822 results about "Gaussian noise" patented technology

The invention relates to the technical field of data protection, and discloses a privacy protection type data joint modeling method based on federated learning, which comprises the following steps: acquiring local data to perform meta-feature extraction, calculating key statistics to characterize data characteristics, collecting meta-features, grouping the meta-features into similar feature clusters through spectral clusters, and carrying out feature clustering on the similar feature clusters; dynamically allocating and calculating resource weights according to the similar characteristic cluster scale and the equipment computing power; distributing a basic privacy budget according to the client type, calculating a local model accuracy rate and an intra-cluster level difference, dynamically adjusting the privacy budget, adding adaptive Gaussian noise based on the privacy budget, and adjusting gradient sensitivity of gradient calculation; verifying gradient compliance through zero knowledge, carrying out safe aggregation on gradients passing verification, optimizing a meta-model through a knowledge distillation loss function, and generating confrontation sample analysis to obtain a leakage risk value to identify knowledge leakage risks; sensitive neurons in the neuron sensitivity positioning element model are analyzed and calculated, directional noise is injected, and initial parameters are adjusted for initialization training.

The invention relates to the technical field of ocean data interpolation filling, in particular to a space-time deficiency filling method and system based on context association and physical guidance. The method comprises the following steps: acquiring seawater dissolved oxygen data and context data; multivariable space-time dependence extraction is carried out based on the obtained seawater dissolved oxygen data and context data; gaussian noise diffusion is carried out based on the obtained seawater dissolved oxygen data; noise prediction is carried out based on double-view space-time correlation; and the prediction error is constrained based on the joint loss function. According to the method, a physical consistency constraint mechanism based on a partial differential equation is introduced in a model training process, so that model output better conforms to a physical coupling rule among variables in a marine environment. The constraint effectively inhibits non-physical fluctuation possibly occurring in the interpolation result, enhances the physical credibility and interpretability of the result, and provides a more reliable data basis for subsequent scientific analysis and process modeling.

The invention discloses a mechanical arm path planning method based on Transform and a diffusion model, and belongs to the technical field of robots, and the method comprises the following steps: collecting environment information to generate a reference path, and constructing a training data set; a conditional diffusion Transform prediction network is constructed, features are extracted, and multi-modal path prediction is realized; gaussian noise is applied to the reference path, and denoising training is carried out on the conditional diffusion Transform prediction network; in combination with a diffusion model and a cost guidance mechanism, optimizing a noise path sampled from Gaussian distribution until a smooth collision-free path is generated; the candidate paths are evaluated, and a mechanical arm joint control instruction is generated; and the mechanical arm executes the planning track and carries out real-time sensing and online re-planning. According to the method, environment perception, Transform coding and diffusion generation are organically combined, a plurality of feasible tracks which are smooth and capable of avoiding obstacles are rapidly generated in a complex obstacle scene, and the method has good generalization ability and can adapt to different scenes and dimension changes.

The invention discloses a hydroelectric generating set operation fault data enhancement method and device, and the method comprises the steps: firstly collecting a set vibration signal, selecting a time-frequency transformation method to convert a one-dimensional vibration signal into a two-dimensional time-frequency image, enhancing the feature dimension of the signal, constructing a diffusion feature migration model, gradually disturbing the data distribution to Gaussian noise through forward diffusion, and carrying out the recognition of the Gaussian noise. The method comprises the following steps of: performing inverse denoising to generate simulation data highly similar to a real fault sample, realizing relevance learning and migration sharing of fault features among different working conditions in combination with an adversarial feature migration architecture, and finally evaluating an enhancement effect by calculating similarity among samples, and inputting enhanced data into a fault diagnosis model to verify precision improvement. Through the combination of time-frequency transformation and a diffusion model, sample scarcity and working condition barriers are broken through, a remarkable effect is shown in the aspects of expanding the fault sample scale and enriching the sample dimension, the similarity of generated data and a real sample is improved, the diagnosis precision is improved, and the model generalization ability is remarkably enhanced.

