104 results about "Linear modulation" patented technology

The invention provides an industrial defect image generation system and method based on deep learning, and relates to the technical field of artificial intelligence. A defect form adaptive module, a physical attribute modulation module and a multi-mechanism fusion module are integrated in a defect image generation module; dynamically selecting a feature extraction unit according to the defect type label based on a pre-constructed generative network model, and generating a defect feature map; generating an affine transformation parameter based on the physical attribute vector, and performing channel-by-channel linear modulation on a defect feature map of a middle layer of the generative network model, so that a multi-scale defect feature map finally generated by the generative network model contains specified defect type features and physical attribute features; and fusing the multi-scale defect feature image and the defect-free background image to obtain a fused defect image. The problems that in the prior art, industrial defect image generation is insufficient in sense of reality, poor in controllability and poor in fusion effect are solved, the method can be used for data enhancement of industrial visual inspection, and downstream model performance is improved.

The invention discloses an intelligent agent digital image interaction generation method based on multi-modal perception, which comprises the following steps: collecting multi-modal input data of a user, and respectively carrying out preprocessing and feature extraction on the multi-modal input data; inputting to an improved efficient modal cross learning network, and carrying out multi-modal feature fusion processing; constructing a semantic intention map, introducing a time index edge weight and an emotion driving edge weight, and encoding the map by using a structure perception map neural network; a modal style vector is extracted through a cross-modal style contrast learning mechanism, and a personalized style coding vector is generated through a hierarchical nested structure; inputting a personalized regulation and control gating mechanism, and regulating and controlling the middle layer representation in the interaction strategy generation process by adopting a feature channel linear modulation method; inputting the representation vector into a behavior strategy generation module to generate a multi-modal behavior output sequence; and the sequence is output to drive the digital image to perform synchronous response, and natural response generation in the user interaction process is completed.

The invention discloses a long-tail data generation method based on feed-forward stylized reconstruction. The method comprises the following steps: constructing multi-modal geometric perception input and feature codes; reconstructing a semantic guidance scene based on a feature-level linear modulation mechanism; the invention relates to 3D consistency controllable style migration based on a feedforward reconstruction model. According to the invention, a feedforward reconstruction network based on DINOv2 improvement is constructed. Multi-modal adaptation is performed on a backbone network input layer, and decoupled high-level semantic features are explicitly injected by using a feature-level linear modulation mechanism, so that the geometric accuracy of automatic driving scene reconstruction is improved. On the basis of a three-dimensional scene of feedforward reconstruction, a distillation strategy is adopted, and the generation capability of a 2D diffusion model is migrated to a 3D Gaussian field. According to the method, the phenomena of object deformation, disappearance and the like in the generated data are effectively avoided, and long-tail weather data with geometric consistency of rain, snow and the like can be generated through the text instruction.

The invention discloses a small sample crack recognition method and system based on transfer learning, and the method comprises the steps: constructing an innovative and deep-coupled neural network architecture, and enabling a self-adaptive crack perception attention module integrated with a parallel asymmetric convolution kernel to recognize the linear geometric features of a crack, and generating an attention graph; then, the attention map is adopted to carry out pixel-by-pixel signal pre-modulation on an original input image, the enhanced image is sent to a pre-training MobileNetV4 backbone network integrated with a feature-level linear modulation layer, and channel-level dynamic adaptation of feature flow in the network is realized by learning affine transformation parameters; and finally, the extracted depth features are sent to a Gaussian naive Bayes classifier for classification, and depth geometric analysis is carried out on the attention map so as to realize interpretable fracture severity evaluation. According to the method, the accuracy, robustness and interpretability of crack identification are remarkably improved, and the technical bottleneck in a small sample scene is effectively solved.

