20results about How to "Avoid fit problems" patented technology

The application discloses a lining cloth dyeing control method and system based on artificial intelligence, and is used for the control field, and the method comprises the following steps: real-time monitoring of multi-source data in the lining cloth dye vat, capturing the video stream inside the lining cloth dye vat; using a Gaussian mixture variational autoencoder model to extract features from the multi-source data, obtaining multi-source feature data; obtaining the changing moment of the fabric state in the video stream, and extracting the fabric state change feature data according to the observed fabric color change and the heterogeneity of the dye distribution in the video stream; fusion of multi-source feature data and fabric state change feature data; introducing a model predictive control layer, predicting the result to adjust the dyeing parameters in real time; establishing an intelligent feedback mechanism. The Gaussian mixture variational autoencoder model is trained and optimized through the small-batch stochastic gradient descent and back propagation algorithm, important features are learned and captured from the data.

The utility model discloses an assembly for manufacturing a tire section mold, which comprises a pattern sheet assembly which is integrally formed by 3D printing; the pattern sheet assembly comprises a framework, a continuous sheet-shaped pattern sheet main body with a set thickness and a plurality of sheet-shaped blades with a set thickness, the pattern sheet main body extends upwards from the peripheral surface of the framework, and the plurality of sheet-shaped blades extend from the pattern sheet main body to the two sides and are of an integrated structure through 3D printing. The surface welding procedure of the matching surface of the combination part in the subsequent procedure can be omitted, and the subsequent welding procedure and polishing procedure can be reduced or omitted to a great extent; the problem of matching of the assembly parts of a plurality of original parts is avoided, and displacement caused by deformation of the assembly structures of the adjacent parts due to temperature change in the casting production process is reduced; in addition, corresponding procedures are reduced, so that the manufacturing period can be shortened to a great extent.

This invention discloses a method and system for large-scale urban village extraction based on a token mask mechanism, relating to the technical field of remote sensing geographic information systems. The method includes: acquiring a high-resolution remote sensing image dataset; labeling urban village areas to obtain an urban village labeled dataset; performing data augmentation on the urban village labeled dataset to obtain an augmented urban village labeled dataset; iteratively training a constructed urban village extraction model; setting training parameters; updating the model parameters of the urban village extraction model to obtain a trained urban village extraction model; acquiring a high-resolution remote sensing image of the area to be detected; inputting the trained urban village extraction model; and extracting the urban village areas within the area to be detected. This invention can quickly and accurately identify urban village areas from high-resolution remote sensing images, with high accuracy and efficiency, wide application range, and realizes high-precision and robust large-scale urban village mapping applications.

The utility model provides a double-inlet full-specification cigarette stacking device which comprises a first cigarette inlet conveying line, a second cigarette inlet conveying line, a film wrapping machine and a thermal shrinkage furnace, a first cigarette inlet belt is arranged on the first cigarette inlet conveying line, and a first lifting cigarette inlet mechanism and a second lifting cigarette inlet mechanism are correspondingly arranged on the first cigarette inlet belt. A cigarette stacking device is correspondingly arranged at the position of a cigarette outlet of the first cigarette inlet belt, and a cigarette pushing device is arranged on a cigarette receiving structure of the cigarette stacking device. The cigarette stacking machine has the advantages that the problem that in the prior art, a cigarette stacking machine can place stacked cigarette boxes into a conveying belt to be conveyed only by depending on other equipment is solved. In the scheme, a plurality of cigarette receiving bin structures are adopted in the direct extraction type cigarette stacking device, the direct extraction type cigarette stacking device can be directly in butt joint with the conveying belt to arrange the cigarette boxes into regular cigarette box layers, and therefore the direct extraction type cigarette stacking device is suitable for the special-shaped cigarette boxes, and the problem that the cigarette boxes are damaged due to the fact that the cigarette boxes are not easy to arrange in an existing cigarette stacking machine and are clamped or fall off is solved.

This application relates to the field of robotics, and in particular to a training data acquisition device for robot gripper control. The training data acquisition device mainly comprises four core modules: the acquisition device body, an image acquisition module (51), a motion detection module (52), and a data processing module (53). In use, the operator holds the device, aligns the mechanical finger (2) with the object to be grasped, and presses the handle (3) on the acquisition device body to control the mechanical finger (2) to close and grasp the object. During this process, the image acquisition module (51) records visual information, and the motion detection module (52) records the degree of finger opening and closing. The data processing module (53) aggregates these two types of heterogeneous data. Thus, the hand-eye coordination process of a real robot can be simulated at extremely low hardware cost, and key matching data is provided for robot imitation learning.

