Kitchenware fault diagnosing method based on case-based reasoning

Abstract

The invention discloses a kitchenware fault diagnosing method based on case-based reasoning, which comprises the following steps: a kitchenware fault problem is submitted to obtain fault symptom information, the fault symptom information is transmitted to a fault diagnosing server of an enterprise for retrieving and screening of kitchenware fault cases so as to obtain a target fault case, and the target fault case is transmitted back to a client, and a maintenance man or a user maintains the kitchenware according to a maintenance scheme in the target fault cases; if the kitchenware fault can be solved, the fault case is studied; otherwise, the step (5) is carried out to revise the fault case to obtain a new kitchenware fault case for maintenance till the kitchenware fault is solved. The kitchenware fault diagnosing method disclosed by the invention utilizes CBR (Case-Base Reasoning) to diagnose the kitchenware faults, and adopts the strategic concept of classifying the fault cases for retrieval, therefore, the method is beneficial to not only reduction of the difficulty in representing and organizing cases and creating a case base, but also great improvement of the case retrieving efficiency, and has higher adaptability compared with the traditional RBR (Rule-Based Reasoning) or MBR (Model-Based Reasoning) expert systems.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence fault diagnosis, and more specifically, relates to a method for fault diagnosis of kitchen utensils based on case reasoning. Background technique [0002] Kitchen utensils, as a necessary daily necessities in the family, with the improvement of people's living standards, the requirements for kitchen utensils are also increasing. However, in today's highly homogenized products, many products are placed together in the same way, and even high-quality products cannot form an advantage. With the continuous improvement of production quality and technical service requirements, people's selection requirements for household kitchen utensils are also rising. The homogenization of kitchenware product technology and product image makes it impossible for enterprises to obtain differentiated competitive advantages in products. If kitchenware companies want to gain survival advantages in the f...

AI Technical Summary

Problems solved by technology

Since these traditional expert systems are model-driven (model-based diagnostic methods use deep knowledge such as the structure, behavior, and function models of the diagnostic object for diagnostic reasoning), there are problems in model construction, information acquisition, and information processing. Serious deficiencies, there are some insurmountable shortcomings, such as the difficulty of extracting the rules of system domain knowledge; the creation and management of rule bases and pattern bases is complex and arduous; it is difficult to accurately select rules and patterns in the reasoning process, etc.

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