The invention discloses a graph neural network-based relevance credit default early warning method. The method comprises the following steps of S1, performing multi-source cross-mechanism data fusion and dynamic graph construction; s2, designing a space-time diagram neural network model to output a final risk score; s3, a federal learning framework: local training: locally training a sub-graph model by each participation mechanism, and retaining sensitive data; parameter aggregation: aggregating gradient information by the central server, and adding Gaussian noise by using differential privacy; model updating: the space-time diagram neural network model supports a heterogeneous graph structure through weighted average updating parameters; and S4, carrying out risk early warning and interpretability output. The method at least has the following beneficial effects: comprehensive risk coverage: dynamic heterogeneous graph construction: integrating multi-source heterogeneous data and dominant / implicit relationships, constructing a dynamic graph comprising enterprises, individuals and geographic nodes, dynamically adjusting edge weights through a time decay function, and quantifying timeliness of association strength;

The invention relates to a federated learning method, system and device for personalized differential privacy protection and a medium. The method comprises the steps that a central server initializes global model parameters and issues the global model parameters to clients; each client sets an initial value and an extreme value of a personalized privacy budget based on data characteristics of the client; the client performs local training, cuts the gradient in the training process, and adds corresponding Gaussian noise processing based on the current personalized privacy budget; the central server performs weighted aggregation on the model parameters uploaded by the clients to update a global model, and issues the updated global model parameters to the clients for a new round of local training; and the central server dynamically adjusts the personalized privacy budget of each client based on the reward factor, and then allocates the personalized privacy budget to each client for local training again until a global model meeting a preset requirement is obtained. The method can be widely applied to the field of distributed machine learning data security.

The invention relates to the field of signal processing, and discloses an FPGA-based dynamic multi-modal signal fusion algorithm and an adaptive noise suppression system, and the algorithm comprises a signal collection module which is used for collecting signals of different modals through a multi-modal sensor, adding a timestamp and a sensor identifier to each modal signal, and obtaining an original signal; the space-time calibration module is used for performing time synchronization and space external parameter calibration on the original signal and outputting a space-time aligned single-mode signal; and the noise suppression module is used for mapping high-dimensional signals to low-dimensional Riemannian manifolds through a manifold embedding algorithm for each single-mode signal aligned in time and space, and suppressing non-Gaussian noise by using a manifold thermonuclear filtering algorithm. Space-time labels are given to multi-mode signals by means of a signal acquisition module, time deviation and space position difference between sensors are eliminated through a space-time calibration module, and non-Gaussian noise is filtered out through a noise suppression module based on manifold learning and intrinsic characteristics of the signals are reserved.

The invention provides a vibration signal space-time reconstruction method based on a multi-modal condition diffusion model, and relates to the technical field of vibration signal reconstruction, and the method comprises the steps: firstly collecting structure vibration response through multiple sensors, constructing a multi-dimensional vibration signal matrix, and automatically recognizing a space continuous missing region and a time random missing region; performing coarse reconstruction on the missing region by adopting self-adaptive multi-scale interpolation so as to recover the basic trend and frequency band characteristics of the signal; a pseudo-missing mask is further applied to complete data, a training sample is constructed through a self-supervision strategy, and the model is guided to learn spatio-temporal correlation features and missing modes; in a training stage, a diffusion model is used as a generation framework, Gaussian noise disturbance is applied to a missing region, four types of condition embedding of time, space, trend and frequency domain are introduced in a denoising inversion process, signal periodicity, multi-sensor space coupling, low-frequency change and a physical frequency spectrum structure are respectively described, and the noise is reduced; and high-fidelity signal reconstruction under multi-modal information joint constraint is realized.

