Dynamic weighing correction method based on deep neural network in digital twin environment

A deep neural network and dynamic weighing technology, which is applied in the identification scheme for drivers' weight loss and toll evasion, and in the field of dynamic weighing correction, can solve the problem of low measurement accuracy, achieve accurate simulation data, improve measurement accuracy, and enrich samples The effect of capacity

Active Publication Date: 2021-03-23
陕西智慧路衡电子科技有限公司
View PDF9 Cites 5 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a dynamic weighing correction method based on a deep neural network in a digital twin environment. By constructing a digital-physical twin system of a dynamic weighing system to establish a high-speed dynamic weighing correction system, the traditional dynamic weighing Optimize the problem of low measurement accuracy of the heavy system

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Dynamic weighing correction method based on deep neural network in digital twin environment
  • Dynamic weighing correction method based on deep neural network in digital twin environment
  • Dynamic weighing correction method based on deep neural network in digital twin environment

Examples

Experimental program
Comparison scheme
Effect test

specific Embodiment approach

[0049] (1) Firstly, rationally arrange the quartz sensor and camera device, and build the detection device of the dynamic weighing correction system, so as to build a road-vehicle-scale digital twin system;

[0050] Due to the shortcomings of traditional quartz load cells such as uneven pressure sensitivity, sensitivity to environmental changes, rough processing, and low assembly accuracy, the high-speed dynamic weighing system optimizes the quartz sensor used. The installation position of the chip should be used to avoid uneven pressure sensitivity. Secondly, aluminum materials with low impact on environmental changes should be selected. Finally, the shortcomings of traditional quartz load cells should be improved by precision machining of pressure planes, quartz assembly planes and high-precision assembly. .

[0051] The sensor arrangement is as figure 1 As shown, it is assumed that the weighing area S is 5 meters, and the four quartz load cells used are dislocated in the w...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a dynamic weighing correction method based on a deep neural network in a digital twinning environment, wherein the method is characterized in that a quartz weighing sensor converts a pressure signal into an electric signal for output when a vehicle passes through a weighing region, an axle recognizer recognizes the number of axles of the vehicle, and a camera device recordsthe driving process of the vehicle; refined simulation and data twinning are carried out through a road-vehicle-scale digital twinning system with a multi-scale numerical simulation technology, various parameters capable of being measured by a quartz weighing sensor when a vehicle passes through a weighing area are simulated, a part of data is twined, and the sample capacity is enriched; the collected data and the twinning data are input into a trained dynamic weighing correction system based on the deep neural network to be corrected, finally, the dynamic vehicle weight is output and compared with the latest 'highway freight vehicle overrun overload identification standard', and whether the vehicle is overweight or not is judged.

Description

technical field [0001] The invention belongs to the technical field of intelligent transportation applications, and in particular relates to a dynamic weighing correction method for road freight vehicles in a digital twin environment and an identification scheme for drivers to reduce weight and evade tolls. Background technique [0002] At present, the mature weighing technology on the market is mainly divided into two categories, one is static weighing, and the other is dynamic weighing. The general method of static weighing is to use the weighing platform for weighing, which requires the vehicle to slow down and stop before weighing. This method is slow and inefficient, and it is easy to cause congestion and traffic accidents, affecting the efficiency of transportation. However, the advantage of dynamic weighing is that the vehicle does not need to stop during the entire weighing process, that is, the vehicle is weighed during normal driving, which greatly improves the wei...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G01G19/02
CPCG01G19/02
Inventor 赵栓峰李明月杨建伟李瑶
Owner 陕西智慧路衡电子科技有限公司
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap