20results about How to "Implement refactoring" patented technology

The application discloses a photoelastic transparent soil three-dimensional stress-deformation full-field cooperative measurement device and a use method thereof. The device comprises a loading system, a stress-strain measurement system and a model test system. The model test system comprises an optical platform and a model groove arranged at a central position of the optical platform and used for containing a sample to be tested. The loading system is arranged above the optical platform and used for applying axial pressure to the sample to be tested. The stress-strain measurement system comprises a camera module, a laser module, a light source module, a guide rail and a control system II. During work, the camera module, the laser module, the light source module and the guide rail are adjusted by the control system II, so that the camera is scanned around the sample to be tested, deformation images are collected, full-field strain is obtained, and a three-dimensional digital model of the soil body is reconstructed. The device has high integration, can obtain stress distribution information of any cross section and any direction in the material, and overcomes the inherent defects that a two-dimensional photoelasticity cannot reflect stress gradient in the thickness direction.

The application discloses a reconstruction method and system for a cavity under a shallow reinforcement net shelter, and comprises the following steps: constructing a layered medium scene containing a shallow reinforcement net and a cavity; acquiring image data information with different shelter states and preprocessing to construct a reconstruction data set; constructing a reconstruction initial model for the cavity under the shallow reinforcement net shelter and training to obtain a reconstruction model for the cavity under the shallow reinforcement net shelter; and using the obtained reconstruction model for the cavity under the shallow reinforcement net shelter to perform actual reconstruction for the cavity under the shallow reinforcement net shelter. The application acquires multiple types of training data through the constructed layered medium scene containing the shallow reinforcement net and the cavity, and trains the reconstruction model for the cavity under the shallow reinforcement net shelter which comprises a U-shaped network, a Sobel operator, an attention mechanism, a residual structure, an average mechanism and a skip connection scheme. Therefore, the application can not only realize the reconstruction for the cavity under the shallow reinforcement net shelter, but also has higher reliability, better accuracy and higher reconstruction efficiency.

The invention relates to a radiation meter dense feed source array directional diagram optimization method based on a generative adversarial network. The method comprises the following steps: constructing a training data set, a condition vector containing a target directional diagram performance index and a corresponding optimal amplitude-phase weight vector; designing a conditional generative adversarial network model; constructing a physical constraint loss function by embedding a differentiable electromagnetic calculation module; loading the constructed training data set, and training a complete generative adversarial network model based on a loss function embedded with a differentiable electromagnetic calculation module; and when the system antenna pattern obtained according to the amplitude-phase adjustment parameters output by the generative adversarial network model meets the antenna pattern evaluation index, completing dense feed source array antenna pattern optimization. According to the method, the target weight is dynamically adjusted and optimized on orbit, so that a single model can adapt to beam forming requirements in different observation scenes, and flexible switching of multiple task modes such as high-resolution detection and wide-range general survey is realized.

The present invention belongs to the technical field of rare earth permanent magnet materials, and specifically relates to a high-abundance rare earth rapid quenching alloy and a preparation method thereof. In order to synergistically improve the solubility of La, the Ce 3+ proportion and magnetic properties in the high-abundance rare earth rapid quenching alloy, the high-abundance rare earth rapid quenching alloy of the present invention is expressed in atomic percentage as (La 1.53 Ce 3.57 RE 11.9 )-M x Fe 78‑x -B6, where 0 < x < 1; RE is an optionally added rare earth element selected from one or more of Nd, Pr, Dy, and Tb; M is a doping element selected from at least one of Ag, Sn, Au, and Cu, and the mixing formation enthalpy of the binary system formed by M and Fe is greater than the mixing formation enthalpy of the binary system formed by La and Fe, and it is prepared by a two-step method of vacuum induction melting and melt rapid quenching. The present invention can significantly improve the solubility of La in the matrix phase, and while maintaining a high proportion of Ce 3+ , it greatly optimizes the coercivity and the maximum magnetic energy product.

The application provides a kind of floating wind turbine physical-numerical mixed scale model test method, belongs to offshore wind power generation technical field, the test method will floating wind turbine modeling into two parts of physical model and numerical model, in addition to physical model and numerical model, test platform also includes measurement system and loading system;Real-time interaction is carried out between the measured signal of measurement system and the calculation result of numerical model by computer, signal loading is realized by loading system, and the construction and closed-loop control of hybrid test platform are realized.Finally, the required information is monitored and extracted through the visual interface.The application can not only accurately capture the performance of the dynamic response of the key structure, but also solve a series of problems such as scale effect, incompatibility of scale rule caused by scale effect and difficulty in simulating indoor test of offshore floating wind turbine.

