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Weighing model scheme determination method and device, computer equipment and storage medium

A determination method and computer technology, applied in calculation, design optimization/simulation, instruments, etc., can solve the problems of high cost, inconsistent learning results of intelligent weighing equipment models, and inability to evaluate learning effects, so as to improve accuracy and versatility Effect

Active Publication Date: 2022-06-24
YANTAI TRIAL RETAIL ENG CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] In the prior art, each intelligent weighing device learns the weighing model independently. Since the application scenarios of different intelligent weighing devices are different, each intelligent weighing device The model learning results of heavy equipment are different, and its learning effect cannot be evaluated
And for large chain supermarkets, each store has the same product types. When opening new stores, it is still necessary to repeatedly deploy smart weighing equipment and learn weighing models, which is costly and affects user experience.

Method used

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  • Weighing model scheme determination method and device, computer equipment and storage medium
  • Weighing model scheme determination method and device, computer equipment and storage medium
  • Weighing model scheme determination method and device, computer equipment and storage medium

Examples

Experimental program
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Embodiment 1

[0031] figure 1 The first embodiment of the present invention provides a flowchart of a method for determining a weighing model scheme. This embodiment can be applied to the situation in which the weighing model scheme of each intelligent weighing device in the cash register system is determined. The method can be determined by weighing The device for determining the model solution can be implemented in the form of hardware and / or software. The device for determining the weighing model solution can be configured in a computer device such as an intelligent weighing device or a server.

[0032] like figure 1 As shown, the method includes:

[0033] S110. If it is determined that the weighing model matching the target commodity type in the first node satisfies the learning completion condition, acquire the weighing model matching the target commodity type in the second node.

[0034] The number of the first nodes may be one or more. The first node and the second node are intell...

Embodiment 2

[0048] Figure 2a This is a flowchart of a method for determining a weighing model scheme provided in Embodiment 2 of the present invention. Based on the foregoing embodiments, the embodiment of the present invention clarifies that when the first node and the second node are nodes in the same local area network, The process of determining the weighing model scheme, and the judging process of the learning completion conditions and the determination of the target weighing model matching the target commodity type are further specified.

[0049] like Figure 2a As shown, the method includes:

[0050] S210: Determine whether the model accuracy of the weighing model matching the target commodity type in the first node is greater than or equal to the preset accuracy threshold, if so, execute S220; otherwise, return to execute S210.

[0051] In this embodiment, whether the model accuracy is greater than or equal to the preset accuracy threshold is used as an example to determine whe...

Embodiment 3

[0064] Figure 3a This is a flowchart of a method for determining a weighing model scheme provided by Embodiment 3 of the present invention. Based on the above embodiments, the embodiment of the present invention clarifies that the first node is the slave node in the local area network, and the second node is the local area network. The process of determining the weighing model scheme when the primary node in the .

[0065] like Figure 3a As shown, the method includes:

[0066] S310: Determine whether the model accuracy of the weighing model matching the target commodity type in the first node is greater than or equal to the preset accuracy threshold, if so, execute S320; otherwise, return to execute S310.

[0067] S320. Send the weighing model in the first node that matches the target commodity type to the second node.

[0068] In this embodiment of the present invention, the first node is a slave node in the local area network, and the second node is a master node in the...

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Abstract

The invention discloses a weighing model scheme determination method and device, computer equipment and a storage medium. The method comprises the steps that if it is determined that a weighing model matched with a target commodity type in a first node meets a learning completion condition, a weighing model matched with the target commodity type in a second node is acquired; wherein the number of the first nodes can be one or more; comparing the weighing model matched with the target commodity type in the first node with the weighing model matched with the target commodity type in the second node, and determining a target weighing model matched with the target commodity type; and combining the target weighing models matched with the commodity types to determine a weighing model scheme. By using the technical scheme of the invention, the precision and universality of the weighing model can be improved, and the deployment, maintenance and upgrade costs can be reduced.

Description

technical field [0001] The invention relates to the technical field of intelligent weighing, in particular to a method, device, computer equipment and storage medium for determining a weighing model scheme. Background technique [0002] With the development of the Internet of Things technology, many supermarket cashier systems have deployed intelligent weighing equipment. For a single intelligent weighing equipment, it is necessary to continuously strengthen the accuracy of the weighing model through on-site self-learning. [0003] In the prior art, each intelligent weighing device independently learns the weighing model. Since different intelligent weighing devices have different application scenarios, the model learning results of each intelligent weighing device are different, and their learning effects cannot be evaluated. In addition, for large chain supermarkets, each store has the same types of goods. When opening new stores, it is still necessary to repeat the deploy...

Claims

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Application Information

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IPC IPC(8): G06F30/20G06F16/2455
CPCG06F30/20G06F16/24553Y02P90/30
Inventor 闫凤图韩震张剑曙光李想
Owner YANTAI TRIAL RETAIL ENG CO LTD