The invention provides a self-adaptive bearing fault diagnosis method based on multi-base wavelet fusion. The objective of the invention is to solve the problems of noise reduction, insufficient feature extraction and low diagnosis precision under noise conditions. A Kaisixi University bearing public data set is used as original data, and Gaussian noise with different SNRs is superposed to simulate various noise intensities. And uniformly carrying out length alignment, down-sampling, equal-length segmentation, division and normalization preprocessing. Then, wavelet bases such as sym4, db4, coif5 and the like are adopted for parallel multi-scale decomposition and reconstruction; and adaptively determining the number of decomposition layers and a threshold strategy according to the noise level, and generating a de-noising branch. And performing weighted fusion on the denoising results of the branches, and performing iterative denoising on the residual error. Signals subjected to noise reduction processing are sent to a double-branch convolution-cycle-attention network, a convolution layer extracts features, an LSTM and a self-attention module capture time sequence changes, and accurate recognition of various bearing faults is achieved. The training adopts a segmented attenuation learning rate and an early stop strategy, and the robustness and generalization ability of different SNR working conditions are improved.

The invention provides a wheat stripe rust remote sensing change information-driven early disease prediction method and system, and the system comprises a multi-source data progressive fusion module which enables a natural image and a multispectral image to be spliced step by step into corresponding hierarchical features, and carries out the fusion and outputting of deep fusion features; the frequency domain decoupling-based change detection module is used for outputting a change probability graph of adjacent moments; constructing a plurality of groups of training samples and inputting the training samples into a conditional diffusion prediction model for training; to-be-predicted wheat remote sensing image data and corresponding meteorological data are collected, a change probability graph is obtained, the change probability graph, the corresponding meteorological data and diffusion mode prior data are fused to serve as a condition vector, the condition vector and random Gaussian noise are input into the trained condition diffusion prediction model together for denoising, and prediction denoising data are obtained; then a prediction change probability graph is obtained through a visual decoder; and on the basis, obtaining a prediction result of the severity and distribution range of the wheat stripe rust disease on the d-th day. The method can be used for predicting the early wheat stripe rust.

The invention provides a federated learning differential privacy method based on Rayleigh divergence and adaptive noise adjustment, and aims to balance data privacy protection and model training performance and improve model accuracy and convergence speed of federated learning on the premise of protecting user data privacy. And the contradiction between privacy protection and model performance in the existing federated learning is solved. The method comprises the following steps: step 1, constructing a privacy loss quantification model based on Rayleigh divergence; 2, deducing a tight upper bound of a Gaussian noise standard deviation; 3, initializing noise parameters of the federated learning system; 4, the client side executes local model training and noise adding; 5, updating the aggregation model of the central server and evaluating the performance; step 6, implementing a self-adaptive noise adjustment decision based on model performance; and step 7, iterating federal learning training until convergence or completion.

The invention belongs to the field of image processing, and relates to a method for embedding a robust watermark in a diffusion model generated image, which comprises the following steps of: acquiring cue words, initial Gaussian noise and watermark information, and inputting the cue words, the initial Gaussian noise and the watermark information into a trained diffusion model based on watermark embedding to obtain a watermark-embedded image; the training process of the diffusion model comprises the following steps: acquiring cue words, initial Gaussian noise and watermark information, and inputting the cue words, the initial Gaussian noise and the watermark information into an encoder to obtain potential vectors; inputting the potential vector into a self-attention module to obtain an embedded position vector; embedding the watermark information into the potential vector according to the embedding position vector; inputting the potential vector embedded with the watermark information into a decoder to obtain an image embedded with the watermark; extracting watermark information from the image embedded with the watermark; updating parameters of a diffusion model according to the image embedded with the watermark and the extracted watermark information until a trained diffusion model is obtained; according to the method, the embedding position is selected by combining the potential of the diffusion model and the accuracy of the self-attention mechanism, so that efficient watermark embedding and extraction are realized.