This utility model belongs to the field of trolley technology, and specifically relates to a trolley that facilitates straight-line travel. It includes a balance scooter and a trolley body, connected by a connecting frame. The connecting frame is detachably connected to the trolley body and rotatably connected to the balance scooter. The balance scooter has a positioning groove, and the connecting frame has a positioning element and an elastic element. The elastic element drives the positioning element to engage with the positioning groove, fixing the balance scooter and the connecting frame relatively, thus allowing the trolley to maintain straight-line travel or maintain a certain angle of rotation. Under external force, the positioning element can move relative to the positioning groove, allowing the balance scooter and the connecting frame to rotate relative to each other, thus enabling the trolley to turn flexibly. The purpose of this utility model is to provide a user-friendly trolley that facilitates straight-line travel.

The invention discloses a voiceprint and vibration signal-based elevator health state prediction method and system, and the method comprises the steps: collecting a sound signal and a vibration signal through a sensor group; respectively extracting voiceprint features and vibration features of voiceprint and vibration by using a double-branch convolutional neural network, obtaining a first enhanced feature and a second enhanced feature through a cross attention mechanism, and splicing to obtain a multi-modal fusion vector; and finally, inputting the multi-modal fusion vector into a long short-term memory network to output a health score sequence and the remaining service life of a future time window. Through modal complementation realized by a cross attention mechanism and a physical-data dual verification mechanism, environmental interference and real faults are effectively distinguished; clear physical constraints (such as monotonicity constraints and fatigue crack propagation rules) are fused into the deep learning model, so that the model is prevented from being over-fitted, and a prediction result is more in line with a mechanical principle.

This invention relates to the field of image recognition and discloses an image segmentation method for dynamic backgrounds, comprising: acquiring an image sequence containing a target frame and historical frames; extracting feature vectors corresponding to each pixel position to form a spatial feature tensor; calculating the second-order temporal variance of the feature vectors within a sliding time window to construct a spatiotemporal divergence matrix characterizing the intensity of fluctuations; using the global median of the spatiotemporal divergence matrix to correct the discrimination threshold and determine a feature decay weight matrix whose weight values ​​are negatively correlated with the second-order temporal variance; multiplying the weight matrix with the target frame at the pixel level to suppress the feature activation amplitude in high-frequency oscillation regions; and outputting a foreground segmentation mask using the processed feature tensor. This invention utilizes the statistical properties of feature fluctuations to decouple the physical quantities of foreground displacement and background oscillations, effectively blocking the transmission of non-rigid background noise and improving the segmentation sharpness and recognition robustness of object boundaries in complex environments.

The present application relates to the technical field of neural network, especially to a low back pain anomaly analysis method based on weighted graph convolution network, comprising data preprocessing; constructing graph network and graph convolution; adding self-attention-based feature weighting mechanism; introducing feature importance mask; using the feature importance mask to select features with special attributes for model adjustment, the feature mask retains the top important features and clears the positions of other features. m The present application reduces the influence of uneven sample category distribution on the prediction result; represents the physiological properties of the spine anomaly as a weighted feature map, mines the potential relationship between patients through edge connection, dynamically captures the importance of biological properties to different patients through node internal feature weighting; calculates the feature importance according to the information amount and correlation of the features, and generates a feature mask guided by the feature importance, realizes feature sparsification while strengthening important features, and avoids small sample learning overfitting.

The utility model relates to the technical field of sagger charging, and discloses a sealed charging system which is characterized in that a charging mechanism is arranged at a discharge port of a stock bin device; the sagger supply device is arranged on the rack; the sagger supply device is connected with the charging mechanism, and a sealing door is arranged on the sagger supply device; the stock bin device is used for supplying materials to the loading mechanism, and the loading mechanism is used for loading the saggers conveyed by the sagger supply device; the vacuum pipe is arranged on the sagger supply device and is used for vacuumizing the sagger supply device; the vacuum dust removal and collection device is arranged at the bottom of the sagger supply device and used for sucking dust-containing air in the sagger supply device under negative pressure, filtering the dust-containing air and then collecting dust. According to the technical scheme, the sagger supply device has the sealing function, material leakage is effectively prevented, and the cleanliness of the production environment is guaranteed. The vacuum dust removal and collection device can be started in time to suck away air in the sagger supply device and then filter the air, sagger charging can be matched with vacuumizing and negative pressure dust collection, and the production efficiency of sagger charging is greatly improved.

