19results about How to "The solution is limited" patented technology

The invention relates to the technical field of re-hooking robots, in particular to an automatic unhooking and re-hooking robot for a car dumper, which comprises a moving carrier, a first motor is arranged at the top end of the moving carrier, an electric push rod is arranged at the output end of the first motor, a support is arranged at the output end of the electric push rod, and an adjusting assembly is arranged in an inner cavity of the support. A connecting plate is arranged at the top end of the adjusting assembly, a supporting rod is arranged at the front end of the connecting plate, reinforcing ribs are arranged on the supporting rod and fixedly connected with the connecting plate, and a rack is arranged at the front end of the supporting rod. The automatic unhooking and rehooking robot of the car dumper solves the problems that in the prior art, an automatic unhooking and rehooking robot of the car dumper is not provided with a positioning structure in the actual use process, operation is interrupted due to the fact that the moving situation often occurs in the rehooking and unhooking processes, and the shape of a clamping jaw is fixed; the problems that hook bolts in different shapes cannot be stably clamped, and limitation is large in the using process are solved.

This utility model relates to the field of sealing structure technology, specifically an adaptive sealing structure, including a cylinder. A fixing plate is fixedly sleeved on the outside of the cylinder. Both sides of the fixing plate are connected to L-shaped sealing gaskets, which are fitted onto the outside of the cylinder. Several springs are arranged on both sides of the outer wall of the fixing plate, with the other end of each spring fixedly connected to the side of the sealing gasket parallel to the fixing plate. A rigid metal plate is embedded inside the side of the sealing gasket parallel to the fixing plate, and a flexible rubber gasket is located on the side of the sealing gasket perpendicular to the fixing plate. Multiple fixing components are arranged around the circumference of the outer wall of the cylinder. This adaptive sealing structure solves the problem that existing sealing structures lack adaptive capability, and in actual use, thermal expansion and contraction can affect their sealing performance, leading to pipeline leaks and significant limitations in application.

The invention relates to the technical field of computers, in particular to a data classification and grading method and device, and the method comprises the steps: obtaining target data corresponding to a target file, and an application scene of the target file; performing label matching on the target data according to a preset matching rule corresponding to the application scene to obtain a label set; and classifying and grading the label set according to a preset classification and grading rule corresponding to the application scene and the sensitivity level set to obtain a classification and grading result corresponding to the target file. The problem that an existing classification and grading method is greatly limited to application scenes is solved, and the flexibility of data classification and grading of the application scenes is improved.

This invention belongs to the field of multi-beam antenna arrays and discloses a filtered multi-beam array and method based on a four-channel filter jumper. By integrating a microstrip-to-substrate integrated waveguide structure, a filter Butler matrix containing a four-channel filter jumper, and an antenna array on a first metal layer, a filtered multi-beam array based on a multi-layer planar structure is constructed. The four-channel filter jumper utilizes multiple resonant cavities and coupling structures, and by precisely controlling the coupling phase between the resonant cavities, a preset phase shift is directly introduced while achieving the filtering response, thus integrating filtering and phase-shifting functions into a single device. This design avoids the complex structure of filter couplers and the additional losses of filter phase shifters in traditional integrated schemes, significantly simplifying the device architecture, reducing transmission loss, improving system integration and beamforming accuracy, and effectively meeting the urgent needs of 5G and future 6G communication systems for high performance, low loss, and high integration.

This invention discloses a self-balancing group control system for elevator energy, relating to the field of intelligent elevator technology. It includes: a multi-agent scheduling layer, a multi-dimensional perception layer, a simplified energy storage buffer layer, a loss feedback layer, and elevators. The multi-dimensional perception layer is used to collect real-time data on pedestrian flow within the building, the operating status data of multiple elevators, and the energy storage status data of the energy storage device. The multi-agent scheduling layer is used to construct each elevator as an independent intelligent agent. The simplified energy storage buffer layer includes the energy storage device and is used to initiate charging and discharging for buffering. The loss feedback layer is used to monitor the loss data of the core components of each elevator in real-time and feed it back to the multi-agent scheduling layer. This invention achieves real-time data flow through a closed-loop architecture, forming a self-optimizing cycle of data, decision-making, execution, feedback, and iteration, which can solve the problem of limited overall efficiency caused by the independent operation of each link.

The present invention is a method for detecting abnormal sounds that uses metadata to generate audio to predict unknown abnormalities, which solves the problem that the abnormal audio samples of the target machine are invisible to the existing abnormal sound detection model, resulting in the inability to select hyperparameters for the abnormal sound detection model and the limited performance of the abnormal sound detection system. The abnormal sample prediction strategy provided by the present invention uses the alignment of audio features and metadata information features to construct an audio generation method based on metadata information, and then predicts the abnormal sound characteristics of unknown target machine types, and generates predicted sound samples of the target machine that can be used for model training in the First-Shot scenario for the existing abnormal sound detection method. With the help of the predicted sound samples, the abnormal sound detection method can determine the optimal model in complex real-life scenarios for abnormal sound detection, thereby improving the performance and versatility of the abnormal sound detection method. At the same time, the unified sample generation model constructed by the present invention alleviates the problem of scarcity of abnormal samples in real-life scenarios, and effectively reduces the difficulty of industrial deployment of the abnormal sound detection method.