The invention relates to the technical field of optical fiber sensing, in particular to a distributed optical fiber vibration-temperature combined monitoring method and system. Comprising the following steps: carrying out linear frequency modulation on single-frequency light waves output by a light source to enable the single-frequency light waves to serve as probe light for distributed vibration and distributed temperature sensing at the same time; linear sweep frequency light is injected into an optical fiber to be measured, and Rayleigh scattering and Raman scattering light signals in the optical fiber are separated by using a wavelength division multiplexer. And the vibration information of each point along the optical fiber is recovered by combining a Rayleigh scattering light signal with a coherent detection architecture through algorithms such as back-end matched filtering and the like. And the Raman scattering light signal is combined with intensity detection and a dual-wavelength difference algorithm, so that the temperature information of all optical fiber lengths can be recovered. According to the invention, vibration and temperature measurement is realized in the same system, and the problems of temperature and vibration measurement time synchronization, sampling rate alignment and the like in different systems are avoided.

The invention belongs to the technical field of speech enhancement, and particularly relates to a controllable causal double-path speech enhancement method and system, and the method comprises the steps: taking noisy speech collected in an actual scene as input, carrying out the short-time Fourier transform, and obtaining a complex spectrum containing an amplitude spectrum and a phase spectrum; constructing a streaming encoder, taking the complex spectrum as input, and extracting local correlation of the noisy speech in a time dimension and a frequency dimension; a controllable causal double-path module is constructed, long-term dependency relationship modeling is carried out on the time dimension and the frequency dimension, and linear modulation is carried out on channel characteristics through control parameters; constructing a streaming amplitude / phase decoder, and reconstructing an amplitude spectrum and a phase spectrum of the enhanced speech; and performing short-time inverse Fourier transform by combining the reconstructed amplitude spectrum and phase spectrum to obtain the waveform of the enhanced speech. According to the invention, the real-time adjustment of the output characteristics of the voice enhancement network is realized, and the noise residue and the voice distortion level can be timely balanced according to the demands of a listener in the voice enhancement process.

The application discloses a kind of based on multiple exposure generation fusion's low-light image enhancement system, belong to low-light image enhancement technical field.System includes: adaptive multiple exposure adjustment module (AMEA), joint prediction gamma coefficient, linear modulation factor and compensation parameter, to input low-light image carries out nonlinear and linear combination transformation, generates multiple luminance, contrast and different color response multiple exposure candidate image;Multiple exposure fusion module (MEF), original low-light image and candidate image channel splicing are carried out, and preliminary enhancement image is obtained by fine-grained weighting fusion of channel fusion coefficient and introduction of perception compensation parameter correction;Color refinement module (CRM), using encoder decoder structure and introducing fast fourier representation in bottleneck layer, realize spatial domain and frequency domain joint modeling, carry out global color calibration and local detail optimization to preliminary enhancement image.The overall network uses lightweight design such as depth separable convolution and dense connection, inference is simple, and can be run in real time in edge device.The application generates by adaptive multiple exposure, structure constraint fusion and frequency domain refinement, effectively restores dark texture, suppresses over-enhancement and color deviation, improves visual naturalness and robustness in complex low-light scene.

This invention discloses a method for inverting sea surface wind speed from GNSS reflected signals based on mask perception and image-based Mamba-CNN. The method includes: performing spatiotemporal matching and interpolation processing on multi-source spaceborne GNSS-R observation data and ERA5 reanalysis data; gridding the processed observation parameters according to geographical location to obtain a multi-channel grid image and acquiring a binarized occupancy mask; inputting the multi-channel grid image and the binarized occupancy mask into a mask perception Mamba-CNN model; calibrating the grid features through a mask conditional encoder and a feature linear modulation layer, and performing spatial modeling using feature extraction blocks combining convolutional and Mamba state space paths to obtain the wind speed inversion result; aggregating the extracted features using center-perception pooling, and outputting the final sea surface wind speed inversion image through a regression network. This invention can significantly reduce computational complexity, effectively reconstruct the continuous wind field structure, and improve the inversion accuracy and robustness in high-wind-speed areas.