This invention relates to a negative electrode current collector with a composite aperture array, its preparation method, and its application. The negative electrode current collector includes a copper foil substrate on which a composite aperture array structure is provided. The composite aperture array structure includes multiple through holes and multiple blind holes. The through holes are arranged through the thickness direction of the copper foil substrate to provide lithium ion transport channels. The blind holes are arranged along the thickness direction of the copper foil substrate and are closed at the bottom to provide nucleation sites for lithium deposition and buffer space for volume expansion. The through holes and blind holes are alternately distributed according to a preset arrangement rule. In any local area, at least one through hole and multiple blind holes constitute a combined unit to form a current density difference distribution on the surface of the copper foil substrate and guide lithium deposition. The preset arrangement rule includes: the through holes and blind holes are arranged periodically in a plane according to the combined units. The combined unit includes at least one through hole located at the center and multiple blind holes arranged in a ring around the through hole.

The present application relates to the field of biomedical engineering, specifically provides a kind of self-powered water-volt battery dressing with self-release water and water function, the dressing innovationally integrates temperature-sensitive conductive hydrogel layer and water-volt power generation layer, realizes the self-adaptive, self-sustaining intervention to wound microenvironment, through the unique "water storage-release-water" closed-loop mechanism, system utilizes body temperature trigger water release on demand, and realizes the self-supply of fuel and energy recycling by absorbing tissue exudate and external water environment moisture, so as to overcome the technical bottleneck that the energy source of existing self-powered system is unstable, intermittent supply, ensure that continuous, stable therapeutic electric stimulation is provided in the whole healing period;Compared with traditional treatment methods, the dressing can significantly accelerate wound closure and promote ordered and high-quality regeneration repair.

The present application relates to sensing technology and seabed topography monitoring field, aims at providing a kind of test device and method for analyzing the coupling law of sensing array and soil deformation.The device includes the main box of top opening and the scanning mechanism arranged at the opening, side push mechanism and vertical push mechanism are arranged in the main box;Variable volume test cavity is formed by deformable side plate and multiple side push oil cylinders arranged in side push mechanism, and vertical push bottom plate and multiple side push oil cylinders arranged in vertical push mechanism, for containing test soil and burying sensing array.The present application uses telescopic oil cylinder and push plate combined mechanism to simulate soil settlement, lifting and collapse, which can realize the simulation of soil in multiple directions, different displacement deformation variables and deformation speed, and the main device is simple to process and low in material cost.The deformation coupling law of sensing array with different scales and different burial depths, different soil, different scales, different deformation variables can be studied.

The application discloses a kind of brain injury diagnosis and treatment auxiliary decision system based on medical big data, specifically related to computer-aided diagnosis field, for solving the problem of inaccurate structure evolution interpretation in the existing brain injury diagnosis and treatment process;The trajectory construction module obtains the brain injury CT image sequence in the complete diagnosis and treatment cycle, extracts the brain density value to construct the structure evolution trajectory sample library;Through the abnormal identification module, the evolution abnormal feedback signal is identified;Then the conflict triggering module introduces the patient's historical brain function state, to judge whether there is a pattern conflict between structure change and function evaluation;If there is a conflict, the reasoning reconstruction module reconstructs the cross mechanism reasoning chain;The model training module is based on the reconstructed reasoning chain and function state score Training injury mechanism identification model;Finally, the decision output module assists in pathological mechanism judgment according to the identification result, improves the diagnosis and treatment decision accuracy under complex injury condition.

The invention relates to a contact with a gradient fine grain structure and a preparation method and application thereof, and belongs to the field of machine manufacturing. The method comprises the steps that a CuCr alloy blank bar is subjected to finish machining, a rolling machining area is longitudinally reserved when the front face of the contact is machined, then the contact is put into a cryogenic low-temperature box to be subjected to low-temperature nitrogen cryogenic treatment, an obtained contact blade is subjected to multiple times of rolling treatment to complete contact outer surface machining, and the treated contact is put into a packaging bag to be sealed after being cleaned, deoiled and dried. According to the invention, gradient structure design is carried out on the contact, a CuCr material matrix-surface synergistic structure regulation and control method is developed under the condition that the existing structure is not changed, and the vacuum arc-extinguishing chamber electrical contact with high hardness and an ultra-fine grain structure is developed. The microstructure of the alloy is controlled by adopting collaborative refining methods such as matrix cryogenic treatment and surface rolling treatment, and reconstruction of the Cu-Cr two-phase microstructure is achieved. The CuCr contact material and device with the grain size smaller than 10 microns and the hardness improved by 30% under the condition of equivalent electric conductivity are developed.

