9results about How to "Reliable calculation" patented technology

The application relates to a terminal and a page generation method. The page generation method relates to the technical field of display devices. In the page generation method, when it is determined according to page information of a target page that a target content area in the target page contains a target function control, reference size information of a reference page is acquired; a reference direction of the target content area is selected from a width direction and a height direction according to a scrolling direction of the target content area; a scaling ratio of the target content area is determined according to actual pixel numbers of the target content area in the reference direction and reference pixel numbers of a reference content area in the reference direction in the reference size information; the reference size of the target function control in the reference content area in the reference size information is scaled according to the scaling ratio, so that the display size of the target function control in the target content area is obtained; and the target page is generated according to the page information and the display size of the target function control in the target content area, thereby improving the page display effect.

The present application relates to the field of laser measurement technology, and more particularly to a kind of steel rail piece geometric dimension intelligent measurement method based on laser scanning.The present application includes the following steps: measurement preparation and system calibration;Laser scanning data acquisition;Point cloud data preprocessing;Geometric dimension intelligent calculation, based on the profile feature extraction of rail head, rail waist and rail bottom of each section profile point cloud after standardization;Measurement result output and storage.The present application constructs global reference axis of steel rail, converts local coordinates of each section to global coordinate system and compensates scanner attitude deviation, establishes continuity constraint of adjacent section profile feature parameters according to steel rail model, obtains optimal profile feature parameters through global optimization, can stably extract profile features of rail head, rail waist and rail bottom under unified reference, reduces the influence of attitude deviation and point cloud noise, and realizes reliable calculation of geometric dimension of steel rail section, full-length straightness and twist degree form error.

The application discloses a kind of cable curvature risk assessment method, device, equipment and medium, belong to the risk assessment field of cable curvature.The method is: obtaining the visible image of the cable to be measured and extracting two-dimensional contour data;The cable to be measured is scanned non-contact, and three-dimensional point cloud data is obtained;Two-dimensional contour data is mapped to the three-dimensional coordinate system in which three-dimensional point cloud data is located, to obtain the three-dimensional point set after fusion, and least square method is used to fit spline curve;Finally, the risk assessment of the spline curve is carried out, and the risk assessment result of the cable to be measured is obtained.Therefore, by implementing the present application, the problem of low accuracy of cable curvature risk assessment in the prior art can be solved.

The invention discloses a low-permeability reservoir carbon dioxide flooding numerical simulation method based on Maxwell-Stefan cross diffusion, and belongs to the technical field of reservoir numerical simulation and recovery efficiency improvement. Comprising the following steps: establishing a mass conservation equation of each component based on an oil reservoir thermodynamic field, a rock physical property field, an effective diffusion coefficient matrix and reaction kinetic parameters; a diffusion flux item is expressed by a generalized Fick law based on a Maxwell-Stefan theory so as to describe a multi-component cross diffusion effect; a reaction source convergence item is determined by a COand crude oil reversible miscible phase kinetic model so as to describe a miscible phase and split phase dynamic process; sequentially executing a diffusion transmission step and a chemical reaction step for solving by adopting an operator splitting method to obtain an updated concentration field, and synchronously updating the thermodynamic field, the rock physical property field, the reaction kinetic parameters and the effective diffusion coefficient matrix; repeatedly iterating until each field and parameter change meet a convergence condition, and completing simulation of each chemical component concentration field; according to the method, the physical simplification defect in low-permeability simulation is overcome, and high-precision prediction of the displacement process is achieved.

This invention relates to the field of engineering data processing, and more particularly to an intelligent data acquisition and processing method and system for dredging engineering projects in tunnel drainage systems. The method includes the following steps: receiving processing tasks based on a cloud-based dredging management platform; setting acquisition constraints based on the processing tasks to obtain an acquisition constraint set; driving a pipeline detection robot to perform operational detection based on the acquisition constraint set, outputting a first dataset; adaptively optimizing the first dataset to output a second dataset; performing index-based analysis on the second dataset to output multi-dimensional analysis indicators; and planning the dredging operation path based on the multi-dimensional analysis indicators, synchronously uploading the data to the cloud-based dredging management platform to complete the data acquisition and analysis operation. This invention optimizes dredging operation planning, improves dredging efficiency, and enhances the accurate structural analysis of the drainage system in the early stages through precise data acquisition and processing.

The invention relates to an optical mark positioning device for PC component mold visual inspection and a detection method. The method comprises the following detection steps: step 1, a preparation stage: selecting a base matched with the height of a to-be-detected mold, assembling the optical mark positioning device, and loading component quality inspection data by a machine vision system background; secondly, a mold and a mark are placed, specifically, an operator places the PC component mold on a mold table, and then an optical mark positioning device is adsorbed to the mold table according to a preset rule; step 3, triggering detection: triggering a machine vision system; step 4, image acquisition and processing: shooting by a camera, and identifying an optical mark and identification information thereof in an image by software; the machine vision system calculates the actual position and posture of the mold according to the marked position, the actual position and posture are compared with standard data, and whether the mold is qualified or not is judged; and step 6, result feedback and mark removal: the HMI displays the result, and the magnetically attracted mark positioning device is taken down from the mold table for storage after the detection is completed.

Embodiments of the present application provide a neural network compression method and device, electronic equipment and storage medium, and belong to the technical field of artificial intelligence. The method comprises: performing quantization processing on a target neural network to obtain a plurality of first compressed neural networks; training the plurality of first compressed neural networks to obtain a candidate network sequence; performing iterative compression processing on the candidate network sequence until an iteration end condition is met; and selecting at least one optimized compressed neural network from the candidate network sequence as a target compressed neural network. The embodiments of the present application can obtain a required neural network compression model, which is beneficial to improving the compression accuracy of the neural network.

The invention discloses a three-dimensional target detection system based on two-dimensional perception. The three-dimensional target detection system comprises a two-dimensional perception module, a three-dimensional coordinate acquisition module and a post-processing module, the two-dimensional sensing module is used for performing feature extraction and region detection on an input image based on a deep learning target detection and key point detection algorithm, and outputting a bounding box of a to-be-detected target and two-dimensional coordinates of a plurality of key points; the three-dimensional coordinate acquisition module is used for performing spatial mapping on the two-dimensional coordinates of the plurality of key points and depth data acquired by the depth sensor, and calculating the three-dimensional coordinates of the key points; and the post-processing module is used for performing space consistency optimization on the three-dimensional coordinates of the key points in combination with the structure prior of the to-be-detected target, and calculating the pose of the to-be-detected target. According to the method, the high-precision three-dimensional pose of the to-be-measured target can be directly calculated from the two-dimensional visual information and the depth data without depending on complex three-dimensional modeling, the calculation precision is improved while the calculation amount is greatly reduced, and the calculation process is more stable and reliable.

This application provides a monocular vision distance estimation method, system, and product for aerial targets. The method is applied to a monocular camera and includes: acquiring a monocular image containing an aerial target, prior geometric parameters of the aerial target, and camera intrinsic parameters; performing target detection on the monocular image to obtain a two-dimensional bounding box of the aerial target in the monocular image; calculating a first candidate depth and a second candidate depth based on the prior geometric parameters and the camera intrinsic parameters, according to the width and height of the two-dimensional bounding box; selecting the smaller value between the first candidate depth and the second candidate depth as the estimated target depth; and determining the distance of the aerial target relative to the camera based on the estimated target depth and the center point of the two-dimensional bounding box. This invention, by employing a bounding box model combined with a minimum value selection strategy, can perform stable and reliable distance measurement of aerial targets based solely on monocular vision. Furthermore, this method is computationally efficient and easily integrated and deployed.