The invention discloses a 12-lead electrocardiosignal generation method based on a medical text and related equipment. The method comprises the steps that text information is acquired; inputting the text information into a pre-trained TTE model to generate a 12-lead electrocardiogram signal; wherein the TTE model comprises an encoder, a noise predictor and a decoder; in an electrocardiogram generation stage, a noise predictor takes a text semantic condition as input, starts from initial random Gaussian noise zT, gradually recovers a potential feature vector # imgabs0 # pretrained decoder meeting conditional constraints through iterative denoising, reflects a potential feature vector # imgabs1 # to a high-dimensional original signal space, and outputs the potential feature vector # imgabs1 # pretrained decoder to a high-dimensional original signal space. According to the method, medical text description is used as condition input, text semantic constraints are embedded in a potential diffusion model framework, 12-lead simulated electrocardiosignals conforming to specific pathological features are generated, and a feasible alternative scheme is provided for shortage of current labeled ECG data sets.

The invention discloses an intelligent creative design system based on a diffusion model. The system comprises sketch generation, sketch optimization, style migration and high-quality rendering. According to the method, the design efficiency and innovativeness are improved through an intelligent technology, and meanwhile, the personalized requirements of users are met. According to the system, firstly, diversified design sketches are generated through a diffusion model, an initial sketch is generated through Gaussian noise step-by-step iteration, and keywords or style labels input by a user are introduced in combination with a conditional diffusion model so as to control the generation direction; performing feature extraction on the sketch by using a convolutional neural network, and optimizing lines and structures of the sketch through a multi-layer perceptron; then, in combination with a style migration technology, migrating an artistic style specified by a user into the sketch, and meanwhile, introducing an attention mechanism to ensure local detail consistency of style migration; and finally, performing high-quality rendering on the design by using a GAN, optimizing detail performance through adversarial training of a discriminator and a generator, and improving the image resolution by using a super-resolution technology.

The invention belongs to the technical field of seismic exploration, and discloses a robust full-waveform inversion method, system and device based on generative modeling, and a medium, and the method comprises the steps: obtaining seismic observation data; the method comprises the following steps: starting from random Gaussian noise, establishing a model space comprising a plurality of initial velocity models through an unconditional score model, and determining a global optimal initial velocity model of the model space by adopting a strategy search algorithm; de-noising is carried out on the global optimal initial velocity model based on back diffusion, seismological observation data are introduced as observation constraints, the velocity model is updated by calculating the mismatch gradient of forward modeling data of the current velocity model and the seismological observation data, and an implicit condition sample is obtained; performing forward diffusion processing on the implicit condition sample to obtain a speed model for a subsequent annealing time step; and when the annealing process reaches a preset condition, outputting a final speed model. According to the method, the robustness and accuracy of full-waveform inversion are improved, and the bottleneck problem of traditional full-waveform inversion is solved.

The invention relates to the technical field of identity authentication, in particular to a federated identity authentication method fusing mouse behavior modeling and self-adaptive differential privacy, which comprises the following steps: building a federated learning framework; an authentication problem is converted into a time sequence behavior modeling task; updating model parameters by each client, and calculating local gradient information of the current round; cutting the local gradient information; the privacy budget is dynamically distributed; gaussian noise is added to the cut local gradient information; the central server aggregates the disturbed local model parameters to obtain new global model parameters; when the model reaches the preset convergence standard or the maximum round limit after multiple communication rounds, training is ended, a global model terminal is deployed, mouse behavior modeling and a federal learning mechanism are combined, the collection requirement for original user data is avoided, the leakage risk of sensitive data in the transmission and storage process is fundamentally reduced, and the user experience is improved. And the data security and the privacy protection capability are improved.

The invention discloses a federal learning security training method and system based on differential privacy, belongs to the technical field of artificial intelligence, and aims to solve the technical problem of how to realize data privacy protection and model precision balance in cross-mechanism model training. Comprising the following steps: constructing a distributed structure comprising a plurality of clients and a server; each client constructs a noise variance calculation model by taking the gradient feature, the privacy budget coefficient, the data sensitivity level coefficient and the training stage coefficient as calculation parameters after each round of local training is finished, and Gaussian noise is calculated according to the noise variance; verifying the noise variance of each round, and adjusting the calculation parameters of the noise variance based on the comparison result of the accumulated budget consumption and the global privacy budget; dynamically allocating the data sensitivity level of the local training data and the budget allocation proportion of the client; and the server performs secure aggregation through a secret sharing algorithm, and distributes the updated global model parameters to each client.

