534 results about "Feedforward neural network" patented technology

The invention relates to the technical field of electric power engineering purchase demand prediction, in particular to an electric power engineering purchase demand prediction system based on machine learning, and the system comprises the steps: obtaining historical purchase data, construction progress information and electric power engineering design parameters, carrying out the standard stage division and time alignment, and constructing a stage sequence model reflecting the material use rhythm; and a coupling factor matrix is generated based on the material co-occurrence frequency and the stage position relationship, and the modeling capability of the model for the material cooperation relationship is enhanced. And the stage time sequence features, the coupling information and the structured engineering parameter vectors are fused and input into a regression prediction model, so that accurate mapping of material demands and multi-dimensional engineering features is realized, and the purchase prediction precision in a target period is improved. A deviation sequence is constructed based on historical prediction errors, and error correction is performed through a feedforward neural network, so that prediction accuracy and response capability are effectively improved, and resource waste and construction delay are reduced.

The invention discloses a large language model accelerator architecture based on a three-dimensional NAND flash memory, and belongs to the technical field of calculation, reckoning or counting. The architecture comprises a three-dimensional NAND flash memory used for executing feedforward neural network calculation; the auxiliary calculation unit is used for executing attention mechanism calculation; the DRAM chip is used for storing attention mechanism related weights and KV cache; and the interconnection resource is used for realizing data interaction among the components. Wherein the three-dimensional NAND flash memory comprises a logic chip and an NAND array chip, the logic chip is used for controlling and executing calculation, and the NAND array chip is used for storing weights and participating in calculation. The invention further provides a scheduling method based on KV cache awareness. The scheduling method comprises decomposition and dynamic allocation of calculation tasks. Through collaborative design of the hardware architecture and the scheduling method, the memory wall bottleneck in large language model calculation can be relieved, the energy consumption is reduced, and the overall calculation performance is improved.

The invention discloses a remote sensing image segmentation method fusing frequency modulation and spatial perception, and the method comprises the steps: obtaining and preprocessing an original remote sensing image, and generating a standardized input image; the image is input into a multi-scale frequency domain enhanced feature extraction network, features are extracted step by step according to a plurality of feature levels, each level realizes frequency adaptive semantic enhancement through frequency domain modulation transformation and spatial feature fusion, and deep feature expression is enhanced through feedforward neural network modeling and residual connection output and cross-level residual fusion introduction; the final multi-scale features are decoded through a decoding module, the spatial resolution is recovered, and a pixel-level segmentation result is generated; and constructing a composite loss function containing classification errors, boundary perception and frequency consistency items, and carrying out optimization training on the network. According to the method, semantic complementarity of a remote sensing image in a frequency domain and a space domain is fully mined, so that segmentation precision and robustness of a ground object target in a complex scene are improved, and the method has good generalization ability and engineering practicability.

The invention discloses an H-bridge key equipment service life and system reliability evaluation method and system for a cascade network construction type energy storage system, and belongs to the technical field of power system automation. The method comprises the following steps: firstly, extracting task profile parameters under multiple time scales, and constructing a time sequence feature model; secondly, estimating a hot spot temperature sequence of the IGBT device and the capacitor based on a multilayer feedforward neural network; then, in combination with a continuous extreme point paired temperature cycle extraction method and a Miner linear cumulative damage criterion, the damage factor and the residual life of the device are evaluated; then, task profile samples are expanded based on a generative adversarial network with gradient penalty, and life distribution and reliability indexes of key devices under different profiles are calculated; and finally, based on H-bridge series structure mapping device level information, constructing a system level reliability model, obtaining system failure rate, average fault-free operation time and a reliability function, and realizing health state perception and reliability quantitative evaluation of the energy storage system.

Aspects of the disclosure include methods for leveraging retrieval augmented generation (RAG) over a graph neural network (GNN) for edge building and the generation of reason-aware graph recommendations. A method can include constructing a graph neural network from an input graph having a plurality of nodes and one or more edges. The graph neural network includes one or more internal layers, each internal layer having one or more node vectors encoding a K-hop neighborhood for a target node of the plurality of nodes. RAG data including non-graph contextual data is retrieved for each of the plurality of nodes and transformed into embeddings using a large language model encoder. The RAG embeddings are encoded into node vectors of the graph neural network. The graph neural network generates a representation for the target node that is transformed by a feed forward neural network tower into an output vector.