This invention discloses a few-sample inductor core surface defect detection method based on model interaction, belonging to the fields of machine vision and industrial defect detection technology. The method includes the following steps: S1 Data acquisition and image preprocessing, constructing normal samples, labeled samples, and unlabeled samples; S2 Constructing an unsupervised statistical model based on statistical learning; S3 Constructing a supervised semantic segmentation model; S4 Simultaneously inputting the inductor core image to be detected into both the unsupervised statistical model and the supervised semantic segmentation model for processing, generating segmentation results; S5 Quantifying the differences in detection results; S6 Updating the parameters of the unsupervised statistical model; S7 Generating pseudo-labels based on the unsupervised statistical model; S8 Updating the weights of the supervised semantic segmentation model; S9 Using batches of processed images to be detected, inputting them into the updated unsupervised statistical model and the supervised semantic segmentation model for detection, obtaining detection results, and analyzing and calculating system performance indicators.

An improved differential evolutionary sparse distribution optimization method based on virtual interference patterns is proposed. This invention relates to sparse distribution optimization methods for spaceborne antennas. The purpose of this invention is to solve the problem that the limited number of uniform phased array elements due to the limited load of spaceborne antennas restricts the antenna performance. The process is as follows: 1. Create an initial population, with each population having N... p An individual represents one of the M radii corresponding to the antenna array ring; the population represents the N radii corresponding to the antenna array ring. p ×M radii; N p For all individuals in a population; 2. Calculate the fitness value of each individual; Fitness value F(d1,d2,...,d...) N The value is defined as the average of the main lobe and side lobe gain ratios on the plane and the θ = 0° plane; III. Repeat step II to obtain N. p The population is analyzed by first determining the fitness value of each individual, then retaining the individual with the optimal fitness value; fourth, a differential operation is performed on the population to obtain the optimal individual, which is the optimal antenna array. This invention is applicable to the field of aerospace technology.

The present application relates to the technical field of skin bag material, and particularly relates to a weather-resistant elastic composite skin bag material and a preparation method thereof, which is prepared from the following components: 65-85 parts of elastic matrix resin, 10-20 parts of elastic reinforcing agent, 4-9 parts of weather-resistant agent, 1-4 parts of plasticizer, 0.5-2.0 parts of antioxidant, and 0.3-1.0 parts of lubricant. The present application designs a multi-component synergistic composite weather-resistant agent to achieve all-round weather resistance: abandoning the single action mode of traditional small-molecule weather-resistant agents, adopting bentonite, boron nitride and nano-silicon carbide as physical barrier phases, lanthanum oxide as a chemical stable phase, chitosan and sodium silicate as interface compatible phases, and nano-cellulose agent as a dispersion reinforcing phase.

The utility model provides a shear bar machining jig frame for steel bar machining. The shear bar machining jig frame for steel bar machining comprises a bearing frame, a first cross beam is arranged in the middle of the upper surface of the bearing frame, the two ends of the first cross beam are fixedly embedded into bearing frame rods correspondingly, and the first cross beam is slidably sleeved with four sets of adjusting columns; the two ends of the second cross beam are fixedly embedded into the bearing frame rods correspondingly, and four sets of balance columns penetrate through the second cross beam in a sliding mode. The adjusting device is composed of the bearing frame, the adjusting columns and the balance columns, the adjusting columns are arranged in the middle of the bearing frame and can slide front and back, the adjusting blocks are installed on the adjusting columns, the adjusting blocks can be freely adjusted up and down on the adjusting columns, and by adjusting the positions of the adjusting columns and the positions of the adjusting blocks on the adjusting columns, the adjusting columns can be adjusted freely. The adjusting blocks can form different structures, the shear ribs penetrate through the adjusting blocks to form structures corresponding to the adjusting blocks, and limitation of the device is effectively reduced.

The present invention discloses a ship classification method based on multi-source information feature-level fusion. First, the HRRP and SAR raw radar data of the ship target are obtained, and the HRRP raw radar data are preprocessed to generate HRRP samples in two formats. Secondly, feature extraction is performed on the HRRP samples in the first format to form traditional features. Then, a temporal network model branch is constructed, and the HRRP samples in the second format are used as input, and traditional features are embedded to output mixed features; a two-dimensional convolution model branch is constructed, and the SAR raw radar data is passed through a lightweight CNN network to extract the ship target structure features. Finally, a feature fusion module of the attention weighted mechanism is designed to weightedly fuse the mixed features with the ship target structure features, and output the ship classification result. The present invention solves the problem of limited recognition accuracy and performance of automatic target radar recognition based on single modal data, and improves the classification accuracy.

