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Weighing method applicable to truck scale

A truck scale and weighing technology, which is applied in the direction of weighing, special scales, measuring devices, etc., can solve the problems of large difference and poor compensation effect

Inactive Publication Date: 2013-08-07
HUNAN NORMAL UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This method is based on the linear relationship between the input and output of the truck scale, which is quite different from the actual situation, so the compensation effect is poor

Method used

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  • Weighing method applicable to truck scale
  • Weighing method applicable to truck scale
  • Weighing method applicable to truck scale

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0087] Figure 4 Shown is the main structure of the weighing part of the truck scale applicable to the present invention: the output end of each load cell is connected to a data acquisition device, and the data acquisition device includes a conditioning circuit 1 and an analog-to-digital conversion circuit 2; the weighing signal is in the conditioning circuit 1 is amplified and filtered, and converted into a digital signal by the analog-to-digital conversion circuit 2, and the digital signal is transmitted to the microprocessor 3; the microprocessor is also equipped with a power module 4, a keyboard 6, a display 7 and a communication interface 8; The power supply module 4 supplies power for the conditioning circuit 1, the analog-to-digital conversion circuit 2, the microprocessor 3, and the like. During training, the microprocessor 3 is connected to the external computer 5 through the communication interface 8; during online weighing, the external computer 5 is evacuated.

Embodiment 2

[0088] Embodiment 2: method and steps of actual weighing model training.

[0089] In this embodiment, the truck scale has 8 load cells ( N =8), the measuring range is 40 tons, the maximum capacity of each load cell is 20 tons, the division number is 4000, and the verification division value e and the actual division value d Both are 10kg; the microprocessor 3 adopts the high-performance single-chip microcomputer MSP430F449 of TI Company. see figure 1 , the steps to train the actual weighing model are as follows:

[0090] (1) Construct an ideal weighing model as the constraint condition of the truck scale weighing system: take N The weighing signal output by the load cell is the input and A(X) is the output, construct the ideal weighing model of the truck scale, as shown in formula (1);

[0091] (2) Construct the actual weighing model of the truck scale based on the neural network: take the weighing signal of 8 sensors as the input and the weighing result of the truck s...

Embodiment 3

[0101] Embodiment 3: online weighing.

[0102] In this embodiment, the truck scale has 8 load cells ( N =8), the measuring range is 40 tons, the maximum capacity of each load cell is 20 tons, the division number is 4000, and the verification division value e and the actual division value d Both are 10kg; the microprocessor 3 adopts the high-performance single-chip microcomputer MSP430F449 of TI Company, and utilizes the actual weighing model that has been trained and qualified in the embodiment 2 to carry out online weighing. see figure 2 , the online weighing steps are as follows:

[0103] (1) Loading of the measured load: that is, the truck is loaded on any position of the truck scale body, and the weighing signal is collected after stabilization;

[0104] (2) Microprocessor acquisition N The weighing signal of the load cell: the system collects the output signal of the load cell of 8 channels through the load cell, the conditioning circuit 1, the analog-to-digital con...

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Abstract

The invention discloses a weighing method applicable to a truck scale, and the method comprises a weighing sensor, a data acquisition device, a microprocessor and a display which are used, and the following steps that a weighing mathematical model is established, a weighing signal is acquired and on-line weighing is carried out; the step of establishing the weighing mathematical model comprises an ideal weighing model, an actual weighing model and the training methods thereof; the ideal weighing model is a linear function; the actual weighing model is a three-layer BP neural network, wherein a first layer is an input layer, a second layer is a hidden layer and a third layer is an output layer; before on-line weighing, the training of setting sample sizes must be carried out on the ideal weighing model and the actual weighing model; the training is carried out when the microprocessor is connected with an external computer; sample information of set quantity is acquired; the ideal weighing model and a derivative thereof serve as constraint conditions; finally the parameters W, b (1), V and b (2) of the actual weighing model are acquired and saved in the microprocessor; and then the external computer is removed.

Description

technical field [0001] The invention relates to a weighing method suitable for truck scales. technical background [0002] Truck scale is an important branch of weighing apparatus, mainly used for bulk cargo measurement. At present, the analog electronic truck scale occupies a dominant position in the truck scale market, which is mainly composed of three main components: a load-bearing force transmission mechanism (scale body), an analog load cell, and a weighing display instrument. Truck scales generally have 4 to 12 load cells depending on the range. These sensors are symmetrically distributed under the scale body according to a certain topology, forming a multi-sensor system. The multi-sensor system is coupled, and the output of each sensor is related to each other and related to the loading position of the load. The truck scale accumulates the output signals of each load cell in the analog junction box to obtain a voltage signal proportional to the mass of the measure...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01G19/02G06N3/02
Inventor 林海军滕召胜汪鲁才杨进宝
Owner HUNAN NORMAL UNIVERSITY
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