Urban air traffic noise reduction system and method

Abstract

An urban air traffic noise reduction system and method are provided, wherein the urban air traffic (UAM) noise reduction system includes: a UAM configured to detect and provide revolutions per minute (RPM) information and position coordinate information of a propeller; and a noise cancellation device configured to predict a noise cancellation sound wave amplitude based on the RPM information and position coordinate information of the propeller received through the UAM, and output a noise cancellation sound wave corresponding to the predicted noise cancellation sound wave amplitude to the UAM.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of Korean Patent Application No. 10-2021-0065335 filed on May 21, 2021, in the Korean Intellectual Property Office, the disclosure of which is incorporated herein in its entirety for all purposes by reference. Technical Field

[0003] The present disclosure relates to a noise reduction method. Background Art

[0004] Urban air mobility (hereinafter referred to as "UAM"), which transcends the limitations of time and space, is becoming the leader of future urban transportation.

[0005] In recent years, companies around the world such as EHang, Uber, Toyota, etc. have successively announced strategies related to the development of UAM, proving that three-dimensional (3D) air transportation is the key to the next generation of mobile travel.

[0006] In South Korea, the Ministry of Land, Infrastructure and Transport reported that "urban aerial roads will be opened in 2025", and Hyundai Motor Group exhibited a UAM model at CES2020 held in January 2020 with the theme of future mobility solutions.

[0007] Among the many issues of UAM, one that needs to be urgently addressed is noise.

[0008] Aircraft are usually the vehicles that make a lot of noise, and even large drones can make a lot of noise.

[0009] UAM (i.e. aircraft moving around cities) cannot be commercialized when the noise emitted by them is as loud as that emitted by existing aircraft.

[0010] Uber's proposed low noise standard is 62dB@500ft, and many companies are working hard to address this issue.

[0011] Various attempts have been made to reduce the noise of existing UAMs, for example by opting for vertical take-off and landing systems that use electric power instead of engines, or using distributed rotors instead of a single rotor.

[0012] However, noise cannot be completely reduced simply by analyzing and eliminating the causes of aircraft noise, so complementary approaches that reduce noise in different ways may be a new solution. Summary of the invention

[0013] This Summary is provided to introduce some concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed claims, nor is it intended to be used as an aid in determining the scope of the claimed claims.

[0014] In one general aspect, an urban air traffic (UAM) noise reduction system is provided, comprising: a UAM configured to detect and provide revolutions per minute (RPM) information and position coordinate information of a propeller; and a noise cancellation device configured to predict a noise cancellation sound wave amplitude based on the RPM information and position coordinate information of the propeller received via the UAM, and output a noise cancellation sound wave corresponding to the predicted noise cancellation sound wave amplitude to the UAM.

[0015] The noise cancellation device may include: a communicator configured to receive RPM information and position coordinate information of a propeller through communication with a UAM; a deep learning machine configured to predict the amplitude of noise cancellation sound waves based on the RPM information of the propeller and the position coordinate information of the UAM received through the communicator; one or more speakers configured to output noise cancellation sound waves; and an output controller configured to control the one or more speakers to output the noise cancellation sound wave amplitude predicted by the deep learning machine.

[0016] The UAM noise reduction system may include a weather information inputter configured to receive weather information, wherein the deep learning machine may predict the noise cancellation sound wave amplitude based on the RPM information of the propeller, the location coordinate information of the UAM, and the weather information.

[0017] The UAM noise reduction system may include: a microphone configured to receive noise generated in response to a UAM takeoff or landing; and a noise cancellation sound wave calculator configured to calculate a displacement wave using the noise received through the microphone.

[0018] The UAM noise reduction system may include a verifier configured to verify a noise cancellation sound wave amplitude predicted from the offset wave and manage the verified noise cancellation sound wave amplitude by storing the verified noise cancellation sound wave amplitude in a database.

