KR102607740B1 - Drone as repair management method and system - Google Patents

Abstract

The present invention discloses a drone AS repair management method and system. The drone after-sales service repair management system of the present invention includes a defect symptom and damage evaluation unit that determines defect symptoms of a drone received for after service (AS) and determines damaged parts of the drone; a repair unit that repairs the drone by replacing the damaged parts; and a test flight unit that checks whether the repaired drone operates normally. According to the drone AS repair management method and system according to the present invention, systematic management of drone AS repair is possible.

Description

Drone AS REPAIR MANAGEMENT METHOD AND SYSTEM}

The present invention relates to the field of drone after service (AS), and more specifically, to a drone after service (AS) repair management method and system that efficiently determines failed drone parts and provides accurate repair of failed parts.

A drone is an airplane or helicopter-shaped flying object that flies under the guidance of radio waves without a person on board. Initially developed and used for military purposes, the commercial drone market is currently expanding as demand increases in various fields such as firefighting, disaster prevention, media, transportation, and hobbies.

As the use of drones increases, unintentional accidents such as falls due to malfunction, collisions with obstacles, explosions due to battery overheating, and in-flight accidents due to durability problems of various components during handling are occurring frequently.

As drones are used in a variety of industries throughout the industry, systematic methods and systems for after-sales service repair of drones are required.

Registered Patent Publication 10-2359091 (2022.01.28)

The purpose of the present invention is to provide a drone after-sales service repair management method and system that efficiently determines drone malfunctioning parts and provides accurate repair of malfunctioning parts.

The above and other objects and advantages of the present invention will become apparent from the following description of preferred embodiments.

The above purpose is to provide a drone after-sales service repair management method and system.

The drone AS repair management system according to an embodiment of the present invention,

A defect symptom and damage evaluation unit that determines defect symptoms of a drone received for after service (AS) and determines damaged parts of the drone;

a repair unit that repairs the drone by replacing the damaged parts; and

It includes a test flight unit that checks whether the repaired drone operates normally.

Preferably,

The test flight unit is characterized in that it checks whether the gimbal mounted on the drone is operating normally or whether the gimbal is in focus.

Preferably,

The defect symptom and damage evaluation department,

an outlier extractor that extracts data from each flight record recorded in the loaded aircraft and the internal storage device of the transceiver;

a failure-related parts prediction unit that derives failure-related parts predicted to be related to the occurrence of outliers in the individual flights; and

and a target repair part determination unit that determines, as a target part to be repaired, the part with the highest cumulative number of predicted failure-related parts in the failure-related parts prediction unit for the entire flight of the aircraft and the transceiver internal storage device.

Preferably,

The repair unit is characterized as an augmented reality execution unit that assists a repair engineer repairing the drone.

Preferably,

The repair department,

It includes a virtual simulation unit that simulates a virtual flight of the drone based on the flight records of the aircraft and the internal storage device of the transceiver,

The virtual simulation unit simulates whether normal flight of the drone is possible by repairing the target part using the normal operating range value of the target part.

A drone AS repair management method according to another embodiment of the present invention,

A defect symptom and damage evaluation step in which the defect symptom and damage evaluation unit determines the defect symptom of a drone received for after service (AS) and determines a damaged part of the drone;

A repair step of repairing the drone by replacing the damaged parts by a repair department; and

It includes a flight test step of checking whether the drone for which repairs have been completed by the test flight department operates normally.

Preferably,

The defect symptom and damage evaluation step is,

An outlier extraction step of extracting outliers from individual flights stored in the flight record of the aircraft loaded by the outlier extraction unit and the internal storage device of the transceiver;

A failure-related parts prediction step of deriving failure-related parts predicted to be related to the occurrence of an outlier in the individual flight by a failure-related parts prediction unit; and

A target part determination step of determining as a target part to be repaired the part with the highest cumulative number of predicted failure-related parts by the target repair part determination unit for the entire flight of the aircraft and the transceiver internal storage device; Includes.