The present application relates to the technical field of deep learning, in particular to a novel deep learning method and device based on symmetric cross compound training, which specifically comprises: obtaining first sample data, dividing the first sample data into a first training set, a first validation set and a first test set, and further dividing the first training set into a first data set and a second data set; taking the first data set and the second data set alternately as a training set and a validation set to perform symmetric cross training, obtaining outlier samples and true samples; performing compound training on the outlier samples and the true samples, simultaneously performing output correction classification, and obtaining a first optimal model. The present application changes the traditional deep learning training method mode, uses the function of deep learning feature extraction to automatically identify and separate the outlier samples in the training set, and further improves the model performance.

This invention belongs to the field of automatic measurement in civil engineering, and relates to a method for column dimension measurement and corner point localization based on UAV technology and computer vision. The method includes: acquiring orthophotos of the engineering site; coarsely locating the columns in the orthophotos based on a target detection model and marking coarse positioning boxes; segmenting the columns according to the coarse positioning box information to obtain single-column datasets, extracting the column inner contours from the single-column datasets, and calculating the column dimensions and corner point coordinates; fusing the coarse positioning box coordinates and column corner point coordinates to obtain the image coordinates of the column in the orthophotos; and performing an affine transformation to obtain the column world coordinates. This invention completes the dimension measurement and corner point localization of columns at the engineering site through a technical route of "coarse positioning → contour extraction → joint precise positioning," ultimately achieving column localization. It systematically solves the problems of insufficient positioning accuracy of rotated columns, difficulty in achieving sub-pixel accuracy of contours, and low measurement efficiency at the engineering site in traditional methods.

The application relates to an industrial process fault diagnosis method, and discloses an industrial process fault diagnosis method based on a graph convolution network of space-time fusion, which comprises the following specific process: collecting fault data in an industrial production process, then performing z-score standardization processing and sliding window interception on the fault data to obtain a node feature matrix, and inputting the node feature matrix into a ST-Res-GCN model which has been trained and tested to obtain a fault classification result; the application proposes an industrial process fault diagnosis model based on a graph convolution network of space-time fusion, which can automatically acquire and deeply mine space-time fusion features in data, and effectively improves the fault diagnosis rate.

The application relates to the technical field of mechanical design, and provides a fluid thermal power machine design optimization method and device and electronic equipment. The method comprises the following steps: acquiring small sample data corresponding to a low-dimensional design space of a fluid thermal power machine; for each optimization index, a regression model with high-order base functions between the optimization index and multiple design parameters is constructed; the regression model is trained based on the small sample data until a target function of the regression model is minimum, and the regression model at this time is determined as a prediction model of the optimization index; the target function comprises a prediction error term obtained based on the high-order base functions and an L1 regularization term; and the fluid thermal power machine is designed and optimized based on the prediction model. When multiple design parameters, multiple performance indexes and small sample data are faced, the application can consider the prediction accuracy, model generalization ability, physical interpretability and optimization efficiency, and thus comprehensive optimization design parameters meeting engineering constraints can be efficiently and accurately obtained.

The application provides a docking mechanism small cavity leak rate test tool and test method related to the field of leak rate test, which comprises an upper head assembly and a leak detection pipeline assembly, the leak detection pipeline assembly is connected to the upper head assembly, the upper head assembly is sealed and locked with a docking mechanism product to be tested to form a small cavity space, the leak detection pipeline assembly inflates the small cavity space of the docking mechanism product to be tested, and the internal pressure of the small cavity is tested in real time. Through the design of the upper head assembly, the setting of the pre-tightening force of the docking lock mold simulation part hook, the functional performance of the passive part of the docking mechanism is fully simulated, the cooperation of the active and passive parts of the docking mechanism during the small cavity leak rate test is avoided, the complexity of the test tool system is effectively simplified, the operation process is simplified, and the operation difficulty is reduced. Through the series connection of the gas pressure sensor in the leak detection pipeline, the product pipeline pressure is monitored in real time, the difficulty of the leak rate test is reduced in the mechanical and electrical combination mode, and the reliability of the leak rate test is ensured by the reverse installation of the stop valve to ensure the air tightness after being closed.

