Equipment fault alarm method and system based on deep learning
A technology of equipment failure and deep learning, applied in neural learning methods, character and pattern recognition, instruments, etc., can solve problems that do not adapt to the status quo of IT operation and maintenance, improve problem location and resolution efficiency, improve identification and location cycle, The effect of guaranteeing service quality
Active Publication Date: 2021-01-29
上海蒙帕智能科技股份有限公司 +1
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However, this simple method based on human-made rules is increasingly unsuitable for the increasingly complex IT operation and maintenance status
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[0041] In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings. Obviously, the described embodiments are only some of the embodiments of the present invention, rather than all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.
[0042] Terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. The singular forms "a", "said" and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms, unless the context clearly indicates otherwise, "multiple" Generally contain at least two.
[0043] It sho...
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The invention relates to an equipment fault alarm method and system based on deep learning. The method comprises the following steps of: configuring related parameters of an inspection robot; drivingthe inspection robot to execute machine room field image data acquisition based on the configured parameters; storing collected machine room field image data into a database; reading the collected field image data of a plurality of machine rooms from the database, and preprocessing the field image data of the plurality of machine rooms; training and establishing a deep learning algorithm model; and based on the trained model, performing real-time control on the inspection robot to determine whether the equipment states of the machine rooms are abnormal or not. Compared with traditional schemes, the method and system provided by the invention can quickly classify and accurately recognize problems by performing image recognition through the deep learning algorithm, thereby shortening a problem fault positioning period and greatly improving the problem solving efficiency.
Description
technical field [0001] The present invention relates to the technical field of equipment monitoring in a computer room, in particular to a deep learning-based equipment failure alarm method and system. Background technique [0002] The IT operation and maintenance work of quite a few enterprises mainly relies on the establishment of a maintenance team by the enterprise, and adopts the method of manual regular inspection. This method has a long inspection cycle, and the inspection results depend on the personal experience of the maintenance personnel, which may not be able to find problems in a timely and effective manner. Usually, the manual inspection method, even if a problem is found, cannot be accurately located in time, and it will cause delays in repairing and solving the problem. In addition, most enterprises will rely on sales equipment and technical maintenance personnel of system manufacturers to locate and fix problems. However, there is also a relatively long cy...
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IPC IPC(8): G06K9/00G06K9/32G06K9/46G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V20/10G06V10/25G06V10/44G06N3/045G06F18/2415
Inventor 张蔓琪
Owner 上海蒙帕智能科技股份有限公司