs sampling can be realized, and the system has the advantages of wide application range, fast information transmission, simple structure, low cost, fast calculation and the like. According to the invention, a coherent wide-spectrum light source is used as signal input, a light field is subjected to light splitting by using a light splitting device such as a grating and then enters a modulator, then spinning, interaction and magnetic field parameters are coded on light wavefront at different positions through a time or space light modulator, and optical Fourier transform is carried out by using a time or space lens system, so that the optical field is obtained. The light intensity after Fourier transform is measured through the detector, and finally the difference between the light intensity measured twice is calculated, so that optical Gibbs sampling is realized, the calculation complexity is reduced, and the calculation efficiency is improved. The method provided by the invention has important application prospects in the fields of optical neural networks and the like, can realize applications of content generation, classification and identification and the like, and is convenient to integrate in optical chips and the like.

The present invention belongs to the field of air quality detection application technology, and in particular, relates to an environment and rating system based on big data analysis, including obtaining air quality levels, air quality evaluation parameter indicators and corresponding sky image data sets, and standardizing them. Based on the improved principal component analysis algorithm, feature extraction is performed on the air quality evaluation parameter indicators, a stacked noise reduction autoencoder is used to learn the evaluation parameter indicator characteristics and sky image characteristics of air quality samples, and a restricted Boltzmann machine is used to analyze and model the relationship between various features to obtain fused features, and a multi-feature fused air quality rating LSTM model is established based on the fused features to quickly and efficiently analyze and rate air quality in real time. The method proposed in the present invention fully takes into account the influence of various influencing factors on the air quality level, and integrates the sky image features to rate the air quality in real time, reliably and efficiently.

The invention provides an audio-visual multi-mode speech recognition method, a model training method and electronic equipment. The voice recognition method comprises the following steps: acquiring first video data and first audio data; a target model for audio-visual multi-modal speech recognition is obtained, the target model comprising a first target network, in which the first target network introduces a multi-scale packet sparse constraint comprising a plurality of norm constraints and a plurality of packets on the basis of a deep belief network (DBN), and the multi-scale packet sparse constraint comprises a plurality of norm constraints and a plurality of packets; the first target network divides hidden layer units of a restricted Boltzmann machine (RBM) into non-overlapping groups and performs feature extraction of the first voice by using a non-overlapping group lasso method; and inputting the first video data and the first audio data into a target model to obtain a voice recognition result of the first voice output by the target model. By introducing a multi-scale grouping sparse constraint and multi-head attention fusion mechanism, the accuracy and robustness of speech recognition in a complex scene are improved.

The invention discloses a coal mine catastrophe scene deduction method, which belongs to mine disaster emergency rescue, and comprises the following steps: collecting environmental data of a coal mine stope; performing data screening according to the environment data and the key elements, and extracting time sequence features; deducing time sequence characteristics by using an energy-based dynamic time sequence model to obtain probability energy distribution of disaster development; according to the probability energy distribution, constructing a Boltzmann machine model with a limited time sequence for predicting a mine catastrophe development trend; wherein the explicit layer unit corresponds to a sensor observation value at a current time point, the hidden layer unit captures potential time sequence characteristics and modes, correlation between a hidden layer at the current moment and hidden layers at the previous N moments is established through time sequence connection, and model training is conducted through a time-dependent conditional contrast divergence algorithm; aiming at the problem that a traditional Boltzmann machine model is mainly used for processing static distribution, so that the processing precision of a coal mine catastrophe scene with obvious time sequence evolution characteristics is low, the coal mine catastrophe scene deduction precision is improved.