The application belongs to the technical field of earthquake magnitude determination, and specifically discloses a HR-GNSS large earthquake magnitude prediction method fusing physical perception and attention mechanism. The method first acquires HR-GNSS three-component waveform timing data of multiple stations, extracts timing characteristics through multi-layer convolution, and compresses along the time dimension to form a feature matrix corresponding to multiple stations. Then, a dot product self-attention mechanism is introduced in the station dimension to build the correlation between different station features and perform information interaction, and a global waveform feature vector representing an earthquake event is output. The magnitude prediction model also takes the epicentral distance of each station as auxiliary input, extracts a geometric distance feature vector of the spatial distribution of the station from the epicentral distance vector through a multi-layer perception machine. Finally, the obtained global waveform feature vector and geometric distance feature vector are fused in a high-dimensional space, and a prediction result of the magnitude of an earthquake is output through a fully connected regression module. The method can realize fast and accurate estimation of the magnitude of a strong earthquake.

This invention relates to an augmented reality scene planning and display system for large-scale industrial scenarios, comprising: a mobile terminal, a scene planning unit, and an augmented reality display unit. The mobile terminal is equipped with a camera. The scene planning unit includes a scene creation module and an AR content planning module. The scene creation module is used to read image data acquired by the camera and create a corresponding scene map based on the characteristics of the image data. The AR content planning module is used to save virtual models, place virtual models in the scene map, bind virtual models corresponding to AR tags, and save the virtual model parameters as an AR display content script file. The augmented reality display unit is used to load and parse the scene map data and the AR display content script file, and simultaneously relocate the scene map data using the currently read image data. Based on the relocated scene map data and the parsed AR display content data, the virtual models are displayed in the physical scene.

This application discloses a satellite radiation pattern reconstruction method and system based on low-dimensional representation, relating to the field of satellite antenna radiation pattern measurement and reconstruction technology. The method includes: representing the complete radiation pattern of the satellite under test as a linear combination of a preset basis matrix and a coefficient vector; based on... D Optimal sampling criteria, from M Select from candidate sampling points A sampling set is formed by several target sampling points, with the objective being to maximize the determinant of the Fisher information matrix in the coefficient vector estimation process; within the sampling set... N Measurements are taken at each target sampling point to obtain the observation vector, and the coefficient vector is estimated using the maximum likelihood estimation method. Using the estimated values ​​and a preset basis matrix, a complete radiation pattern is reconstructed. This invention has the advantages of achieving high-precision radiation pattern reconstruction with a limited number of sampling points, significantly improving measurement efficiency and reliability.

This application discloses a fault detection and reconfiguration method for an avionics system interaction channel, comprising the following steps: A control management device sends a fault detection signal transmission command to each functional execution device via an FC bus, CAN bus, and Ethernet, to obtain fault detection signals transmitted by each functional execution device according to the fault detection signal transmission command; the control management device determines, based on each fault detection signal, whether control information can be exchanged between the control management device and each functional execution device via the FC bus, CAN bus, and Ethernet, the control information including control commands sent by the control management device to each functional execution device to implement corresponding functions, and data sent by each functional execution device to the control management device according to the control commands; the control management device determines, based on the operating status of the FC bus, CAN bus, and Ethernet, whether and how to reconfigure the control information interaction channel to ensure control information interaction between it and each functional execution device.

This invention provides a deep learning method for reconstructing sea surface current fields based on multi-source air-sea spatiotemporal features, relating to the field of marine data processing. Specifically, it includes: acquiring multi-year continuous sea surface current velocity data and multi-source environmental feature data for a target sea area; constructing a full-field training sample in a continuous time series manner, using multi-source environmental features from multiple consecutive historical moments as input to candidate deep learning models, and using the full-field data of the eastward and northward current velocity components at a future target time as prediction labels; constructing velocity moduli using the eastward and northward current components; training candidate deep learning models using a joint loss function; evaluating candidate deep learning models, selecting the optimal model, and using the optimal model to output the full-field current velocity results at the target time. The technical solution of this invention overcomes the problem in existing technologies of difficulty in obtaining long-term, large-scale, spatially continuous sea surface current field data.