The invention belongs to the technical field of target detection, and discloses an unmanned aerial vehicle image target detection method based on an improved RTDETR model, and the method introduces an FAPPA module in a backbone network, and effectively alleviates the information loss problem of small target features in a deep network. According to the method, a feedforward neural network of an AIFI module in an encoder is replaced by an EDFFN module, and high-frequency features such as edges and textures are selectively enhanced by using a patchwise FFT strategy and learnable frequency domain weights. Besides, according to the method disclosed by the invention, adaptive alignment of multi-scale features is realized by utilizing a Zoomcat module, and local details and global semantics are fused by utilizing a convolution attention double-path architecture of a CAFMFusion module, so that complementary information of different levels is fully utilized. According to the method, the unmanned aerial vehicle image target detection capability can be remarkably improved, and small target features in a complex background can be identified.

The invention discloses an energy storage rapid compensation method and system based on track overhead line system voltage fluctuation feature recognition, and relates to the technical field of track traffic traction power supply. The method comprises the following steps: synchronously acquiring voltage and current signals of the overhead line system, and extracting a composite feature vector containing disturbance root attributes and transient change rate; on the basis of the vector, a transient energy vacancy sequence covering the ultra-short-term future is output in real time through a prediction model fusing dynamic phasor analysis and a feedforward neural network; according to the sequence, a cooperative control strategy fusing overshoot and active damping is generated in combination with a proximity compensation principle and a voltage recovery state so as to drive an along-line energy storage unit; the strategy is executed in advance, compensation energy is injected, and finally double closed-loop correction is conducted based on the voltage residual error. According to the method, the problems of response lag, unclear disturbance identification, poor collaboration and lack of adaptive ability in the prior art are solved, and advanced, accurate, collaborative and self-optimized rapid suppression of the voltage fluctuation of the overhead line system is realized.

The invention discloses a multi-context semantic recognition and understanding method based on a large language model. The method comprises the following steps: S1, generating a semantic unit sequence; s2, constructing a context nested vector sequence; s3, constructing context priori representation, and generating a semantic representation sequence; s4, outputting a state vector of the semantic path by adopting a gating loop unit, obtaining a matching degree score according to a feedforward neural network, and determining a deliberate map tag and an alternative intention tag; s5, slot field extraction and semantic filling are completed, and a structured semantic task unit is generated; and S6, completing semantic recognition and service response closed loop. According to the method, by introducing a prefix regulation and control mechanism and a multi-context semantic modeling structure, the accuracy of intention recognition in multiple rounds of dialogues and the consistency of the context generated in response are remarkably improved, and the method is suitable for natural language understanding scenes of multi-language intelligent customer service, cross-context man-machine interaction and complex task driving.

The invention provides an optical element surface type simulation method based on a physical information neural network, and relates to the technical field of element design and simulation, and the method comprises the steps: constructing a feedforward neural network; generating a deformation control equation model library; constructing a three-dimensional simulation database; forming a machine learning parameter library; training the PINN deformation simulation model in combination with a deformation control equation model library, a three-dimensional simulation database and a machine learning parameter library, and screening out a target PINN deformation simulation model meeting a preset condition; inputting the design parameters, the support structure parameters and the given external load parameters of different combinations into the target PINN model to obtain a key performance prediction result of the optical element; according to the method, the key performance of the element under various parameter combinations can be rapidly evaluated, the design iteration period is shortened, the trial and error cost is reduced, direct data support is provided for supporting structure optimization design, and the bottleneck problem that a traditional method restricts the research and development efficiency of a high-precision optical system is effectively solved.

The invention belongs to the technical field of remote sensing image analysis, and provides a multi-source remote sensing building extraction method based on hybrid experts, electronic equipment and a storage medium. The method comprises the steps of basic model construction, two-way structure model construction, optical feature extraction, earth surface feature extraction, two-way feature fusion, ViT coding, feature injection and extraction and semantic alignment step-by-step up-sampling. According to the invention, a dual-path structure is adopted to assist the multi-scale encoder and the bidirectional attention fusion module, and DSM data and optical images are used for feature fusion, so that the capability of distinguishing buildings from backgrounds is improved; the hybrid expert LoRA structure is introduced into the ViT encoder, parameters of the feedforward neural network are dynamically adjusted, the adaptability and flexibility of the model to input features are further enhanced, and the calculation complexity is reduced.