The invention provides a flower quality grading quality inspection method and system based on AI and image processing, and belongs to the technical field of computer vision and pattern recognizing.The method comprises the steps that an original image of the surface of a flower is collected through high-resolution imaging equipment, and high-frequency sub-band data representing tiny physical characteristics are extracted through multistage discrete wavelet transform; meanwhile, a sliding window is adopted to traverse the image, the color information entropy of a local area is calculated, and a color entropy graph is constructed. And after vectoring and splicing the two types of features, inputting the two types of features into a deep belief network model based on a multilayer restricted Boltzmann machine, and extracting deep abnormal feature codes. And finally, performing linear discriminant analysis on the code by utilizing a classification projection vector based on inter-class and intra-class distance optimization to generate an insect attack infection index, and realizing automatic grading quality inspection of the flower quality according to the insect attack infection index. According to the method, precise recognition and quantitative grading of tiny insect pests and recessive lesions on the surfaces of the fresh flowers are realized.

Embodiments of the present disclosure relate to a question answering method, an electronic device, and a computer program product. The method includes: determining an answer library associated with a question; determining a restricted Boltzmann machine associated with the answer library, wherein the restricted Boltzmann machine is configured to determine a set of questions that the answer library can answer and association relationships between questions in the set of questions and the answer library; and determining, using the restricted Boltzmann machine, annotation information associated with the question and targeted to the answer library. With the technical solution of the present disclosure, it is possible to determine, while determining an answer library associated with the question, annotation information that is targeted to the answer library, and to enable customer service personnel to have a more thorough understanding of the determined answer library and obtain targeted recommendation information.

The invention discloses a bearing residual life prediction method based on BN-RBM and DA-BiLSTM, and the method comprises the steps: firstly collecting an original vibration signal in a bearing operation process, carrying out the data preprocessing, and then constructing a bearing residual life prediction model which comprises a BN-RBM feature extraction network, a DA-BiLSTM feature fusion network and a linear regression layer. The BN-RBM feature extraction network dynamically updates the weight prior distribution of a restricted Boltzmann machine RBM by using the posterior probability constructed by Bayesian BN, and performs feature extraction by using the trained RBM, the DA-BiLSTM feature fusion network performs bidirectional feature fusion, the linear regression layer performs linear activation, and the predicted value of the residual life of the bearing is output. The method effectively improves the prediction accuracy of the residual life of the bearing, and reduces the shutdown risk caused by the fault through timely and effective life prediction before the fault.

The present invention discloses a method, device and equipment for predicting drug side effects based on a restricted Boltzmann machine with a penalty regular term, including: constructing a target drug similarity matrix, a target side effect similarity matrix and a drug-side effect association matrix; constructing a drug restricted Boltzmann machine model and a side effect restricted Boltzmann machine model; constructing a maximum likelihood function based on initial model parameters, the target drug similarity matrix, the target side effect similarity matrix and the drug-side effect association matrix, adding a penalty term to the maximum likelihood function, and performing iterative training until a preset number of iterations is reached to obtain the optimal model parameters; finally, using the trained model to perform prediction. The present invention trains the optimal parameters of the model through the gradient descent algorithm of the maximum likelihood function with a penalty term, avoids overfitting during the training process, improves the prediction accuracy of the model, and makes the prediction results more accurate.

The present invention discloses a water quality image analysis method, system, device and medium based on deep learning. A trained restricted Boltzmann machine model is used to obtain dynamic weight values, and then grayscale processing is performed according to the weight values and the weighted average method, which improves the adaptability of dynamic images to the actual environment and the accuracy of dynamic image information; through image anomaly judgment, the influence of other factors causing image anomalies on the water quality analysis results is eliminated, and the accuracy of water quality analysis is improved; the anti-interference ability of the image is greatly improved through noise detection and filtering processing, the image analysis speed is increased, and the accuracy of water quality analysis is further improved; normalization processing is performed on all pixel points of normal images, which further improves the accuracy of dynamic image information and further improves the accuracy of water quality analysis.