The invention discloses an aero-engine combustion chamber ignition state prediction method and system based on deep learning. The method comprises the steps that a combustion chamber ignition parameter data set is collected; analyzing sample distribution to determine an augmentation amount, generating a new sample based on KNN interpolation, injecting adaptive Gaussian noise, and referring to physical relevance between a fuel-air ratio and a temperature-pressure ratio of a real sample during augmentation of a special working condition; an AttResVGG deep neural network is constructed, gradient disappearance is prevented by adopting residual connection, a multi-head self-attention mechanism is embedded to capture a long-range dependency relationship among parameters, and multi-scale features are integrated through a global feature fusion module; double classifier training is designed, a main classifier adopts category weighted cross entropy loss, an auxiliary classifier adopts label smooth loss, and model parameters are jointly optimized; and inputting working condition parameters for real-time prediction. According to the invention, parameter dependence of a traditional empirical model is broken through, and adaptive prediction of complex working conditions is realized; the calculation time consumption is reduced, and the real-time decision demand is met; and the device adapts to various combustion chamber structures, and re-modeling is not needed.

The present invention provides a context-enhanced image generation method, and a model training method and system, comprising: acquiring a training image data set; using a preset noise-adding mechanism to perform multi-timestep noise-adding on a training image, wherein a variance of Gaussian noise added at each timestep depends on a current timestep and progressively increases until a clean training image is transformed into standard Gaussian noise so as to obtain a noisy image at each timestep; using a random masking mechanism to generate a mask, and according to the mask, discarding a masked region of the noisy image and discarding an unmasked region of the clean image; and on the basis of reconstructing a discarded region using a non-discarded noisy image and the clean image, training a context-enhanced image generation model so as to obtain a trained image generation model. The present invention effectively improves the context comprehension capabilities of image generation methods, improves image generation quality, and achieves high-resolution diversified image generation.

The invention provides an accurate calculation method and system for the axial pretightening force of an ultrasonic bolt, and the method combines a high-order statistic theory with a nonlinear filtering technology, and solves the problem that the measurement precision of a conventional method is insufficient under the conditions of strong noise and nonlinearity. Particle filtering and Kalman filtering are combined, so that the problem that a traditional Kalman filtering method easily causes sensitivity of an initial value of filtering divergence is solved; a double-Gaussian attenuation model is adopted to fit an echo envelope, the problem that a single Gaussian or single index model cannot adapt to envelope distortion caused by stress change is solved, and a nonlinear state space model is established to adapt to nonlinear echo signal characteristics under dynamic stress; gaussian noise is effectively suppressed by using a third-order cumulant cross-correlation algorithm, the time difference resolution in a low signal-to-noise ratio environment is remarkably improved, and the Gaussian noise and asymmetric interference are effectively suppressed.

Methods for motion-controllable video diffusion include extracting optical flow fields from an input video and computing warped noise by iteratively warping noise between consecutive frames using the optical flow fields. The iteratively warping includes (i) re-Gaussianizing expanded pixel regions by sampling fresh Gaussian noise, and (ii) aggregating contracted pixel regions by merging noise particles and renormalizing variance to preserve spatial Gaussianity. An output video is generated by initializing a diffusion process with the warped noise and iteratively denoising to produce temporally coherent output frames. Various other methods, systems, and computer-readable media are also disclosed.

