CN-121998607-A - AR-based QEC (quality of service) machine safety supervision method and system

CN121998607ACN 121998607 ACN121998607 ACN 121998607ACN-121998607-A

Abstract

The invention relates to a QEC (quality of service) machine safety supervision method and system based on AR (augmented reality). The method comprises the steps of generating a maintenance total task, decomposing and generating a plurality of subtasks, sending the subtasks to a pre-obtained online AR device, obtaining maintenance parts and target models where all the subtasks are located, associating the subtasks according to the maintenance parts, obtaining a mark subtask from the association subtask sets when the current subtask is executed, obtaining a real-time model of the maintenance parts where the mark subtask is located, executing the next subtask when the real-time model of the maintenance parts where the current subtask is located before and after execution meets a first preset condition, completing the association subtask sets if the real-time model of the maintenance parts where all the subtasks are located in the current association subtask set meets a second preset condition, and completing the maintenance total task if the completion time of all the subtasks and the real-time model of the total task meet a third preset condition. Compared with the prior art, the invention has the advantages of improving the data analysis efficiency and accuracy in QEC duty work and the like.

Inventors

XU MINYAN
XUE WEI
KONG LINGXIN
LIU YADONG
ZHANG HAILIN
YUE TING
ZENG XIAOWEN
HUANG YANQING
ZHOU ZUOCHENG
DAI GUOHONG
Bai Minjian
Jiang Minxu

Assignees

东方航空技术有限公司
中移物联网有限公司

Dates

Publication Date: 20260508
Application Date: 20241105

Claims (10)

1. The AR-based QEC mechanical safety supervision method is characterized by comprising the following steps of: Acquiring maintenance aircraft parameters and maintenance targets, and generating a maintenance total task; Decomposing and generating a plurality of subtasks according to the maintenance total task, sending the subtasks to the online AR equipment which is acquired in advance, and acquiring maintenance parts where each subtask is positioned and target models of the maintenance parts, wherein the online AR equipment is used for acquiring the working progress of the subtasks in real time; according to the obtained maintenance piece, correlating the plurality of subtasks to obtain a correlated subtask set of each subtask; When executing the current subtask, acquiring the completed subtask from the associated subtask set of the current subtask, and marking the subtask as a marked subtask; Acquiring a real-time model of a maintenance piece where the marking subtask is located, judging whether the real-time model accords with a first preset condition before and after the current subtask is executed, if so, continuing to execute the next subtask, otherwise, giving an alarm; after the execution of the current associated subtask set is finished, judging whether a real-time model of a maintenance piece where all subtasks in the current associated subtask set are located meets a second preset condition, if yes, completing the current associated subtask set to be qualified, and otherwise, giving an alarm; and when the completion time of all the subtasks and the total task real-time model corresponding to all the associated subtask sets meet a third preset condition, the maintenance total task is qualified after completion.
2. The AR-based QEC mission safety supervision method of claim 1, wherein the obtaining process of the target model comprises: Constructing an engine standard model after aircraft maintenance according to the maintenance aircraft parameters and the maintenance targets; and acquiring a target model of the maintenance piece where each subtask is located based on the engine standard model.
3. The AR-based QEC mission safety supervision method of claim 1, wherein the decomposing to generate the plurality of subtasks specifically includes: a plurality of sub-steps of the maintenance total task are arranged, and each sub-step is split to obtain a plurality of unit tasks; And acquiring the operation time, the use tool and the maintenance mode of each unit task, dividing a plurality of unit tasks with adjacent operation time, same use tool and same maintenance mode into one subtask, and obtaining a plurality of subtasks.
4. The AR-based QEC mission safety supervision method of claim 3, wherein, when decomposing to generate a plurality of subtasks, the specific process comprises: step labels corresponding to the online AR equipment are added to the multiple sub-steps, and the sub-steps are subjected to weight proportion and weight time calculation to obtain sub-step time weights; And generating the subtasks based on the time weights of the substeps, taking the step labels as task names of the subtasks, and uploading the task names to a server.
5. The AR-based QEC mission safety supervision method of claim 1, wherein the associating the plurality of subtasks according to the acquired repair parts comprises the following specific procedures: the maintenance piece where each subtask is located is obtained, and whether the two maintenance pieces have a connection relation is judged: if so, all the subtasks on the two maintenance pieces are in the same associated subtask set, and if not, the execution is not carried out; traversing all maintenance pieces and generating at least one associated subtask set.
6. The AR-based QEC mission safety supervision method of claim 1, wherein the first preset condition specifically includes that a second matching degree is higher than a first matching degree, the first matching degree is a matching degree between the real-time model and a corresponding target model before the current sub-task is executed, and the second matching degree is a matching degree between the real-time model and the corresponding target model after the current sub-task is executed.
7. The AR-based QEC mission safety supervision method of claim 1, wherein the second preset condition specifically includes that the matching degree between the real-time model of the maintenance piece where all the subtasks in the current associated subtask set are located and the corresponding target model is higher than a predetermined threshold.
8. The AR-based QEC mission safety supervision method of claim 1, wherein the third preset condition specifically includes that the subtask is completed qualified, a total time error of a total time of the subtask and a completion time of the subtask is lower than a predetermined error threshold, and a matching degree between the total task real-time model and a pre-built standard model is higher than a predetermined completion threshold.
9. The AR-based QEC engine safety supervision method of claim 1, wherein the online AR device includes a camera, a display screen, a processor and a man-machine interaction end, and the online AR device is used for acquiring the working progress of the subtasks in real time, and the specific process includes: receiving a subtask processing request and acquiring a corresponding unique identifier; Acquiring task permission and task standard execution guidance according to the unique identification, and displaying the task permission and the task standard execution guidance through a display screen; Acquiring a real-time operation video by using a camera, and comparing the real-time operation video with the task standard execution instruction by using a processor to obtain a subtask working progress; And recording the subtask work progress to a subtask form by using the man-machine interaction terminal.
10. AR-based QEC mission safety supervision system, characterized in that said system is adapted to implement the method according to any one of claims 1 to 9, in particular comprising: The total task generating module is used for acquiring maintenance aircraft parameters and maintenance targets and generating maintenance total tasks; The subtask generation module is used for acquiring maintenance parts where each subtask is located, decomposing and generating a plurality of subtasks according to the maintenance total task, then sending the subtasks to the online AR equipment which is acquired in advance, and simultaneously acquiring a target model of each maintenance part, wherein the online AR equipment is used for acquiring the working progress of the subtasks in real time; The subtask association module is used for associating the plurality of subtasks according to the acquired maintenance piece to obtain an associated subtask set of each subtask; The marking module is used for acquiring completed subtasks from the associated subtask set of the current subtask when the current subtask is executed, and marking the completed subtasks as marked subtasks; the first judging module is used for acquiring a real-time model of the maintenance piece where the marking subtask is located, judging whether the real-time model accords with a first preset condition before and after the current subtask is executed, if so, continuing to execute the next subtask, and otherwise, giving an alarm; The second judging module is used for judging whether the real-time model of the maintenance piece where all the subtasks in the current associated subtask set are located meets a second preset condition after the execution of the current associated subtask set is finished, if yes, the current associated subtask set is finished to be qualified, and otherwise, an alarm is sent; And the third judging module is used for completing the maintenance of the total tasks if the total task real-time model corresponding to all the associated subtask sets meets a third preset condition after all the subtasks are executed.

