Feature data acquisition algorithm-based device tracing method

Abstract

The invention discloses a feature data acquisition algorithm-based device tracing method. The method includes the following steps that: a feature data acquisition unit acquires the feature data of a wind turbine generator set device through a feature data acquisition algorithm, and transmits the acquired feature data to a feature data processing unit; the feature data processing unit judges the validity of the acquired feature data through a feature state diagnosis model, keeps the feature data and transmits the feature data to a device integrated management system if the feature data are valid feature data, and filters out the feature data and sends an abnormality data report if the feature data are abnormal feature data; and the device integrated management system stores the valid feature data into a database through a feature matching algorithm, and then displays the real-time location information of the wind turbine generator set device through a visual management interface. According to the feature data acquisition algorithm-based device tracing method of the invention, a user can trace the device just through integrating the soft feature attributes of the bound device itselfwithout using device hard scanning tools.

Description

technical field [0001] The invention relates to the technical field of fan equipment data processing, in particular to an equipment traceability method based on a characteristic data collection algorithm. Background technique [0002] In the current field of wind power generation, the information in the wind turbine factory is relatively comprehensive, including material codes, cabinet codes, product barcodes, production dates, delivery dates, component names, component bit numbers, component models, component serial numbers, and component batch numbers. , drawing number and other information. However, the information after leaving the factory, including product replacement and call, is still untraceable, especially due to human factors such as owners and other non-manufacturer staff who perform equipment replacement, equipment removal, and equipment exchange outside the factory. Unable to bind equipment information occurred frequently, resulting in scattered, uncontrollabl...

AI Technical Summary

Problems solved by technology

However, the information after leaving the factory, including product replacement and call, is still untraceable, especially due to human factors such as owners and other non-manufacturer staff who perform equipment replacement, equipment removal, and equipment exchange outside the factory. Unable to bind equipment information occurred frequently, resulting in scattered, uncontrollable, and untraceable out-of-control state of unit equipment information, which in turn caused different degrees of property losses for manufacturers

[0003] For equipment traceability management, most manufacturers currently use barcodes for equipment identification, scan equipment through code scanners and record manually, and a small number of manufacturers use QR codes for equipment identification, and can scan equipment and synchronize files through mobile phones. Either way is limited by the identifiable state of the equipment identification. Once the identification is blocked or corroded, the identification will become unrecognizable, which makes the equipment untraceable.

[0004] As for the equipment information integration, binding, and traceability functions of wind turbines outside the factory, there is currently no mature and unified entrance for users to use

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