This utility model provides a three-inlet, double-flip, drop-type cigarette stacking machine for all specifications. A flip-flip cigarette stacking machine packaging line is located on each side of the cigarette stacking transport platform, and a drop-type cigarette stacking machine packaging line is located at the top of the platform. Drop-type cigarette stackers are installed on these packaging lines. Multiple cigarette-supporting devices are vertically and equidistantly arranged on the frame of each drop-type cigarette stacker. Each cigarette-supporting device has two symmetrically opening and closing cigarette-supporting plates, which are slidably connected to the device. A drive unit on each device works in conjunction with the plates. The frame of the drop-type cigarette stacker has a smoke inlet, positioned to match the uppermost cigarette-supporting device. The advantage of this utility model is that it overcomes the problem of existing cigarette stacking machines requiring additional equipment to place stacked cigarette boxes onto a conveyor belt for transport. The multiple cigarette-receiving compartments allow direct connection to the conveyor belt, organizing the cigarette boxes into neat layers.

The invention relates to the field of algorithms, in particular to a time-interleaved ADC (analog-to-digital converter) inter-channel mismatch correction method, which comprises the following steps of: iterating the mismatch of each channel to obtain the mismatch of each channel relative to a standard value, and returning the mismatch value to the data of the channel to finish the mismatch correction. For a single-channel ideal and non-offset ADC and a quantized single-frequency sine wave, the mean value of quantized output data of the ADC is close to 0, actual ADC offset Oi of a certain channel is calculated through an iterative formula, the essence of the Oi is the mean value of the quantized output data of the channel, the Oi is returned to the data of the channel, the mean values of the quantized output data of all channels are adjusted to be 0, and the offset Oi of the ADC is calculated through the iterative formula. And the offset of the time-interleaved ADC can be completed. According to the method for correcting the mismatch between the time-interleaved ADC channels, almost no complex multiplication and division method is used for calculation, the calculation complexity is reduced, and meanwhile the good correction effect is achieved.

The invention relates to a pavement service performance self-optimization prediction method, which comprises the following steps: acquiring pavement vibration data, extracting vibration features, constructing a time sequence feature tensor as model input, and constructing a feature decoupling multi-head deep network model by taking a vibration time domain root mean square as model output; dividing the current pavement vibration data into a teacher stage and at least one increment stage, calculating a comprehensive drift score of a single vibration feature, and calculating a drift score of a single sample based on the comprehensive drift score; a teacher stage data training model is used as a basic teacher model, incremental learning is carried out by using data in an incremental stage to obtain a student model, and a loss function of incremental learning is combined with a drift score of a single sample for construction; outputting a prediction result by using the student model; and when new data is accumulated, taking the student model as a new basic teacher model, and carrying out incremental learning by utilizing the accumulated new data so as to realize continuous evolution of the model. Compared with the prior art, the method has the advantages of long-term and accurate prediction and the like.

The application relates to the technical field of image recognition, in particular to a metal particle motion pattern recognition method and device in a direct current GIS (Gas Insulated Switchgear), wherein the method comprises the following steps: acquiring a motion pattern of metal particles in a direct current GIS (Gas Insulated Switchgear); then carrying out denoising processing on the motion pattern to obtain a denoised image meeting preset processing conditions, and generating a standardized image meeting preset standard conditions according to the denoised image; extracting deep morphological features of the standardized image; and recognizing the speed and acceleration of the metal particles in the motion pattern according to the deep morphological features. Therefore, the problem that in the related art, the direct current GIS (Gas Insulated Switchgear) is a unipolar electric field, the metal particles do high-speed accelerated motion under the action of the continuous electric field force, and the human eye observation mode is difficult to apply and the metal particle motion state is difficult to accurately recognize is solved.

The application belongs to the technical field of crop disease and pest identification, and discloses a wheat disease and pest intelligent identification method and an intelligent identification system based on meta learning. The collected leaf and stem images of the wheat to be identified are subjected to filtering processing by using an adaptive Wiener filtering method; the filtered leaf and stem images of the wheat are subjected to gray scale processing to obtain the gray scale images of the leaf and stem of the wheat; the filtered gray scale images of the leaf and stem of the wheat are subjected to enhancement processing by using a cross line gray scale image definition improvement model; and the diseases and pests, symptoms, disease spots and pest body characteristics of the leaf and stem of the wheat are identified according to the enhanced images. The application can be used in agricultural actual production, can provide a reference for further chemical control and biological control of wheat diseases and pests, and can provide a reference for the application of automatic distinguishing and identifying plant stress.