The invention discloses a cascading filtering positioning method and device in a complex and severe environment of a well mine and a medium, and achieves a high-precision positioning function of a well mine unmanned vehicle based on a cascading filtering method. According to the method, the IMU, the wheel speed and the wheel rotation angle are subjected to data fusion through adaptive Kalman filtering, the influence of non-Gaussian noise on wheel speed measurement is reduced through non-Gaussian noise feature extraction, and robust prior positioning information is obtained. According to the method, a regular term representing feature degradation is constructed, a laser radar observation model based on a prior map and laser radar data is constructed, and finally, a result of the laser radar observation model and prior positioning information are fused according to the regular term under a regularization Kalman filtering framework to obtain posterior positioning information. The precision and robustness of the method meet the positioning requirements of the mine unmanned vehicle.

The invention belongs to the technical field of image processing, and particularly relates to a crack image segmentation method and system. Gaussian noise is introduced into image data for enhancement processing, so that the robustness of the model to noise is improved; an encoder composed of three layers of progressively stacked residual units is adopted to extract crack features, and a DropBlock regularization module layer is matched to reduce feature redundancy and enhance feature diversity; the image spatial resolution of the crack features is recovered through a decoder symmetrical to the encoder, and the fine structure of the crack boundary is restored; and finally, optimizing the crack probability graph by adopting a dense conditional random field, and outputting a final crack segmentation graph. According to the method, the problem of foreground-background category imbalance is effectively relieved, the phenomena of false detection and missing detection are reduced, the segmentation performance of the model is improved, the method is particularly suitable for deployment of edge equipment such as unmanned aerial vehicles and mobile phones, and a crack recognition scheme with high precision, low complexity and high robustness is provided for infrastructure health monitoring.

The invention discloses a nuclear magnetic resonance free induction decay signal reconstruction method based on a diffusion probability model, and relates to the field of magnetic resonance spectrums.The method comprises the steps that a simulation data set is constructed based on a mathematical model of free induction decay signals, and the simulation data set is divided into a training set, a verification set and a test set; sampling each data set by adopting a Poisson gap non-uniform sampling strategy, and simulating an actual under-sampling condition; constructing a diffusion probability model, gradually adding Gaussian noise through a forward diffusion module to generate a noise-containing sample, and recovering an original signal from a noise signal through a reverse denoising module; and inputting test data into the optimal model for multiple times to obtain a plurality of reconstruction results. According to the method, the reconstruction method based on the diffusion probability model is constructed, training optimization is carried out through Poisson gap non-uniform sampling and simulation signal data sets, and high-precision reconstruction of the under-sampling nuclear magnetic resonance free induction decay signals is achieved.

The invention relates to the technical field of intelligent control and information security crossing, in particular to an anti-error data injection Kalman filtering method based on neural network enhancement, which comprises the following steps: firstly, constructing a neural network auxiliary correction module for identifying and correcting an extended Kalman filtering intermediate variable affected by an error data injection attack; secondly, designing a hybrid filtering architecture, and combining the learning ability of a neural network with the theoretical advantages of traditional Kalman filtering; and finally, realizing adaptive optimization of model parameters through a mixed training mechanism. Accurate state estimation can be achieved only through limited prior information, and meanwhile the calculation efficiency and interpretability of a traditional Kalman filter are kept. And the anti-interference capability of the system in a non-Gaussian noise environment is effectively improved. The application of the method on an induction motor model is obviously better than that of a traditional anti-error data injection scheme, and breakthrough progress is achieved in the aspects of estimation precision and robustness.

The disclosure deals with system and method for discerning the radius maximizing reliability for binary modulation on conjugate-reciprocal zeros (BMOCZ) implemented with both a maximum likelihood (ML) and direct zero-testing (DiZeT) decoder. The optimal radius for BMOCZ is disclosed to be a function of the employed decoder. The radius maximizing the minimum distance between polynomial zeros does not maximize the minimum distance of the final code. While maximizing zero separation offers an almost optimal solution for the DiZeT decoder, the ML decoder outperforms the DiZeT decoder in both additive white Gaussian noise (AWGN) and fading channels when the radius is chosen to maximize codeword separation. Different sequence-to-subcarrier mappings for BMOCZ-based orthogonal frequency division multiplexing (OFDM) are analyzed to highlight a flexible time-frequency mapping approach that avoids distortion introduced by a frequency-selective channel at the expense of higher peak-to-average power ratio (PAPR).