The invention discloses a flow field prediction-oriented full-process automatic joint reduced-order modeling method, which comprises the following steps of: specifying a target physical field parameter space, and randomly generating a sample space according to a Latin hypercube sampling method; constructing a full-process automatic simulation tool chain, driving target physical field numerical calculation and generating a training data set; carrying out singular value decomposition-based intrinsic orthogonal decomposition on the output physical field data, and only retaining first r main feature components to construct a reduced-order data set; constructing a multi-input multi-output full-connection feedforward neural network, and modeling and training a nonlinear mapping relation between input parameters and reduced-order features; new working condition parameters are input, reduced-order features are predicted through the trained neural network, distribution of a target physical field is reconstructed according to a singular value decomposition reduction matrix, and more flexible and reliable technical support is provided for reducing the training cost of a reduced-order model and improving simulation efficiency.

The invention relates to a power battery performance degradation prediction method and system based on an electrochemical-thermal-mechanical-neural network model, and belongs to the technical field of battery management. According to the method, an enhanced single particle model considering liquid phase diffusion, an integrated thermal model, a particle mechanical stress model caused by concentration gradient and an aging mechanism model considering SEI growth, lithium precipitation and active material loss are coupled to establish ETMD, and based on the ETMD, the identification efficiency of aging model parameters is low aiming at the problem that a PSO algorithm is low. Under a circulating aging experiment of a limited path, the characteristic parameters of the aging model are obtained by using the feedforward neural network, and the parameters are substituted into the ETMD model again to predict the capacity, the power attenuation curve and the corresponding main attenuation mechanism under the current path, so that the prediction precision and the interpretability of the performance attenuation of the battery under the full path are improved. According to the method, the modeling capability of battery performance degradation under different working conditions can be effectively improved, and a theoretical basis and technical support are provided for residual life prediction and safety management of the power battery.

The invention relates to a bearing fault diagnosis method based on variational mode decomposition and time sequence partitioning cross attention fusion, which comprises the following steps: acquiring an original vibration acceleration signal of a rolling bearing, and constructing a standardized original data set; segmenting the standardized original data into a plurality of data blocks, and generating a time domain embedding feature; based on the time domain embedded features, extracting high-order global time domain features by using a multi-head self-attention mechanism, residual connection and a feedforward neural network; decomposing the standardized original data into a plurality of intrinsic mode functions, and extracting frequency domain distribution features through a convolutional neural network; taking the frequency domain distribution characteristics as query vectors, retrieving and matching related fault context information in the global time sequence characteristics, realizing weighted fusion of time-frequency modes, inputting fused fault representation vectors into a classifier, and calculating a result of a bearing health state; and constructing a loss function containing label smoothing and a dynamic learning rate scheduling strategy, and carrying out iterative optimization on model parameters until the model converges.

The invention discloses an electric power system large-scale language model tuning method, system and device based on LoRA technology optimization and a medium, and belongs to the technical field of electric power system large-scale language models, and the method comprises the steps: obtaining historical language data formed in the operation process of an electric power system, and converting the historical language data into a training sample; constructing a task data set based on the training sample; constructing a pre-training language model; receiving a to-be-processed input text, extracting semantic features of the input text, and generating an output sequence corresponding to the input text. According to the method, the low-rank weight path is inserted in the feed-forward neural network in a parallel form, and an independent path is bound for each type of tasks, so that differential fine tuning of the electric power semantic tasks is realized on the premise of not modifying the structure and parameters of an original model, the overall tuning cost of the model is reduced, and the stability of the original model is kept.

The invention discloses a blurred image NeRF modeling method, system and device based on a scattering light path model and a medium. The modeling method comprises the steps of blurred image initial pose calculation, camera pose interpolation, neural radiation field NeRF-based 3D scene modeling, scattering light parameter learning guided by an internal scattering light path model, blurred image synthesis prediction by using a scattering sensing volume rendering method, and parameter joint optimization based on blurred image luminosity loss. The system, the equipment and the medium are used for implementing the method. A light propagation phenomenon is represented as internal scattering of light at a medium or surface intersection point through an internal scattering light path model, a scattering light path direction and a sampling point distance are autonomously learned by means of a feedforward neural network, and image rendering is performed by integrating contributions of sampling points in the directions of a main light path and the scattering light path. The real imaging process of a blurred image in a complex illumination environment can be effectively simulated, the geometric ambiguity problem is effectively avoided, the convergence stability of the neural network is improved, and a finer reduction effect on geometric details of a fine object is achieved.