This invention discloses a method and system for evaluating the intelligence level of an agent based on a Restricted Boltzmann Machine (RBM), relating to the field of artificial intelligence evaluation technology. First, it acquires replay data from pairwise battles between multiple tested agents in an experimental task. Then, for each tested agent, multiple evaluation indicators are calculated based on its battle data from each replay. Using these indicators as input, a trained RBM network predicts the ELO score of the tested agent. The final ELO score is then obtained by averaging the ELO scores from multiple replays. Finally, for each tested agent, its intelligence level is determined based on its final ELO score. This eliminates the need for subjective level classification standards, achieving an objective and universal intelligence level evaluation.

The invention discloses a power system oscillation mechanism identification method and a model construction method, and relates to the field of power system low-frequency oscillation, and the method comprises the steps: collecting the active power data of a generator and the active power data of a tie line; performing linear normalization and principal component analysis dimension reduction processing on the data, and constructing a multi-dimensional time sequence to-be-tested sample; a sample is input into a pre-trained deep belief network (DBN) model, the model is formed by stacking a plurality of restricted Boltzmann machines (RBM), data deep features are extracted through unsupervised layer-by-layer greedy pre-training, and parameter optimization is performed in combination with supervised fine tuning. According to the method, negative damping oscillation and forced power oscillation can be automatically identified, the uncertainty of manual feature selection is avoided, the influence of noise and amplitude difference on the identification precision is effectively suppressed, and the adaptability of the model to an actual power grid complex disturbance condition is improved.

The application discloses a gear vibration noise prediction method based on a deep belief network, which comprises the following steps: firstly, denoising the gear vibration noise data collected through experiments; secondly, constructing a database based on a frequency analysis algorithm; thirdly, constructing a deep belief network model based on a restricted Boltzmann machine and training the model; fourthly, obtaining optimal parameters through a particle swarm optimization algorithm based on adaptive inertia weight and a learning factor; and finally, using the denoised and amplified gear vibration data to predict the noise by using the APSO-DBN method. The method amplifies the data through frequency analysis, overcomes the training difficulty caused by insufficient data, and has better iteration effect than the PSO-DBN algorithm. In the total sound pressure level prediction, the average relative error of the APSO-DBN algorithm is obviously reduced compared with the DBN network with manually selected network nodes.

The invention discloses a method and a system for intelligently identifying damage of a hydrogen conveying pipeline based on multi-sensor feature fusion. The method comprises the following steps: firstly, acquiring multi-source sensor detection signals including electromagnetic and ultrasonic; performing time domain, frequency domain and time-frequency domain feature extraction on each sensor signal, and constructing an original feature set; then, a restricted Boltzmann machine is adopted to carry out unsupervised deep feature learning and dimension reduction processing on the high-dimensional features, feature-level fusion of multi-sensor data is achieved, and a fusion feature vector with higher characterization capacity is obtained; and finally, inputting the fusion feature vector into a pre-trained BP neural network classifier to realize intelligent identification and classification of different damage types and damage degrees of the hydrogen delivery pipeline. The method has the advantages of being high in detection efficiency and accurate in recognition result, and the accuracy and reliability of hydrogen conveying pipeline damage recognition are effectively improved.

The invention discloses an environmental air quality monitoring method based on artificial intelligence. The method comprises the following steps: S1, collecting and processing air pollutant concentration data, meteorological parameter data and spatial geographic information data; s2, constructing an improved deep belief network model, wherein the improved deep belief network model comprises an input layer, a multi-layer restricted Boltzmann machine hidden layer, a multi-layer physical prior embedding layer, a space-time dependent modeling module and an output layer; s3, performing layer-by-layer unsupervised pre-training operation on the improved deep belief network model; s4, performing global fine tuning optimization on the improved deep belief network model; s5, performing adaptive dynamic adjustment on the weight of the physical consistency error term; s6, inputting monitoring data collected in real time into the improved deep belief network model after global fine tuning optimization, and executing forward reasoning operation; and S7, executing abnormal pollution event detection. According to the invention, the improved deep belief network model is adopted, and accurate prediction and intelligent early warning of the ambient air quality are realized.