Provided in the embodiments of the present application are a speech enhancement method, a speech enhancement model training method and a computer program product. The speech enhancement method comprises: acquiring a speech data sample of a target speaker, and performing spectral feature extraction on the speech data sample on the basis of a bidirectional long short-term memory network layer and an attention statistics pooling layer, so as to obtain a spectral feature of the target speaker; inputting the spectral feature into a feature-wise linear modulation layer in a time-frequency narrow-band neural network, and by means of the feature-wise linear modulation layer and on the basis of the spectral feature, adjusting a spectral feature corresponding to mixed speech data extracted by the time-frequency narrow-band neural network, wherein the mixed speech data at least comprises speech data of a plurality of speakers including the target speaker; and on the basis of the adjusted spectral feature, performing speech enhancement processing on the speech data of the target speaker in the mixed speech data, so as to obtain the enhanced speech data of the target speaker.

The invention discloses a visual navigation method and system based on a denoising diffusion bridge model. The method comprises the following steps: acquiring initial image data; performing feature coding processing on the initial image data to generate a context vector; performing linear modulation processing on the context vector to generate a conditional variable; according to the context vector and a preset motion rule, a prior action is generated; inputting the priori action and the conditional variable into a denoising diffusion bridge model for diffusion training, and generating a target navigation action; wherein the priori action is used as an initial state of the de-noising diffusion bridge model during reverse de-noising processing. Redundant iteration in the diffusion process can be effectively reduced, accumulative errors are reduced, the efficiency and stability of action generation are greatly improved, and the method can be widely applied to the technical field of computers.

The present invention relates to a control system (20) and method for controlling the flow of current between a voltage source (22) and a multiphase motor or other application / load for controlling a multiphase motor (28). The control system includes, between each phase of the multiphase motor and the voltage source: a high-side diode (30A-C) and a high-side switch (34A-C) positioned in parallel between a positive terminal of the voltage source and the motor, wherein the high-side diode is reverse biased with respect to the positive terminal; and a low-side diode (32A-C) and a low-side switch (36A-C) positioned in parallel between a negative terminal of the voltage source and the motor, wherein the low-side diode is reverse biased with respect to the motor. The control system operates the high-side and low-side switches to provide efficient operation of the motor.

The invention provides a piezoelectric ceramic-based double-optical-comb-cavity modulated gas measurement system and method, a double-optical-comb output module is utilized to generate a detection optical comb and a reference optical comb which are coherent, the detection optical comb enters a resonant cavity to interact with gas to be measured, and the reference optical comb directly reaches a data acquisition and analysis module; the piezoelectric ceramic modulation module dynamically adjusts the length of the resonant cavity by applying a periodic voltage signal; and the data acquisition and analysis module demodulates spectral data of the to-be-detected gas through an asynchronous sampling technology based on double optical comb interference signals of the reference optical comb and the detection optical comb. According to the dual-optical-comb-cavity modulated gas measurement system based on the piezoelectric ceramics, the piezoelectric ceramic modulation module is used for dynamically adjusting the length of the resonant cavity according to the detection optical comb and the reference optical comb, dynamic modulation and transient coupling optimization of the cavity length are achieved, and the problems that in the prior art, the dispersion effect of gas can affect optical signal propagation, and the measurement precision is poor are solved. And non-linear modulation occurs in the ring-down process, so that the measurement precision is influenced.

The present application relates to the technical field of image processing, and more particularly to a lesion segmentation method of adaptive dynamic text prompt, comprising: acquiring a lesion image, and generating an adaptive dynamic text prompt based on the lesion image; constructing a multi-modal enhanced fusion text prompt adapter based on a FiLM global channel recalibration module, a spatial cross-attention interaction module and a zero initialization gate nonlinear integration module cascade; the global channel recalibration module of FiLM utilizes a feature linear modulation mechanism to perform channel-level weighting on a visual feature map; before the feature enters spatial interaction, according to the text semantic enhancement band response, the activation of the background noise channel is inhibited. The present application overcomes the problems of insufficient utilization of multi-modal prompts, dependence on artificial static labeling of text prompts, serious dependence on prior information in the reasoning stage and difficulty in multi-modal feature fusion in a low-contrast environment in the existing medical image segmentation technology.

This invention provides a production monitoring system for mirror cabinet processing equipment based on multi-source data, relating to the field of process control. The system acquires multi-source data from the mirror cabinet through an acquisition module and constructs a discrete mesh model; a deviation topology construction module generates directional selective edge weights and a nonlinear modulation function; a propagation control coupling module achieves synchronous iterative convergence of deviation states and control components; a topology constraint module identifies structural boundaries and divides control sub-regions; a region control module generates region control vectors, which are then decomposed into processing adjustment quantities and coupled in by the instruction module; and a feedback reconstruction module optimizes parameters based on the processing results. This invention enables deviation propagation to match the processing path, achieving bidirectional coupling of state and control, thus improving accuracy and control stability in the processing of complex curved surfaces and multi-material mirror cabinets.

This application discloses a soft-start method, apparatus, device, and storage medium for a three-phase LLC converter. The soft-start method includes: responding to a start command, pre-charging the three phases of the three-phase LLC converter in a preset sequence; after the three phases have completed pre-charging, performing coordinated linear modulation on the duty cycle and frequency of the three phases so that the duty cycle of each phase approaches a preset target duty cycle, and the frequency of each phase approaches a preset target frequency; and controlling the three-phase regulated output when the duty cycle of each phase reaches the preset target duty cycle and the frequency of each phase reaches the preset target frequency. This application can achieve more stable and safer three-phase soft starting.

The invention discloses a motor linear overmodulation method and device, a vehicle and a storage medium, and the method comprises the steps: obtaining a reference voltage component and a DC bus voltage of a motor, and obtaining a reference voltage amplitude and a reference voltage phase angle according to the reference voltage component; a modulation coefficient is obtained according to the reference voltage amplitude and the direct-current bus voltage, the working area of the motor is judged according to the modulation coefficient, and the working area is divided into a linear modulation area, an overmodulation I area and an overmodulation II area according to the modulation coefficient; and when the working area is the overmodulation II area, obtaining a dynamic weight coefficient according to the reference voltage phase angle, and controlling the motor according to the dynamic weight coefficient. Therefore, when the working area of the motor is the overmodulation II area, the motor is controlled according to the dynamic weight coefficient obtained by the voltage phase angle, so that the voltage jump of the overmodulation II area is eliminated, the power performance of the electric vehicle driving system is improved to the maximum extent, and meanwhile, the output smoothness is ensured.

The invention discloses a direct-current-bias-free high-power LED visible light signal transmission method and system, and relates to the technical field of optical communication, and the method comprises the steps: carrying out 64QAM-OFDM modulation and IQ modulation on original audio and video data, carrying out AC coupling peak-to-peak value extraction and DC component filtering, and then carrying out single-power-supply amplification and pre-equalization; and a driving positive voltage signal is generated to drive a high-power LED to emit light, and an optical communication signal is generated. Through a low-loss alternating-current coupling transmission mode, the modulated real signal peak-to-peak value is subjected to coupling transmission and single positive power supply amplification, so that the signal is changed into positive voltage to control the LED light intensity and form an LED optical communication signal, direct-current voltage does not need to be introduced into a digital domain, the problem of bandwidth reduction is avoided, and the signal transmission efficiency is improved. The direct-current bias voltage of the LED can be kept stable, the LED works in a linear modulation area, bandwidth expansion is carried out through the pre-equalization circuit, and the functions of LED illumination and high-speed communication can be considered.