The invention discloses an adaptive controller tuning method based on reinforcement learning. The method comprises the following steps: S1, constructing a photovoltaic control task environment; s2, inputting the state sequence into a long short-term memory network, and outputting an embedded state vector; s3, constructing a hierarchical policy network comprising a basic policy module and a fast adaptation module; s4, training the hierarchical strategy network, inputting the embedded state vector into the hierarchical strategy network to generate a controller parameter action, interacting with a photovoltaic control task environment by using a multi-layer feedforward neural network to obtain response and reward feedback, and executing target value calculation and strategy optimization; s5, obtaining a general strategy initial parameter by using a meta-learning optimization method; and S6, deploying the optimized hierarchical strategy network to a target photovoltaic control task. The method is suitable for photovoltaic control and other multi-working-condition dynamic environments, and has the advantages of being high in strategy migration capacity, high in robustness and excellent in self-adaptive performance.

The invention aims to provide a high-precision design and optimization method for an EMI (Electro-Magnetic Interference) filter aiming at the corresponding defects in the prior art, a mixed neural network architecture model is constructed by combining a feedforward neural network and a convolutional neural network, and the filter is subjected to reverse modeling by utilizing the mixed neural network architecture model, so that the filtering precision is improved. According to the method, the predicted value, the topological structure and the element parameters of the filter are obtained, then the element parameters are subjected to multi-objective optimization by using a multi-objective genetic algorithm to obtain the optimized element parameters, and the optimized element parameters and the topological structure of the filter jointly form an optimization scheme of the filter, so that the design efficiency and the optimization precision of the EMI filter can be remarkably improved; and the design period of the EMI filter is shortened, and the market demand of rapid iterative updating of current high-frequency electronic equipment can be met.

The invention discloses a multi-modal data fusion drug-target affinity prediction system based on Graph Transform. The system comprises a data preprocessing module, a graph representation module, a text representation module and an affinity prediction module. The data preprocessing module is responsible for analyzing and processing the SMILES character string of the compound and the protein ID, and extracting the structural information of the compound and the three-dimensional structural data of the protein. And the affinity prediction module performs multi-modal fusion on the graph features and the text features, processes fusion feature vectors through a feedforward neural network comprising three full-connection layers, and outputs a prediction result of drug-target affinity. According to the method, multi-modal information of a graph structure and a sequence text is combined, the accuracy of cross-domain drug-target affinity prediction can be effectively improved, and the method has a wide application prospect.

The invention is suitable for the technical field of three-dimensional target detection, and provides a 3D target detection method and device based on inter-modal bidirectional interaction and progressive reasoning. According to the invention, independent feature extraction is carried out on an input laser radar point cloud and a multi-view image; performing cross-modal attention interaction, residual refinement and intra-modal local self-attention calculation to obtain enhanced feature data; performing cross-modal reasoning and target decoding to generate fusion feature data; and inputting to a feedforward neural network, and generating a final three-dimensional target detection result through a full connection layer. Bidirectional dynamic compensation of geometric information and semantic features can be realized, cross-modal feature expression is optimized layer by layer by adopting a progressive reasoning mechanism, and a scene adaptive dynamic fusion strategy is designed. The integrity of small target detection in a shielding scene, the accuracy of target positioning under a complex illumination condition and the stability of long-distance target sensing are effectively improved, and meanwhile, the robustness of a multi-mode sensing system in a dynamic environment is guaranteed.

The invention discloses an inverter power supply network construction type control method based on an artificial neural network, and relates to the technical field of inverter power supply network construction type control. Collecting an electrical parameter sequence set of an inverter power supply grid-connected point; constructing a power supply network construction control analyzer based on the deep feedforward neural network, and outputting a first network construction control parameter; building a power distribution network simulation topology model based on PSCAD to perform parameter optimization, and generating a second network construction control parameter; and analyzing and determining the fluctuation degree of the operation state, carrying out weighted fusion on the two types of control parameters according to a set dynamic fitting strategy, obtaining an adaptive networking control strategy, and implementing regulation and control. According to the invention, on-line intelligent optimization of control parameters is realized through a two-way parallel mechanism of quick response of the neural network and accurate verification of the simulation model in combination with a self-adaptive fitting strategy of operation state perception, and the stability, adaptability and control precision of the inverter power supply under complex working conditions are effectively improved.

The invention provides a power short-term load prediction system based on multi-source information adaptive fusion and a working method thereof, and the system comprises a multi-view data construction module which is used for organizing heterogeneous input data into a plurality of independent semantic views; the spatial dependence coding module is used for learning spatial dependence among nodes through a graph attention network and generating spatial embedding representation; the time dynamic modeling module is used for processing the time sequence view through a recurrent neural network and generating time feature embedded representation; the auxiliary information modeling module is used for processing other auxiliary views through independent feedforward neural network processing and generating auxiliary feature embedded representation; the cross-view attention fusion module is used for carrying out adaptive weighted fusion on all the embedded representations so as to inhibit representation degradation caused by low-quality views; and the load prediction module is used for generating a load prediction result based on the fusion representation and carrying out end-to-end training by taking a mean square error as a target.

The invention belongs to the technical field of image recognition, and provides a low-illumination target detection method, which comprises the following steps of: improving a model structure by taking YOLOv11n as a reference model, and designing a noise frequency band sensing spectrum feedforward neural network module, an aliasing sensing frequency reconstruction up-sampling module and an illumination sensing multi-attention fusion detection head; wherein the noise frequency band perception spectrum feedforward neural network module introduces a frequency domain adaptive modulation and illumination perception double-gating mechanism, the aliasing perception frequency reconstruction up-sampling module introduces a frequency perception and reconstruction mechanism, and the illumination perception multi-attention fusion detection head introduces an illumination perception gating mechanism; using an ExDark low illumination data set to train an improved network model; and performing low-illumination image detection by using the trained model. According to the method, the high reasoning speed can be kept in a low-light environment, and the detection capability and the positioning accuracy of weak visible targets, fuzzy edge targets and small-scale targets are improved.

The invention discloses a star-sky dim and small target detection method fusing coordinate attention and a convolutional feedforward neural network, and belongs to the technical field of dim target detection. The method comprises the steps of obtaining a starry sky image data set, outputting an enhanced feature map by applying a coordinate attention mechanism, constructing a network model fusing the coordinate attention mechanism and a ConvFFN module through the ConvFFN module of a convolutional feedforward neural network, training a verification model based on simulation data, and finally outputting the centroid position and bounding box information of a target. According to the method, features are extracted in the transverse and longitudinal directions through a coordinate attention mechanism, the feature expression ability of a weak and small target in a low signal-to-noise ratio environment is effectively enhanced, the detection precision is remarkably improved, the omission ratio is reduced, the calculation efficiency is improved by combining the lightweight structure of ConvFFN and deep convolution optimization, and the method is suitable for large-scale popularization and application. A reliable and efficient detection solution is provided for astronomical observation and space debris monitoring.

The invention relates to the technical field of test instruments, and discloses a machine learning and feature selection-based saturated clay shear strength prediction method, which comprises the following steps of: obtaining physical property parameters of saturated clay and corresponding shear strength data, and establishing an initial data set; performing data preprocessing on the initial data set to obtain a training set and a verification set; taking the screened training set and verification set as input features; constructing a machine learning model, and establishing a mapping relationship between the physical property parameters and the corresponding shear strength data; and utilizing a machine learning model to predict and obtain an anti-shearing strength value. According to the method, physical property parameters and corresponding anti-shearing strength data of saturated clay are collected, and a high-dimensional nonlinear mapping relation between the physical property parameters and the corresponding anti-shearing strength data is constructed by utilizing a feedforward neural network algorithm. Therefore, rapid and automatic prediction of the shear strength can be realized based on the easily obtained physical property parameters, the prediction efficiency is remarkably improved, and the limitation that a traditional test method is time-consuming and labor-consuming is broken through.

The invention belongs to the technical field of computer vision and image processing, and discloses a low-light image enhancement method and system based on space-frequency domain feature resolution self-adjustment. According to the enhancement method, a feature splitting and splicing process, a space-frequency domain feature resolution self-adjusting network architecture based on an encoder-decoder framework, and a complete processing process from low-light image input to normal illumination image output are provided. According to the method, a multi-scale feature map is further flattened into a two-dimensional matrix, image restoration is carried out from features of different scales through a linear layer and a feedforward neural network, and the integrity of a receptive field is further kept; besides, after the important spatial features are extracted, frequency restoration is carried out on the important area, the common problem that the dark area is excessively enhanced and details of the bright area are lost is effectively solved, and the color fidelity and the structural integrity in the low-illumination scene are better.