Embodiments of the present disclosure relate to a question and answer method, an electronic device and a computer program product. The method comprises: determining an answer base associated with a question; determining a restricted Boltzmann machine associated with the answer base, the restricted Boltzmann machine being used to determine a question set that the answer base can answer and a relationship between a question in the question set and the answer base; and determining, using the restricted Boltzmann machine, description information associated with the question for the answer base. Using the technical solution of the present disclosure, the description information for the answer base can be determined at the same time as the answer base associated with the question is determined, and the customer service personnel can be made to have a more thorough understanding of the determined answer base and obtain targeted recommendation information by providing the determined description information to the customer service personnel, thereby improving the user experience of the customer service personnel using the question and answer system.

A first computing node of a system can receive sensor data about a physical environment. The first computing node can analyze the sensor data with a restricted Boltzmann machine (RBM) neural network to determine whether there is a fault condition in the physical environment, an identification of the fault condition being omitted from data used to train the RBM neural network. The first computing node can update the RBM neural network based on the sensor data to produce a first updated RBM neural network. The first computing node can send a first patch indicative of the first updated RBM neural network to a central server. The first computing node can receive, from the central server, information indicative of a second updated RBM neural network, the second updated RBM neural network being based on an aggregation of the first patch and of a second patch generated by a second computing node.

A three-dimensional animation motion data resource management and generation system belongs to the technical field of three-dimensional animation motion data, and comprises the following steps: executing unreasonable detection on three-dimensional animation motion data by using a Z-score method to obtain unreasonable data, and constructing an unreasonable data set; calculating clutter value representation of the unreasonable data in the unreasonable data group and clutter value representation of the unreasonable data in a near area to calibrate Z scores of the unreasonable data in the Z-score method, and obtaining characteristic Z scores of the unreasonable data; if the characteristic Z score is higher than the score critical quantity, determining that the target unreasonable data is correct unreasonable data, and if the characteristic Z score is lower than the score critical quantity, determining that the target unreasonable data is a clutter value; finally confirming that the rocker is in a failure state; therefore, the restricted Boltzmann machine used for advanced estimation is used for executing advanced estimation of the unreasonable data of the time point queue, and the unreasonable data estimated in advance are accurately obtained effectively through queue information and unreasonable mark information.

The invention provides an image hiding method based on a restricted Boltzmann machine and parallel compressed sensing. The method comprises an encryption stage and an embedding stage. In the encryption stage, sparse decomposition, image scrambling and threshold processing are sequentially carried out on a plaintext image, then a measurement matrix is constructed by using a random sequence to carry out parallel compressed sensing, then quantization processing is carried out on the image, and further rotation scrambling and random diffusion are carried out to obtain a ciphertext image; in the embedding stage, ciphertext image information is embedded into different wavelet coefficients of a carrier image through lifting wavelet transformation, and the embedding depth is dynamically selected according to a random sequence, so that the concealment and the safety of the embedded information are improved; according to the method, the randomness of the sequence can be effectively improved, the image processing efficiency is greatly improved, and high-quality image reconstruction can be realized under a relatively low compression ratio; in addition, the encrypted image generated by the method is higher in anti-attack capability and robustness.

The present invention proposes a method and system for online drilling speed prediction during the drilling process based on multi-source information fusion. The method comprises: a drilling data preprocessing stage, in which drilling pressure, rotational speed, and torque are filtered; a drilling speed modeling optimization stage, in which a hybrid bat algorithm is used to improve the model structure of a restricted Boltzmann machine and a back-propagation neural network, forming a novel hybrid bat algorithm optimization-restricted Boltzmann machine-back-propagation neural network, and using this algorithm to establish an offline drilling speed prediction model for complex formation environments; and a drilling speed prediction model update stage, in which lithologic changes and time intervals are used as update conditions, and a sliding window strategy is used based on the fusion of multi-source formation and drilling information to update the drilling speed prediction model, thereby achieving online drilling speed prediction. The present invention has the beneficial effects of helping to shorten the drilling cycle, reduce operating costs, achieve high-precision drilling speed prediction, and is effective, laying an important foundation for intelligent optimization and control of the drilling process.