Description

AR-based QEC (quality of service) machine safety supervision method and system Technical Field The invention relates to the technical field of infrastructure and IT support, in particular to a QEC (quality of service) machine safety supervision method and system based on AR (augmented reality). Background With the rapid development of aerospace technology, the requirements on passenger capacity and safety of aviation are higher and higher, and crew members need to check and maintain an aircraft frequently, so that airport crew work is heavier and heavier. The complex QEC (Quick ENGINE CHANGE, namely Quick engine replacement) work is an aviation first-line work implemented on an aircraft core engine, and has the characteristics of more assembly and disassembly parts, high assembly and disassembly frequency and high safety risk. Taking a CFM56-7B engine as an example, 300 (approximately 1000) components need to be mounted on the engine each time. For a long time, the workload of maintenance work is huge, each working step needs to ensure compliance, and different working procedures need to be matched. Thus, a maintenance assistance device or system is needed to assist in guiding the crew to perform the work. For example, chinese patent CN111553499a discloses a flat personal maintenance aid. The equipment comprises a reinforced notebook computer and an installed portable maintenance auxiliary system, wherein the portable maintenance auxiliary system adopts a modularized design and comprises a data management and display module, a data unloading module, a fault interpretation module, an in-situ equipment monitoring and management module, a control module for controlling a portable maintenance diagnosis detector, an integrated flying module, a maintenance operation management module, an interactive electronic manual, a user management module and the like. However, the auxiliary equipment still needs to be manually turned over in the operation process of the crew, the form is manually filled in on the auxiliary equipment, and the auxiliary equipment only acquires the aircraft maintenance data for feedback because the relevance among the auxiliary equipment of different personnel is still insufficient, so that the current states of other operators are not acquired by a method, the checking and checking means are single, the information summarization still depends on a manual mode, and the data analysis efficiency is low and the deviation is large. Therefore, how to improve the data analysis efficiency and accuracy in the QEC duty work, and further improve the intelligent degree of QEC duty safety supervision becomes the problem that needs to be solved in the field. Disclosure of Invention The invention aims to overcome the defects of low data analysis efficiency and large deviation in the prior art and provide an AR-based QEC (automatic guided vehicle) engine engineering safety supervision method and system, the method applies an augmented reality (Augmented Reality, AR) technology to engine engineering maintenance work, aims to construct an intelligent engine plant and aviation first-line flow, the method changes some inefficient manual operations into efficient intelligent automatic operations, releases staff from the inefficient work through the AR technology, puts more energy into safe production, and simultaneously utilizes the AR technology to assist the staff in checking or verifying actions of all links, thereby effectively avoiding low-level errors caused by human factors and effectively improving the safety level. The aim of the invention can be achieved by the following technical scheme: According to the first aspect of the invention, the QEC engine safety supervision method based on AR comprises the steps of obtaining maintenance aircraft parameters and maintenance targets, generating a maintenance total task, decomposing and generating a plurality of subtasks according to the maintenance total task, then sending the subtasks to on-line AR equipment obtained in advance, obtaining a maintenance piece where each subtask is located and a target model of the maintenance piece, wherein the on-line AR equipment is used for obtaining working progress of the subtasks in real time, correlating the plurality of subtasks according to the obtained maintenance piece to obtain an associated subtask set of each subtask, obtaining completed subtask from the associated subtask set of the current subtask when executing the current subtask, marking the completed subtask as a marked subtask, obtaining a real-time model of the maintenance piece where the marked subtask is located, judging whether the real-time model meets a first preset condition or not before and after executing the current subtask, if yes, continuing to execute the next subtask, otherwise, judging whether the real-time model of the maintenance piece where all the associated subtask is located meets a second preset condition or not, if the associated subt