TCM Weighing via Vector Prediction Error
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Solution Overview
Problem
Existing methods for weighing traditional Chinese medicine in small packages face challenges such as inconsistent package grasping, unstable weighing due to oscillations, and low data utilization, leading to reduced efficiency and accuracy.
Innovation Solution
A method that establishes an equivalent physical model of the weighing system, uses vector prediction error for coefficient identification, and employs a recursive algorithm to improve weighing accuracy and efficiency by utilizing data from a time window, allowing for faster convergence and reduced steady-state errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If vacuum chucks are used to grasp small packages of TCM, then the efficiency of prescription filling is improved, but the reliability of package grasping deteriorates because it cannot guarantee that each vacuum chuck can successfully grasp the medicine package
Solution Approach 1:
The system employs feedback mechanisms through sensors to detect whether medicine packages are successfully grasped by vacuum chucks. When packages are not properly grasped, the system provides feedback to adjust the vacuum pressure or reposition the chuck, ensuring reliable grasping while maintaining efficient prescription filling operations
2Measurement precision
If the electronic scale waits until stabilized for weighing after medicine packages fall on it, then the measurement precision is improved, but the loss of time increases due to the unstable oscillation period
Solution Approach 1:
The system performs preliminary actions by allowing medicine packages to be placed on the electronic scale in advance and uses vibration isolation mechanisms or damping systems to minimize oscillations before weighing begins. This preliminary stabilization reduces the waiting time while ensuring accurate measurement
3Ease of operation
If the traditional identification model uses current-time scalar prediction error for recursive iteration, then the ease of operation is maintained, but the productivity deteriorates due to slow convergence of weighing model coefficients
Solution Approach 1:
The system dynamically adjusts the identification model by transitioning from static current-time scalar prediction to a dynamic approach that incorporates historical data trends and vector prediction errors. This dynamic adaptation accelerates the convergence of weighing model coefficients while maintaining operational simplicity through automated adjustments
4Device complexity
If the traditional identification model uses current-time scalar prediction error, then the device complexity is reduced, but the measurement precision deteriorates due to low data utilization and large fluctuation of steady-state errors
Solution Approach 1:
The system transitions from one-dimensional scalar prediction error to multi-dimensional vector prediction error that incorporates historical data trends and multiple time points. This dimensional expansion improves data utilization and coefficient identification accuracy while managing complexity through structured mathematical frameworks
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method achieves faster convergence, higher coefficient identification accuracy, and improved weighing efficiency, balancing identification accuracy and speed, while effectively using overall data trends within the local neighborhood.
Implementation Method 1
m is the equivalent mass of the medicine receiving tank; M is the mass of medicine packages to be weighed; y(t) is the output signal of the weighing system; g is the gravitational acceleration
Data Source
AI summary
The present disclosure discloses a method for quick weighing of loose and small packages of traditional Chinese medicine (TCM), comprising: 1) establishing an equivalent physical model of a weighing system for loose and small packages, and, through Laplace transformation and Z transformation, obtaining a formula of the mass M of medicine packages to be weighed; 2) calculating a1, a2, b1 and b2 on the basis of a vector prediction error; 3) according to a1, a2, b1, and b2 obtained and the formula of the mass of medicine packages to be weighed, obtaining the mass of the loose and small packages of TCM that are weighed. According to the method, the scalar prediction error is expanded into the vector prediction error based on the traditional method so as to construct a new identification model based on the vector prediction error, and as a result the utilization efficiency of prediction error turns higher.