Preferably,

The repair step is,

A virtual simulation step of simulating the virtual flight of the drone by a virtual simulation unit based on the flight records of the aircraft and the internal storage device of the transceiver,

The virtual simulation unit simulates whether normal flight of the drone is possible by repairing the target part using the normal operating range value of the target part.

According to the drone AS repair management method and system according to the present invention, systematic management of drone AS repair is possible.

According to the drone after-sales service repair management method and system according to the present invention, accurate repair of failed parts of the drone can be achieved.

However, the effects of the present invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the description below.

In order to more fully understand the drawings cited in the detailed description of the present invention, a brief description of each drawing is provided.
Figure 1 is a diagram schematically showing the configuration of a drone AS repair management system according to an embodiment of the present invention.
Figure 2 is a diagram schematically showing the configuration of a defect symptom and damage evaluation unit according to an embodiment of the present invention.
Figure 3 is a diagram schematically showing the configuration of a repair unit according to an embodiment of the present invention.
Figure 4 is a flowchart of a drone AS repair management method according to an embodiment of the present invention.
Figure 5 is a flowchart of a drone AS repair management method according to another embodiment of the present invention.
6 is a diagram illustrating an example computing device that may implement devices and/or systems according to various embodiments of the present invention.

Hereinafter, the present invention will be described in detail with reference to embodiments of the present invention and drawings. These examples are merely presented as examples to explain the present invention in more detail, and it will be apparent to those skilled in the art that the scope of the present invention is not limited by these examples. .

Additionally, unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as commonly understood by a person skilled in the art to which the present invention pertains, and in case of conflict, this specification including definitions The description will take precedence.

In order to clearly explain the proposed invention in the drawings, parts unrelated to the description have been omitted, and similar reference numerals have been assigned to similar parts throughout the specification. And, when it is said that a part "includes" a certain component, this means that it does not exclude other components, but may further include other components, unless specifically stated to the contrary. Additionally, “unit” as used in the specification refers to a unit or block that performs a specific function.

Identification codes (first, second, etc.) for each step are used for convenience of explanation. The identification codes do not describe the order of each step, and each step does not clearly state a specific order in context. It may be carried out differently from the order specified above. That is, each step may be performed in the same order as specified, may be performed substantially simultaneously, or may be performed in the opposite order.

Figure 1 is a diagram schematically showing the configuration of a drone AS repair management system according to an embodiment of the present invention.

The drone AS repair management system 100 according to an embodiment of the present invention includes a defect symptom and damage evaluation unit 110, a repair unit 120, and a test flight unit 130.

The defect symptom and damage evaluation unit 110 determines the defect symptom of the drone received for after service (AS) and determines the damaged part of the drone.

Defect symptoms can be judged by referring to the records of defect symptoms/accident date/accident details/environment and conditions when the accident occurred, etc., written by the customer when registering for after-sales service online.

Repair engineers can conduct drone repair inspections based on self-inspection results and defect symptoms written by customers.

The drone AS repair management system 100 of the present invention is characterized in that it can be accurately mechanically performed using the internal storage device of the drone's airframe and transceiver by adding the judgment of the drone's defect symptoms to the judgment of the customer and repair engineer. .

The repair unit 120 repairs the drone by replacing damaged parts.

The repair unit 120 of the present invention may be an augmented reality execution unit that assists a repair engineer repairing a drone.

In one embodiment, the repair unit 120 may include an augmented reality (AR) execution unit connected to artificial intelligence (AI). Repair engineers can wear AR goggles to inspect drone equipment and run a dedicated app to repair drones.

In one embodiment, the repair unit 120 may be a robot arm connected to artificial intelligence (AI).

If a damaged part is determined in the defect symptom and damage evaluation unit 110, the drone can be repaired by controlling the robot arm according to commands from artificial intelligence (AI) based on the repair manual for the damaged part.

In particular, in the case of large drones that cannot be repaired by a repair engineer at a workbench, repair using a robotic arm connected to artificial intelligence (AI) can be a very effective method.

The test flight unit 130 checks whether the repaired drone operates normally.

The test flight unit 130 performs a test flight on a drone that has been repaired by a computer or artificial intelligence (AI) within an indoor test space, and compares the flight log records extracted from the test flight with the sample log records of the same product. You can determine whether it is operating normally.

In one embodiment, the test flight unit 130 may check whether the gimbal mounted on the drone is operating normally or whether the gimbal is in focus.

The reason why a drone can fly stably and record stable images without shaking is because a camera equipped with a gimbal function is linked to a remote control (controller) and an app. Gimbals are currently the only way to enable stable aerial photography, and they support stable photo or video shooting by maintaining a level position in any situation.

Figure 2 is a diagram schematically showing the configuration of a defect symptom and damage evaluation unit according to an embodiment of the present invention.

The defect symptom and damage evaluation unit 110 according to an embodiment of the present invention includes an outlier extraction unit 210, a failure-related parts prediction unit 220, and a target repair part determination unit 230.

The outlier extractor 210 extracts outliers from each flight stored in the loaded aircraft and the flight record of the transceiver's internal storage device.

The flight records of the drone's aircraft and the internal storage device of the transceiver contain important information for determining whether there is a malfunction or the cause of the aircraft crash.

Flight log files can be used to obtain desired data about flight records using a dedicated analysis app.

Specifically, flight log files include flight date and time, aircraft name, flight time, battery usage record, total flight distance, maximum distance, maximum altitude, maximum speed, signal strength, GPS strength, Compass status, reaction speed, wind speed, etc. Contains information.

The outlier extractor 210 extracts outliers from data outside the normal range by considering environmental variables such as flight altitude, speed, and wind speed of the drone.

The failure-related parts prediction unit 220 derives failure-related parts predicted to be related to the occurrence of outliers in each flight.

For example, if the maximum altitude, speed, reaction speed, etc. are outside the normal flight range, damage to the blades can be expected.

The target repair part determination unit 230 determines the part with the highest cumulative number of predicted failure-related parts in the failure-related parts prediction unit for the entire flight of the aircraft and the transceiver internal storage device as the target part to be repaired.

For example, if a drone battery fails, the flight data recorded in the drone's flight log file will continue to show problems with information related to thrust or speed.

The target repair part determination unit 230 may additionally consider sensing values of various types of sensors attached to the drone when determining the target part.

For example, the vibration value sensed using a vibration sensor can be additionally considered for minor damage to the blade.

Figure 3 is a diagram schematically showing the configuration of a repair unit according to an embodiment of the present invention.

The repair unit 120 includes a virtual simulation unit 310.

The virtual simulation unit 310 simulates the virtual flight of the drone based on the flight records of the aircraft and the internal storage device of the transceiver.

The virtual simulation unit 310 includes a flight simulator and simulates the virtual flight of the drone by combining various flight information stored in the flight records of the aircraft and the transceiver's internal storage device.

The virtual simulation unit 310 is an application of a flight simulator and is capable of considering weather environmental factors such as wind speed and wind direction.

The virtual simulation unit 310 simulates whether normal flight of the drone is possible by repairing the target part using the normal operating range value of the target part.

The virtual simulation unit 310 is a simulator that seeks to perform preliminary verification before performing actual repair of the target component.

The virtual simulation unit 310 is a flight computer that provides navigation/flight control algorithms essential for drone flight, and a communication interface with a remote controller or transceiver/GCS (Ground Control System).

The method of conducting preliminary verification through the virtual simulation unit 310 is to simulate the flight information stored in the flight record of the aircraft and the internal storage device of the transceiver in software and apply it to the simulation. It has the advantage of being able to identify software defects without hardware.

The drone AS repair management system of the present invention can perform equipment failure diagnosis using machine learning.

The internal storage device of the drone's fuselage and transceiver is connected to a network to collect a vast amount of drone flight records in real time, extracting factors that cause drone failure or a drone crash, including the flight environment and operator manipulation, You can be warned in advance about environmental variables that may cause a breakdown or fall.

In another embodiment, environmental variables that may cause failure or crash can be installed as firmware on the remote controller or transceiver to prevent malfunction-causing flights by users who are inexperienced in controlling drones.

Figure 4 is a flowchart of a drone AS repair management method according to an embodiment of the present invention.

The drone AS repair management method according to an embodiment of the present invention determines the defect symptoms of the drone received for after service (AS) by the defect symptom and damage evaluation unit 110, and determines the damaged part of the drone. Includes symptom and damage assessment step (S410).

Before the defect symptom and damage evaluation step (S410), a step of receiving an AS repair application from the customer online through the AS reception department may be further included.

When registering for after-sales service repair, customers can provide information about drone malfunction symptoms.

The drone AS repair management method according to an embodiment of the present invention includes a repair step (S430) in which the drone is repaired by replacing damaged parts by the repair unit 120.

The drone AS repair management method according to an embodiment of the present invention includes a flight test step (S450) to check whether the drone for which repairs have been completed by the test flight unit 130 operates normally.

The defect symptom and damage evaluation step (S410) includes an outlier value extraction step of extracting outliers from individual flights stored in the flight record of the aircraft and the internal storage device of the transceiver loaded by the outlier extractor 210; A failure-related parts prediction step of deriving failure-related parts predicted by the failure-related parts prediction unit 220 to be related to the occurrence of an outlier in each flight; And the target repair part determination unit 230 determines the part with the highest cumulative number of predicted failure-related parts in the failure-related parts prediction unit for the entire flight of the aircraft and transceiver internal storage device as the target part to be repaired. Includes steps.

The repair step (S430) includes a virtual simulation step in which the virtual simulation unit 310 simulates the virtual flight of the drone based on the flight records of the aircraft and the internal storage device of the transceiver.

The virtual simulation unit 310 simulates whether normal flight of the drone is possible by repairing the target part using the normal operating range value of the target part.

Figure 5 is a flowchart of a drone AS repair management method according to another embodiment of the present invention.

Figure 5 shows a flow chart from the AS reception step (S510) to the invoice output and sending step (S526) of the drone AS repair management method.

Step S510 is the AS reception step. The AS reception step may be performed by the AS reception department (not shown) of the drone AS repair management system.

Customers apply for after-sales service online through the website. Upon receipt of AS, you will receive a registration number. Customers enter phone number, email address, address, or route information that allows them to check real-time progress as customer information.

The customer is informed of the overall repair progress and results via email or phone, and after the repair is completed, it is delivered to the customer's address.

At the time of online application, the customer can separately fill out the defect symptoms/accident date/accident details/environment and conditions when the accident occurred and provide them to the repair engineer. Data such as defect symptoms written by customers can be used as auxiliary data for accurate repairs.

Step S512 is the warehousing step. The AS reception step may be performed by the drone receipt confirmation unit (not shown) of the drone AS repair management system.

The receipt confirmation department confirms normal receipt by sending the invoice number of the package sent by the customer to the customer via email.

The receipt confirmation department prepares a list of products sent by the customer, their serial numbers, quantities, and the customer's personal items, and sends them to the customer's e-mail.

Additionally, the receipt confirmation department sends an email with photos of all items sent by the customer.

Examples of photographed items include (1) a photo of the delivery invoice sent by the customer, (2) photos of the front and back of all received products, (3) photos of the serial numbers (SN) of the products, and (4) personal items of the customer ( SD card, camera lens filter, etc.).

Step S514 is the damage evaluation step.

The damage evaluation step of step S514 corresponds to the defect symptom and damage evaluation step (S410) in FIG. 4 . Accordingly, the damage evaluation step may be performed by the defect symptom and damage evaluation unit 110.

In the damage evaluation step of step S514, the defect symptoms of the drone can be determined and the damaged parts of the drone can be determined according to the defect symptoms and damage evaluation step (S410) described above.

Additionally, in the damage evaluation stage of the S514 stage, the defect symptoms notified by the customer at the reception stage are additionally checked, and after inspecting the entire product, photos of the inspection results and damaged parts and a description of the defect symptoms are sent to the customer via email. do.

Through email as well as call service, customers are provided with an explanation of damaged parts of the product and problematic parts as a result of inspection, and are notified of which parts will be replaced.

Step S516 is the quotation sending step. The quotation sending step may be performed by the quotation sending department (not shown) of the drone AS repair management system. This is the step of preparing and sending an estimate for the costs incurred for damaged parts determined in the damage evaluation step of step S514.

The quotation sent includes the final repair cost, unit price for each part, and certification fee.

The estimate includes whether the customer was at fault, the unit price of each part, and the total repair cost.

Customers who receive a quote via email pay the fee.

Specific examples of what is written in the quotation are as follows, and different terms may be used in some expressions depending on the type and model group of the product.

전방 우측 암 손상,

anterior right arm injury;

anterior left arm injury;

Posterior left arm injury;

Top cover damaged,

middle frame variant,

Bottom cover damaged,

Gimbal mounting cover damaged,

Camera module damage,

battery swollen,

Gimbal motor overload (gimbal axis arm damage).

Step S518 is the shipment material confirmation step. The shipment material confirmation step may be performed by the shipment material confirmation unit (not shown) of the drone AS repair management system.

When the customer's payment according to the quotation has been completed, the shipping materials are released from the material warehouse and the materials are delivered to the repair department.

Step S520 is the drone repair step. The drone repair step is performed by the repair unit 120 of the drone AS repair management system. The drone repair step in S520 corresponds to the repair step (S430) in FIG. 4.

In the drone repair stage, repairs are carried out by replacing defective parts with normal parts (good products), and the serial numbers of the replaced parts are entered into a separate serial number management site.

When you enter the customer registration number and the serial number of the part to be replaced on the serial number management site, the customer management serial number is automatically changed on the basic computer site.

The S522 phase is the flight test phase. The flight test step is performed by the test flight unit 130 of the drone AS repair management system. The flight test step corresponds to the flight test step (S450) of FIG. 4.

The flight test stage includes checking whether the camera (gimbal) mounted on the drone is tilted and focused properly.

The flight test stage is a process to check whether the repaired product can fly and function properly after repair and before shipping. If everything is normal during the flight test stage, 'PASS' is given and shipment is proceeded.

Step S524 is a quality check step. The quality check step is performed by the quality check department (not shown) of the drone after-sales service repair management system.

The quality check step is a quality check process for products to be shipped to customers, and is intended to check whether all products are shipped when they are received.

In the quality check stage, in order to check whether all products have been shipped, a shipment photo similar to the receipt photo is taken and sent by email.

Step S526 is the invoice output and sending step. The invoice printing and sending steps may be performed by the sending department (not shown) of the drone AS repair management system.

In the invoice printing and shipping stage, the repaired drone is delivered by courier to the customer's address. At the invoice printing and sending stage, the printed invoice number, courier company, and invoice photo are sent to the customer's email.

6 is a diagram illustrating an example computing device that may implement devices and/or systems according to various embodiments of the present invention.

An exemplary computing device 600 capable of implementing devices according to some embodiments of the present disclosure will be described in more detail with reference to FIG. 6 .

The computing device 600 includes one or more processors 610, a bus 650, a communication interface 670, a memory 630 that loads a computer program 691 executed by the processor 610, and a computer. It may include a storage 690 that stores the program 691. However, only components related to the embodiment of the present disclosure are shown in FIG. 6.

Accordingly, a person skilled in the art to which this disclosure pertains can see that other general-purpose components other than those shown in FIG. 6 may be further included.

The processor 610 controls the overall operation of each component of the computing device 600. The processor 610 includes a Central Processing Unit (CPU), Micro Processor Unit (MPU), Micro Controller Unit (MCU), Graphic Processing Unit (GPU), or any type of processor well known in the art of the present disclosure. It can be. Additionally, the processor 610 may perform operations on at least one application or program to execute a method according to embodiments of the present disclosure. Computing device 600 may include one or more processors.

The memory 630 stores various data, commands and/or information. Memory 630 may load one or more programs 691 from storage 690 to execute methods according to embodiments of the present disclosure. The memory 630 may be implemented as a volatile memory such as RAM, but the technical scope of the present disclosure is not limited thereto.

Bus 650 provides communication functionality between components of computing device 600. The bus 650 may be implemented as various types of buses, such as an address bus, a data bus, and a control bus.

The communication interface 670 supports wired and wireless Internet communication of the computing device 600. Additionally, the communication interface 670 may support various communication methods other than Internet communication. To this end, the communication interface 670 may be configured to include a communication module well known in the technical field of the present disclosure.

According to some embodiments, communication interface 670 may be omitted.

The storage 690 can non-temporarily store the one or more programs 691 and various data.

The storage 690 may be a non-volatile memory such as Read Only Memory (ROM), Erasable Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), flash memory, a hard disk, a removable disk, or a device well known in the art to which this disclosure pertains. It may be configured to include any known type of computer-readable recording medium.

The computer program 691, when loaded into the memory 630, may include one or more instructions that cause the processor 610 to perform methods/operations according to various embodiments of the present disclosure. That is, the processor 610 can perform methods/operations according to various embodiments of the present disclosure by executing the one or more instructions.

In this specification, only a few examples of various embodiments performed by the present inventors are described, but the technical idea of the present invention is not limited or limited thereto, and of course, it can be modified and implemented in various ways by those skilled in the art.

Claims (8)

A defect symptom and damage evaluation unit that determines defect symptoms of a drone received for after service (AS) and determines damaged parts of the drone;
a repair unit that repairs the drone by replacing the damaged parts; and
Includes a test flight unit that checks whether the repaired drone operates normally,
The defect symptom and damage evaluation department,
an outlier extractor that extracts outliers from each flight stored in the loaded aircraft and the flight record of the transceiver's internal storage device;
a failure-related parts prediction unit that derives failure-related parts predicted to be related to the occurrence of outliers in the individual flights; and
Comprising: a target repair part determination unit that determines, as a target part to be repaired, the part with the highest cumulative number of predicted failure-related parts in the failure-related parts prediction unit for the entire flight of the aircraft and the transceiver internal storage device;
The repair department,
It includes a virtual simulation unit that simulates a virtual flight of the drone based on the flight records of the aircraft and the internal storage device of the transceiver,
The virtual simulation unit is a drone AS repair management system that simulates whether normal flight of the drone is possible by repairing the target part using the normal operating range value of the target part. In claim 1,
The test flight unit is a drone AS repair management system, characterized in that it checks whether the gimbal mounted on the drone is operating normally or whether the gimbal is in focus. delete In claim 1,
A drone AS repair management system, characterized in that the repair unit is an augmented reality execution unit that assists a repair engineer repairing the drone. delete A defect symptom and damage evaluation step in which the defect symptom and damage evaluation unit determines the defect symptom of a drone received for after service (AS) and determines a damaged part of the drone;
A repair step of repairing the drone by replacing the damaged parts by a repair department; and
Including a flight test step of checking whether the drone, which has been repaired by the test flight department, operates normally,
The defect symptom and damage evaluation step is,
An outlier extraction step of extracting outliers from individual flights stored in the flight record of the aircraft loaded by the outlier extraction unit and the internal storage device of the transceiver;
A failure-related parts prediction step of deriving failure-related parts predicted to be related to the occurrence of an outlier in the individual flight by a failure-related parts prediction unit; and
A target part determination step of determining as a target part to be repaired the part with the highest cumulative number of predicted failure-related parts by the target repair part determination unit for the entire flight of the aircraft and the transceiver internal storage device; Including,
The repair step is,
A virtual simulation step of simulating the virtual flight of the drone by a virtual simulation unit based on the flight records of the aircraft and the internal storage device of the transceiver,
The virtual simulation unit simulates whether normal flight of the drone is possible by repairing the target part using the normal operating range value of the target part. delete delete

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