The invention discloses a large language model machine forgetting algorithm based on representation spatial offset. The algorithm comprises the following steps: constructing a forgetting set and a retention set, executing causal tracking on a large model by using a knowledge exploration data set, and identifying a feedforward neural network having a significant contribution to correct prediction of the model to determine a forgetting layer; estimating an input feature space by using the reserved set approximation, and constructing a null-space projection matrix based on the feature space; inputting a forgetting set and a retention set, obtaining representation output of the training model and the original model in the last forgetting layer, and exporting a parameter updating gradient through a loss function; and carrying out null-space projection transformation on the updated gradient and then updating the last linear transformation matrix of the feedforward neural network in all forgetting layers. According to the method, the knowledge storage characteristics of the feedforward neural network in the large model are utilized, knowledge removal is guided in the representation space, interference on non-target knowledge is effectively suppressed while accurate forgetting is achieved, and the effectiveness and controllability of the forgetting process of a large model machine are remarkably improved.

The invention discloses a multi-task electromagnetic model based on a hybrid expert network, and belongs to a wireless communication technology. The model comprises a preprocessing module, a feature extraction module, a task output module and a pre-training-fine tuning learning strategy. The preprocessing module carries out standardization processing on the multi-source electromagnetic signals; the feature extraction module is based on a Transform structure, introduces a hybrid expert network to replace part of a traditional feedforward neural network, and dynamically selects an expert sub-network through a task specific routing mechanism; the task output module configures a special structure according to different task targets; in the pre-training stage, a mask auto-encoder is used for pre-training large-scale label-free data, and a downstream task is subjected to full-amount fine adjustment through small-scale label data. According to the method, multi-task collaborative learning and differential expression are realized, and the recognition performance, robustness and processing efficiency of the model in a complex electromagnetic environment are improved.

The invention relates to the technical field of battery detection, in particular to a method for rapidly detecting the health state of a retired lithium ion battery. The method comprises the following steps: collecting and processing basic parameter information of a battery and multi-working-condition charge-discharge cycle test data, and establishing a training data set; calculating the battery capacity by using an ampere-hour integral method to obtain an SOH tag value; health features are extracted based on the short-time constant-current charging data, and normalization processing is carried out on the health features; constructing and training a feedforward neural network model, and enabling the feedforward neural network model to output an SOH estimated value according to the normalized health features; and performing short-time constant-current charging and data acquisition on a to-be-tested retired battery, extracting health features and injecting the health features into the model to obtain an SOH estimated value. According to the technical scheme, the accuracy and applicability of battery health state estimation can be improved.

The invention relates to a multi-label text classification method based on positive and negative label learning and label correlation, and belongs to the field of multi-label classification. Comprising the following steps: constructing a feedforward neural network model with double hidden layers; initializing a model component; reading features and label information of samples in the training set, and generating a feature matrix and a label matrix; randomly initializing a weight matrix and an offset parameter of the model; inputting the feature matrix into an input layer of the model, and calculating neuron output layer by layer; calculating gradients of weight matrixes and bias parameters among layers in the model by adopting a composite error function, dynamically adjusting the gradients by utilizing an Adam optimization algorithm, and updating the weight matrixes and the bias parameters; when the error change amplitude is lower than a threshold value or reaches a preset number of iterations, stopping training; and after model convergence, predicting the test set to form a final multi-label classification result. According to the method, the limitation of traditional text classification is broken through through a deep learning technology, and high-precision and high-efficiency classification of complex text data is realized.

The invention discloses a current prediction control method and device for a permanent magnet motor, and the method achieves the real-time prediction of a voltage error caused by a dead zone and other non-ideal factors through the powerful nonlinear fitting capability of a feedforward neural network in combination with the learning of historical current time sequence characteristics. And the prediction error is further fed forward and compensated to a future current prediction model to improve the accuracy of current prediction, so that the cost function evaluation is more accurate, the optimal voltage vector is selected, and the steady-state current error and the low-speed torque ripple are effectively inhibited. In this way, precise control over the current of the motor is achieved, and the control precision and operation stability of the driving system are comprehensively improved.