The application discloses a kind of magnetic drive rotor twin control methods based on closed loop optimization, comprising: step 1, collection magnetic drive rotor operating data and utilize synchronous clock signal to carry out time stamp alignment and data fusion, generate synchronous data vector;Step 2, based on synchronous data vector and preset digital twin model, through error function identification key time-varying parameter estimated value, according to key time-varying parameter estimated value update digital twin model;Step 3, prediction is carried out using updated digital twin model, and prediction information package is obtained;Step 4, according to prediction information package, the deviation of the prediction performance and the actual performance of digital twin model is calculated, dynamically adjusts control strategy, generates collaborative control instruction;Step 5, execute collaborative control instruction, drive magnetic drive rotor to operate, optimize digital twin model, return step 1 and form closed loop control cycle.

The invention discloses a signal processing method based on MS-Dynp inflection point detection and AW-GGS segmentation, and the method comprises the steps: firstly, selecting a mechanical part which needs fault diagnosis, and recording a full-life-cycle vibration signal of the selected part through one or more direction sensors; then calculating an inflection point of the statistic sequence, and judging a fault occurrence point of the selected part; setting an adaptive window, and preliminarily segmenting the fault signal to obtain beta preliminary segmentation windows; and finally, according to signal distribution, by taking a maximized objective function xir as a criterion, carrying out re-segmentation on the preliminary segmentation window to obtain theta final segmentation windows, and completing processing of vibration signals of the selected component, including detection of fault occurrence points and segmentation of fault signals. According to the method, the quality of a fault data set is improved, and a theoretical basis and technical guidance are provided for mechanical fault diagnosis.

The disclosed embodiments provide training and application methods, devices, electronic devices and media for pre-trained models. The training method includes obtaining a sample set for training, the sample set includes an image, image description information corresponding to the image and text information in the image; inputting samples in the sample set into the pre-trained model to obtain image features corresponding to the image, fusion features corresponding to the text information and description features corresponding to the image description information, the pre-trained model is a model with visual language understanding and generation capabilities, the fusion features are features that fuse the image features of the image and the text information corresponding to the image; based on the image features, the fusion features and the description features, the pre-trained model is trained. The effect of unified visual language understanding and generation tasks is improved.

This invention discloses a shape control structure for thin-walled cavity parts made of titanium alloy based on laser additive manufacturing, its design method, and its application. The design scheme includes the following steps: modeling the shape control structure of the cavity part in 3D modeling software for additive manufacturing; then constructing a solid volume block with an inner wall adapted to the shape control structure within the cavity part shape control structure; further dividing the solid volume block into multiple discontinuous thin blocks of the same thickness and spacing; and importing the thin blocks and the cavity part shape control structure into 3D printing software. The structure constructed through this design method can more easily realize the generation of multi-scenario supports, break through the constraints of support structures, and can also exist as a shape control structure in the manufactured parts. Furthermore, while meeting the requirements for controlling the deformation of the parts, it also makes the supports easy to remove, effectively improving the manufacturing quality and production efficiency of the parts.

The invention belongs to the technical field of medical instruments, and relates to a domino connector set for spinal surgery, which comprises an angle and height adjustable domino connector and an on-nail domino connector, the angle and height adjustable domino connector comprises a first domino body (1) and a second domino body (2), the first domino body (1) is connected with one end of the first connecting body (3) through the locking pin (5) and can rotate and swing relative to the first connecting body (3), and the second domino body (2) is connected with the other end of the first connecting body (3) through the second connecting body (4) and the clamping ring (6) and can rotate and adjust the height relative to the first connecting body (3). The first domino body (1) and the second domino body (2) can achieve horizontal distance adjustment, and a domino connector on the screw is connected to a pedicle screw. A new rod and an old rod can be firmly fixed, and the operation flexibility and accuracy are improved.

The invention discloses an Internet of Things intelligent safety helmet integrating physiological sign monitoring and environment perception. The Internet of Things intelligent safety helmet comprises a safety helmet body, a control module, a vital sign monitoring module and an SOS alarm module. A control module is arranged in the safety helmet main body, and a power supply module, a communication module, a video acquisition module, a voice intercom module, a falling detection module, a vital sign monitoring module and an SOS alarm module are arranged on the safety helmet main body; according to the invention, a modular design is adopted, the device realizes deep unification of structural rationality and functional practicability through scientific layout and collaborative design of each functional module, and a communication module and a drop detection module are arranged at the top and in front in the structural layout based on the principle of ergonomics and scene adaptation so as to guarantee signal transmission and detection sensitivity. The video acquisition and voice talkback module is preposed to accurately capture field information, and the vital sign monitoring module is laterally arranged to improve the data acquisition precision.

This invention relates to an environmentally adaptive composite radar system, belonging to the field of radar detection technology. It solves the problems of weak environmental adaptability, low single-detection accuracy, and insufficient reliability and stability in existing technologies. The system includes: a laser generation module that outputs multiple laser beams; a microwave radar module that receives a first and second laser beam, modulates the first laser beam with a radio frequency signal to generate a microwave radar transmission signal, and transmits it to the target; a coherent lidar module that receives a third and a fourth laser beam, generates a laser transmission signal based on the third laser beam, and transmits it; receives laser echo signals, and obtains a coherent intermediate frequency (IF) signal based on the laser echo signal and the fourth laser beam, which is then transmitted to a control module; and a control module that processes the IF echo signal and the coherent IF signal to generate characteristic parameters of the target, and controls the operation of the microwave radar module and the coherent lidar module.