Load Carrier Loading State Detection via Neural Network
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Solution Overview
Problem
Conventional methods for tracking load carriers in industrial environments rely on sensors or trackers that cannot provide information about the loading status of the carriers, limiting their functionality.
Innovation Solution
A method and system that utilize sensors and a neural network to detect the loading state of load carriers by analyzing environmental data, allowing for the display of the load carrier's position and loading status in an industrial environment without the need for trackers on the carriers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If trackers are attached to load carriers to locate them, then the position of the load carrier can be determined, but the loading status cannot be provided and the device complexity increases
Solution Approach 1:
The patent creates a digital twin (virtual copy) of the industrial environment that mirrors the physical warehouse. Sensors detect load carriers and their loading states in the physical environment, and this information is replicated in the digital twin environment, allowing position and loading status to be determined without attaching complex trackers to each load carrier.
Solution Approach 2:
The sensor system is designed to perform multiple functions: it detects both the position of load carriers and their loading status simultaneously. This multi-functional approach eliminates the need for separate trackers for position determination and loading status monitoring, reducing overall device complexity.
2Loss of information
If sensors are mounted on load carriers to detect loading status, then loading state information can be obtained, but the risk of sensor damage or theft increases
Solution Approach 1:
The patent introduces stationary sensors mounted on the infrastructure (ceiling or wall) as intermediaries between the load carriers and the detection system. These stationary sensors detect load carriers passing by and determine their loading status without being attached to the moving load carriers, thereby eliminating the risk of damage or theft associated with mobile sensors.
Solution Approach 2:
The load carriers themselves provide information about their loading status through their interaction with the stationary sensor field. The sensors detect changes in the environmental area as load carriers move, and the neural network processes this data to determine loading state, allowing the system to obtain information without physically contacting or attaching sensors to the load carriers.
3Loss of information
If conventional tracking methods are used, then position can be monitored, but loading status information is not provided
Solution Approach 1:
The patent changes the detection parameters from simple position tracking to environmental area analysis. Instead of using trackers that only provide position data, the stationary sensors detect changes in the environmental area (light, sound, or other physical parameters) as load carriers move, and the neural network processes these parameter changes to determine both position and loading status.
Solution Approach 2:
The patent replaces mechanical trackers attached to load carriers with an optical or electromagnetic field-based detection system. Stationary sensors use light, sound waves, or other electromagnetic fields to detect load carriers and determine their loading status without mechanical contact, simplifying the overall system while providing additional information.
Data Source
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AI summary
Method (300) for automatically detecting the loading state of a charge carrier, wherein the charge carrier is located in an industrial environment, comprising the following features: detecting (301) an environmental area of the charge carrier by at least one sensor to obtain detected environmental data; selecting (303) an electronic loading state attribute from a plurality of electronic loading state attributes based on the detected environmental data by means of a neural network, wherein the neural network is configured to select electronic loading state attributes, wherein the neural network is executed by a control device, and wherein the electronic loading state attribute represents the loading state of the charge carrier; assigning (305) the selected electronic loading state attribute to a position of the charge carrier in the industrial environment;and display (307) the position of the load carrier on an electronic display.;