A method (100) for determining a power dissipation value of an electronic power circuit during an electrical period of a modulated control signal is disclosed, comprising: determining a linear mean switching energy value (110) of the electronic power circuit based on a modulated control signal with linear modulation; determining a nonlinear mean switching energy value (120) of the electronic power circuit based on a modulated control signal with nonlinear modulation, in particular overmodulation; determining a scaling factor value (130) based on the linear mean switching energy value and the nonlinear mean switching energy value; and determining the power dissipation value (140) based on the linear mean switching energy value and the scaling factor value.Furthermore, a computing unit designed and equipped to execute the procedure (100) is disclosed.

The application discloses a panoramic image depth estimation method combining spherical correction and semi-supervised learning, innovatively designs a spherical correction (SR) mechanism based on spherical coordinate Fourier coding and feature linear modulation (FiLM), a lightweight plug-and-play PanoAdapter adaptive module, a teacher-student semi-supervised cross-domain knowledge transfer framework and a joint optimization strategy fusing pseudo-label distillation and consistency constraint, realizes apparent correction of equidistant cylindrical projection (ERP) nonlinear spherical distortion, alleviates the contradiction between panoramic labeling data scarcity and perspective model cross-domain migration difficulty, and improves the geometric consistency and generalization ability of panoramic depth estimation in diversified real scenes.

This invention discloses a method for preparing epitaxial stress-controlled polarized metals, belonging to the field of functional oxide thin film preparation. The method includes: performing a special high-temperature annealing pretreatment on a YAlO3(111) substrate; calibrating the pulsed laser deposition rate and using a moving mask technique to prepare a wedge-shaped thin film with continuously varying thickness; and precisely controlling key process parameter windows such as growth temperature, laser energy density, oxygen partial pressure, and the distance between the target and the substrate. This invention obtains an ideal nucleation template by optimizing the substrate pretreatment process, achieves linear modulation of the thickness gradient by precisely controlling the matching of the mask moving speed and deposition rate, and obtains a high-quality polarized metal thin film with a room temperature resistivity as low as 173 μΩ·cm and stability across the entire temperature range by systematically optimizing key process parameters. This method has the advantages of high repeatability, high control precision, and wide applicability, and can be extended to the preparation of other perovskite oxide thin films, showing broad application prospects in the field of polarized electronic devices.

The invention discloses a liquid carbon dioxide phase change refrigeration system and method based on dynamic gas displacement adjustment, and relates to the technical field of refrigeration control and thermotechnical process automation, and the method comprises the following steps: S1, collecting temperature, pressure, cooling capacity and power data, and constructing an operation state vector; s2, setting the opening degree of an exhaust valve and the liquid injection rate as control variables, superposing periodic disturbance and nonlinear modulation signals, and constructing a disturbance input sequence; s3, fusing the disturbance input and the target response signal, and inputting the disturbance input and the target response signal into a control channel filter to generate a channel response signal; s4, estimating a disturbance response value through a generalized state filter; s5, constructing a nonlinear control law, and outputting a basic control quantity; s6, a regulation factor is generated according to the cooling capacity and the temperature change rate, and a control quantity is output; and S7, controlling the execution unit and feeding back the state to form a closed-loop process. According to the invention, the response speed and the temperature control precision of exhaust and liquid injection adjustment are improved.

The present invention discloses a small sample process monitoring method suitable for a multi-working condition environment, which is used to solve the problem of small sample fault detection under different working conditions. The present invention embeds the prior knowledge of the working condition into a model-independent meta-learning framework to improve the learning ability. A meta-learning framework is used to construct multiple small sample tasks, and a convolutional neural network is used to extract features from the samples. An embedding vector representing the working condition mode is constructed, and a sample attention mechanism is introduced to ensure that in the process of generating the embedding vector, samples containing more working condition mode information have a higher weight. Multiple conditional layers are introduced into the basic classifier of meta-learning, and a feature linear modulation method is used to generate modulation parameters by calculating the embedding vector, and then the parameters of the basic classifier network are adjusted point by point, referring to the prior information of the working condition.