The invention provides a dam crack extension tracking method based on acoustic emission signal analysis, which comprises the following steps: S1, carrying out multi-dimensional feature enhancement preprocessing and extraction on acquired acoustic emission signals, and carrying out event identification and segmentation based on a pre-trained long-short term memory network to generate high-dimensional signal feature super vectors; s2, performing fracture mode identification and classification based on a deep learning network of fracture mechanics physical constraints, and outputting a fracture mode probability distribution vector of the acoustic emission event; s3, fusing the fracture mode information and an anisotropic velocity field model related to a dam stress field, and performing sound source high-precision positioning through an iterative inversion algorithm; s4, performing cluster recognition on the positioned sound source event based on a four-dimensional density clustering algorithm of space-time correlation and physical similarity, and reconstructing a three-dimensional crack frontal surface by adopting a moving least square method; and S5, according to the reconstructed crack frontal surface trajectory and the corrected acoustic emission event energy, inverting crack propagation kinetic parameters and quantitatively evaluating a damage state.

The application discloses a power quality disturbance data detection method and device based on compressed sensing. The method comprises the following steps: acquiring an original sampling signal, obtaining an observation vector through a measurement matrix to realize signal compression; separating a fundamental component and a harmonic component of the original signal according to the fundamental wave filtering principle; reconstructing the original signal according to the proportion of the harmonic component, arranging the inner product of a sensing matrix and a harmonic component observation vector in descending order when the harmonic component is more than a set condition, calculating a slope curve, estimating the signal sparsity according to the pole distribution of the slope curve, taking the sparsity estimation value as an initial step of a SAMP algorithm, and iteratively calculating a residual ratio threshold and a growth curve function to obtain a sparse vector estimation value of the harmonic component; and performing power quality disturbance data detection.

An X-ray diagnostic apparatus, an X-ray diagnostic system, and an image reconstruction method. The X-ray diagnostic apparatus includes an X-ray tube that irradiates an X-ray to an object, an X-ray detector that detects the X-ray transmitted through the object, a support portion that supports the X-ray tube and the X-ray detector, a moving mechanism that enables the X-ray tube and the X-ray detector to move, and a control portion that controls the X-ray tube and the X-ray detector to perform X-ray imaging, thereby collecting a plurality of X-ray image data. The X-ray diagnostic apparatus further includes sensors provided on the X-ray tube and the X-ray detector, respectively, to acquire vibration data of the X-ray tube and the X-ray detector. The control portion acquires, in advance, motion model information of the X-ray diagnostic apparatus based on structural analysis, i.e., a structural mathematical model. The control portion corrects the structural mathematical model of the X-ray diagnostic apparatus based on the vibration data acquired by the sensors, and generates a reconstructed image based on the corrected structural mathematical model and the plurality of X-ray image data.

The application discloses a deep foundation pit intelligent monitoring method and system based on multi-source data fusion, relates to the technical field of construction monitoring, and comprises the following steps: step S1, collecting multi-physical field data of a foundation pit in real time through a distributed sensor network; step S2, performing a preprocessing operation on the multi-physical field data by using an edge computing node; step S3, transmitting the data after the preprocessing operation to a cloud platform through a multi-mode communication network; step S4, performing multi-source data fusion analysis on the data in the cloud platform, and calculating a comprehensive risk index by using an improved time-varying weight fusion algorithm; step S5, judging the comprehensive risk index, and realizing graded early warning; and step S6, executing corresponding automatic response measures according to the early warning level. The application realizes real-time collection of data such as foundation pit stability by embedding sensing elements, and realizes informatization and intelligentization of foundation pit monitoring through intelligent calculation and automatic alarm.

This invention proposes a segmentation-guided automatic region-level recognition method for rock thin section images. The method first performs target segmentation on the rock thin section image, obtaining multiple independent regions and corresponding region masks. Then, each independent region undergoes preprocessing including cropping, background suppression, boundary compensation, and size normalization to construct classification input samples. Next, the preprocessed region images are input into a recognition model to extract region texture, morphological, and semantic features, outputting corresponding category labels. Finally, the recognition results of each independent region are mapped back to the original image, and automatic analysis results are generated through region merging and consistency correction. This method effectively connects segmentation results with region-level recognition, improving the accuracy and automated analysis capabilities for identifying mineral grains, pores, and matrix targets in rock thin section images.