The invention particularly relates to an electroencephalogram data enhancement method based on a conditional diffusion model and a graph neural network. The electroencephalogram data enhancement method comprises the following steps: constructing an EEG signal reconstruction model; the EEG signal reconstruction model is trained, the two processes of noise diffusion and reverse denoising are included, and in the noise diffusion process, real EEG signals are subjected to T-step noise-by-noise superposition to be converted into pure Gaussian distribution; in the reverse denoising process, a U-Net model of an embedded graph neural network is used for conducting T-step denoising on Gaussian noise obtained in the diffusion process, and EEG signals are reconstructed; performing sampling by using the trained EEG signal reconstruction model to generate new EEG signal data; the generated EEG signal and the original EEG signal are mixed and input into an EEG decoder together, an original training set is expanded, and the performance of the EEG decoder is enhanced. According to the method, complex spatial-temporal characteristics of the EEG can be effectively captured by using the conditional diffusion model of the fusion graph network, and a high-fidelity EEG signal with neuroscience significance is reconstructed from Gaussian noise.

The invention relates to a training method for providing an underwater vehicle main body structure design model and an underwater vehicle main body structure generation method. The training method of the underwater vehicle main body structure design model comprises the following steps: firstly, generating a noise adding main body structure based on a randomly initialized ordinal number stamp k, a sample main body structure and randomly initialized Gaussian noise generation data; secondly, determining predicted Gaussian noise data based on the ordinal number stamp k, the performance parameters of the sample main body structure and the noise adding main body structure by adopting a main body structure design model; and finally, determining a training loss function based on the generated Gaussian noise data and the predicted Gaussian noise data, and training the main structure design model based on the training loss function. Under the condition that only few sample main body structures exist, enough training sample data used for main body structure design model training can be generated, and model training under a small amount of sample main body structure data is achieved.

The invention discloses a control system for intelligently detecting the lifting process of a tower crane. The control system comprises a data acquisition module, a self-adaptive frequency spectrum filtering module, an impact retention module, a motion time sequence feature construction module, a linear drift monitoring module, a nonlinear disturbance separation module, a threshold statistical decision module and a lifting process control module. The invention belongs to the field of intelligent control, and particularly relates to a control system for intelligently detecting the lifting process of a tower crane, which adopts a self-adaptive sampling method based on gradient triggering to improve the accuracy of fault detection. The control accuracy of the jacking process is improved by applying an optimized wavelet threshold function; extracting linear dynamic characteristics of a load current linear relation through a linear drift monitoring module; residual difference is constructed in the residual subspace, nonlinear energy indexes and errors are calculated, the method is extremely sensitive to nonlinear disturbance and non-Gaussian noise, and a blind area of linear analysis is reinforced; and the control effect of the tower crane lifting process is improved.

The invention relates to the technical field of deep learning, in particular to a radiotherapy plan dose distribution verification method based on deep learning, and the method comprises the following steps: collecting historical radiotherapy plan data, generating a physical reference dose field through a Monte Carlo algorithm, unifying the voxel resolution of an anatomical structure to 1 cubic millimeter, and normalizing the dose according to a prescription, data enhancement is carried out only by adopting translation and mirror transformation, trace Gaussian noise is added, and a physical information enhanced three-dimensional training data set is constructed. According to the method, a three-dimensional convolutional network is utilized to automatically learn a dose distribution rule of a historical high-quality plan, a physical constraint module is embedded to ensure that a prediction result accords with a radiology principle, a real-time clinical rule engine is combined to instantly identify and correct a violation hot spot cold region, and an uncertainty quantification technology is assisted to position a high-risk region, so that the accuracy of a prediction result is improved. Finally, minute-level full-automatic verification is achieved, executable optimization suggestions are output, and efficiency is improved by dozens of times while safety is improved.