The invention relates to the technical field of industrial measurement, and discloses a collaborative automatic measurement system for industrial field multivariable features, comprising: an acquisition module for generating a sampling data stream with a synchronous timestamp generated by a hardware synchronous clock; the wave velocity calculation module is used for extracting the real-time deviation amount of the main variable sampling value relative to the reference value and determining the real-time wave velocity of the propagation medium by using a first-order linear modulation equation; the dynamic addressing module is used for calculating the propagation lag time length of the auxiliary variable relative to the main variable, and backtracking in the historical cache queue according to the propagation lag time length so as to extract the auxiliary variable historical sampling value with the aligned time sequence; according to the method, time axis accumulated drift errors caused by violent evolution of propagation medium attributes can be countered, and it is ensured that the system maintains extremely high measurement fidelity and physical stability under the full-dynamic working condition.

The invention discloses an RGB-D sight line tracking method fusing head postures, and belongs to the field of computer sight line estimation. Facial feature extraction adopts a RetinaFace network as a detection core, a detected human face region is cropped through an affine transformation strategy, a cropped image is input into a 3D Dense Face Alliance Version 2 (3DDFAV2) model after being normalized, and an eye RGB-D image block and a calculated head posture Euler angle are output through registration. In order to effectively integrate complementary information of RGB and a depth mode, a lightweight multi-mode sight tracking network is provided and is divided into three main levels: a feature extraction layer, a feature fusion layer and a regression prediction layer. According to the network, a double-branch multi-mode framework is constructed, five input modes including a binocular RGB image, a depth map and a head posture are processed respectively, Kalman filtering smoothing is utilized, a multi-mode information fusion strategy combining an attention and feature linear modulation (FiLM) mechanism is designed, and finally high-precision sight line regression is achieved through a multi-layer perceptron (MLP).

This invention provides a motion environment perception method based on biomechanical features and gait adaptation, belonging to the field of human-computer interaction perception. The method includes: acquiring the subject's original motion feature sequence and biomechanical feature vector; scaling and aligning the physical dimensions using a biomechanical scaling matrix to obtain a motion feature tensor; inputting the motion feature tensor and biomechanical feature vector into a multi-task temporal convolutional network for feature-level linear modulation to obtain modulated deep features; processing these features through a backbone network, with the classification branch outputting terrain categories and the regression branch outputting initial values ​​of environmental geometric parameters; freezing the backbone network parameters; fine-tuning the regression branch based on an adaptive loss function driven by gait phase and foot arch features to obtain a motion environment perception model; inputting real-time data from the subject; and outputting terrain categories and environmental geometric parameters. This invention solves the technical problem of low accuracy in environmental geometric parameter estimation caused by biomechanical differences between individual subjects in existing technologies.

The application provides an electromagnetic electrostatic double-drive Coppe filter chip and a preparation method thereof, and relates to the fields of optoelectronic chips and semiconductor technologies.The filter chip comprises a movable mirror body, a fixed mirror body, a permanent magnet, a PCB circuit board and a spacing layer.A first reflecting mirror, a first detection electrode, a second detection electrode, a first drive electrode and a second drive electrode are arranged on the end face of the movable mirror body.The first detection electrode and the first drive electrode are arranged on one side of the first reflecting mirror, and the second detection electrode and the second drive electrode are arranged on the other side of the first reflecting mirror.A permanent magnet slot for embedding the permanent magnet is arranged on the other end face of the movable mirror body.The application can overcome the problems of the single-drive Coppe filter chip, such as nonlinear modulation, limited tuning range and poor monochromaticity.