The invention discloses a high-precision position keeping method and device for cooperative control of a multi-dimensional motor and a storage medium. The high-precision position keeping method and device are used for keeping the high-precision position of the multi-dimensional motor. The method comprises the following steps: constructing a multi-dimensional data set; performing standardization processing on the multi-dimensional data set to obtain standard data; calculating a covariance matrix based on the standard data and performing eigenvalue decomposition; determining a plurality of principal component feature vectors according to the cumulative variance contribution rate to form a principal component space; projecting the standard data into the principal component space to form a dimensionality reduction data set; inputting the dimension reduction data set into a pre-trained deep belief network, and outputting a comprehensive feature vector through hierarchical features of a restricted Boltzmann machine; inputting the comprehensive feature vector into a depth deterministic strategy gradient algorithm model, and constructing a reward function; outputting a real-time adjustment strategy based on the reward function, and obtaining an optimization control parameter; and realizing high-precision position keeping of the plurality of motors according to the optimized control parameters.

The invention discloses a distributed resource access-oriented power distribution network operation risk level assessment method, which comprises the following steps of: acquiring multi-source operation data of a power distribution network, and performing dynamic aggregation to generate an aggregation feature matrix; inputting the aggregation feature matrix into a double-target sparse auto-encoder to extract risk sensitive features, and outputting a low-dimensional dense feature vector after feature selection; clustering analysis is carried out on the feature vector data to construct an initial weight matrix of a first-layer restricted Boltzmann machine of the deep belief network, and then the feature vectors are input into a pre-trained deep belief network to obtain probabilities belonging to all risk levels; and comparing the probability of each risk level with a corresponding adaptive threshold value, excluding the risk level with the probability less than the adaptive threshold value, and if the risk level with the probability greater than or equal to the corresponding adaptive threshold value exists, determining that the risk level with the highest probability is an evaluation result. The method can effectively and accurately evaluate the operation risk of the power distribution network.

The application discloses a high-precision position keeping method and device for multi-dimensional motor cooperative control and a storage medium, and is used for keeping high-precision position of multi-dimensional motor. The application comprises the following steps: constructing a multi-dimensional data set; performing standardization processing on the multi-dimensional data set to obtain standard data; calculating a covariance matrix based on the standard data and performing eigenvalue decomposition; determining a plurality of principal component feature vectors according to cumulative variance contribution rates to form a principal component space; projecting the standard data into the principal component space to form a reduced dimension data set; inputting the reduced dimension data set into a pre-trained deep belief network, outputting a comprehensive feature vector through hierarchical features of a restricted Boltzmann machine; inputting the comprehensive feature vector into a deep deterministic policy gradient algorithm model to construct a reward function; outputting a real-time adjustment strategy based on the reward function to obtain optimized control parameters; and realizing high-precision position keeping of the plurality of motors according to the optimized control parameters.

This application provides a dynamic modeling and simulation method and system for the energy efficiency ratio (EER) of a solar-powered seawater desalination system, belonging to the technical field of seawater desalination and system modeling. First, this application acquires data on membrane surface resistance, selective permeability, and DC bus voltage ripple during the photovoltaic electrodialysis process. Second, the ripple data is Fourier transformed and concatenated with membrane parameters to generate an input matrix. This matrix is ​​then imported into a deep belief network, and a restricted Boltzmann machine is used to extract the unsteady-state ion impedance vector reflecting the influence of voltage fluctuations. Subsequently, a nonlinear regression model of this vector and unit water production energy consumption is established using a least-squares support vector machine. Finally, the water production rate and EER are calculated based on the predicted energy consumption and photovoltaic power, generating a dynamic simulation curve. This application can quantify the nonlinear influence of photovoltaic voltage ripple on membrane impedance through deep learning, significantly improving the accuracy of EER prediction for seawater desalination systems under fluctuating power supply conditions.