Method and relevant device for establishing repair action prediction model, and repair action prediction method and device

Abstract

The invention discloses a method for establishing a repair action prediction model, and a repair action prediction method. According to a repair record and diversified work source data, including theoperation record of an operator, a system fault record and / or system key action record and the like, a machine learning way is operated to establish a nonlinear repair action prediction model, and therefore, the established repair action prediction model can effectively reduce an influence of prediction accuracy due to a subjective judgment ability difference can be effectively reduced while therepair action is predicted. Meanwhile, action sampling data in the work time interval of the medical care equipment is obtained, the action sampling data is input into the established repair action prediction model, the repair action can be effectively predicted, so that the use operation habits of the operator can be optimized, the service life of the equipment is prolonged, and after-sales costexpenditures are reduced. The invention also discloses a device for establishing the repair action prediction model and a repair action prediction device.

Description

technical field [0001] The present application relates to the technical field of medical equipment, and in particular to a method for establishing a repair call action prediction model, a repair call action prediction method, and related devices. Background technique [0002] Large-scale medical imaging equipment, such as CT (Computed Tomography, computerized tomography) equipment, usually includes main working modules such as an operating software platform, an image scanning module, and a mechanical operation module. The work of large-scale medical imaging equipment can be completed by the operator through the software platform. The connection of each work link is partly completed automatically by the equipment, and the other part is realized by the operator's operation. [0003] From the perspective of the work type of large-scale medical imaging equipment, it can be divided into two states, one is the scanning state of image acquisition, and the other is the non-scanning ...

AI Technical Summary

Problems solved by technology

However, different usage scenarios, different operators operate the equipment in different ways, and the operating frequency is also different. In addition, the complexity of large-scale medical imaging equipment is relatively high.

Therefore, in addition to the normal life loss of equipment components, the operator's "improper operation" of the equipment may also cause abnormalities in the system that cannot be recovered for a short time. In this case, the operator will initiate a repair action for the equipment

[0004] Due to the uncertainty of the operator's operation, the operator's initiation of repair action is also uncertain. In the existing technology, it is impossible to accurately predict the generation of repair action, which will lead to the uncontrollable after-sales cost of large medical imaging equipment.

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