The invention relates to an ultrafast laser modulation and pulse shaping system based on a carbon nanotube array. The ultrafast laser modulation and pulse shaping system comprises a first carbon nanotube array structure module, a nonlinear modulation module, a second carbon nanotube array structure module, a pulse shaping module, an optical input module and an output coupling module, the first carbon nanotube array structure module is connected with the nonlinear modulation module; the second carbon nanotube array structure module is connected with the pulse shaping module; the nonlinear modulation module and the pulse shaping module are connected with the optical input module; and the optical input module is connected with the output coupling module. The highly-oriented carbon nanotube array is adopted as a core nonlinear optical modulation medium, efficient modulation and optimized shaping of ultrashort pulse laser are achieved by means of the self-saturation absorption effect, the multi-photon absorption effect and the nonlinear refractive index of the carbon nanotube array, and the device can be widely applied to the fields of ultrafast laser processing, optical communication, quantum optics and the like and has wide application prospects. And a new solution is provided for the development of the next generation of ultrafast laser technology.

The embodiments of the present application provide a method, device, and storage medium for measuring optical fiber temperature and / or strain, relating to the field of distributed optical fiber technology. The method comprises: obtaining a first high-frequency optical signal and a second low-frequency optical signal at any linear modulation frequency, and converting them into a first electrical signal and a second electrical signal; determining the average of the Brillouin attenuation and gain values ​​corresponding to the first electrical signal and the second electrical signal based on the central symmetry of BGS and BLS, and determining the measurement result of the temperature and / or strain of the optical fiber to be measured based on the Brillouin frequency shift determined from all the first average values. The present application utilizes the central symmetry of BGS and BLS to convert the high-frequency optical signal and the low-frequency optical signal obtained by the original SA-BOTDA into electrical signals and then average them, thereby suppressing the phase-to-intensity noise caused by the frequency shift of the high-frequency and low-frequency optical signals affected by the laser phase noise, and reconstructing the Brillouin gain spectrum, thereby reducing the impact of the laser linewidth on the signal-to-noise ratio.

The invention discloses a Transform runoff prediction method and system fused with an enhanced interannual climate memory mechanism, runoff prediction is carried out based on a runoff prediction network model of an encoder and a decoder of a Transform, and the method comprises the steps of obtaining dynamic data and static data of a to-be-studied drainage basin, carrying out continuous short-term window and long-term window division on the preprocessed dynamic data, and carrying out runoff prediction on the to-be-studied drainage basin. Forming a short-term dynamic input feature and a long-term dynamic input feature; compressing the static feature into a global vector by adopting an encoder, performing linear modulation on the global vector and the short-term dynamic input feature, and performing feature extraction to obtain a short-term feature sequence; carrying out sparse sampling on the long-term dynamic input features by adopting an encoder, and carrying out feature extraction to obtain a long-term feature sequence; fusing the short-term feature sequence and the long-term feature sequence by using an encoder to obtain a fusion feature of bidirectional information interaction; the decoder predicts runoff prediction features according to the fusion features, and a softplus activation function of the decoder is adopted for adjustment to obtain the runoff prediction features.

The application discloses a washing machine motor operation control method and system based on fundamental wave voltage control, and relates to the technical field of washing machine motor control. The method comprises the following steps: S1, offline calculation of a relationship curve between the fundamental wave voltage and the reference voltage in the over-modulation region and storage of the curve in the memory; S2, calculation of the fundamental wave voltage by using a field-oriented control algorithm, wherein the d-axis and q-axis components of the fundamental wave voltage and the stator voltage are obtained through a speed loop and a current loop; S3, if the fundamental wave voltage is in the linear modulation region or exceeds the over-modulation region, an SVPWM algorithm in a 120-degree coordinate system is used for modulation; if the fundamental wave voltage is in the over-modulation region, an over-modulation algorithm in the 120-degree coordinate system is used to calculate the three-phase voltage action time, and finally, the SVPWM algorithm is used for modulation. The application directly takes the fundamental wave voltage as a control target, avoids the uncertainty caused by the inconsistency between the reference voltage and the fundamental wave voltage, and thus realizes accurate control of the fundamental wave voltage which has an actual effect.