[0019] In another general aspect, a processor-implemented urban air traffic (UAM) noise reduction method is provided, comprising: receiving revolutions per minute (RPM) information and position information of a propeller from the UAM; determining whether the accumulated data is sufficient by analyzing the alternating effects between the RPM information and the position information as factors received from the UAM; in response to determining that the accumulated data is sufficient, predicting a noise cancellation sound wave amplitude by applying the received factors to a deep learning machine; in response to determining that the accumulated data satisfies a reference reliability, storing information about the factors used to predict the noise cancellation sound wave amplitude in a database; and outputting the predicted noise cancellation sound wave amplitude through a speaker.

[0020] The UAM noise reduction method may include, in response to determining that the accumulated data is insufficient, calculating the noise cancellation sound wave amplitude using noise information collected by the microphone.

[0021] The UAM noise reduction method may include: determining whether weather information is input; and in response to determining that the weather information is input, analyzing the alternating effects between factors using RPM information and position information of the propeller received from the UAM and the weather information.

[0022] The UAM noise reduction method may include, in response to determining that no weather information is input, receiving weather information from a server of a meteorological bureau by web crawling or deep learning weather information using a database.

[0023] In another general aspect, an urban air traffic (UAM) noise reduction system is provided, comprising: an offset wave outputter disposed at a mobile transfer station and configured to output an offset wave to offset noise generated by the UAM; a microphone configured to receive noise generated in response to takeoff or landing of the UAM; a noise cancellation sound wave calculator configured to calculate the offset wave using the noise received through the microphone; and an output controller configured to control the offset wave to be output to the UAM through a speaker.

[0024] Other features and aspects will be apparent from the following detailed description, drawings, and claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The above and other objects, features and advantages of the present disclosure will become more apparent to those skilled in the art by describing in detail exemplary embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0026] Figure 1 is a configuration diagram for describing an urban air traffic (UAM) noise reduction system according to an embodiment of the present disclosure;

[0027] Figure 2 Is used to describe Figure 1 A block diagram of a detailed configuration of a noise cancellation device;

[0028] Figure 3 is a block diagram for describing a UAM noise reduction system according to another embodiment of the present disclosure; Figure 4 A flow chart of a UAM noise reduction method according to an embodiment of the present disclosure; and Figure 5 is a block diagram for describing a UAM noise reduction system according to another embodiment of the present disclosure.

[0029] Throughout the drawings and detailed description, unless otherwise described or provided, the same drawing reference numerals will be understood to refer to the same elements, features, and structures. The drawings may not be to scale, and the relative sizes, proportions, and depictions of elements in the drawings may be exaggerated for clarity, illustration, and convenience. DETAILED DESCRIPTION

[0030] The following detailed description is provided to assist the reader in obtaining a comprehensive understanding of the methods, devices and / or systems described herein. However, after understanding the disclosure of the present application, various changes, modifications and equivalents of the methods, devices and / or systems described herein will be apparent. For example, the operation sequences described herein are merely examples and are not limited to those described herein, but except for operations that must occur in a specific order, changes can be made after understanding the disclosure of the present application. In addition, in order to improve clarity and simplicity, descriptions of features known in the art may be omitted.

[0031] The features described herein may be embodied in different forms and should not be construed as being limited to the examples described herein. Rather, the examples described herein have been provided merely to illustrate some of the many possible implementations of the methods, devices, and / or systems described herein that will become apparent after understanding the disclosure of the present application.

[0032] The terms used herein are only used for the purpose of describing specific examples and are not intended to limit the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular forms "one", "an", "the" are also intended to include plural forms. As used herein, the term "and / or" includes any one or any combination of any two or more related listed items. As used herein, the terms "include", "comprise", "have", etc. specify the existence of stated features, numbers, operations, elements, parts and / or their combinations, but do not exclude the existence or addition of one or more other features, numbers, steps, elements, parts and / or their combinations.

[0033] Throughout the specification, when a component is described as being "connected to" or "coupled to" another component, it may be directly "connected to" or "coupled to" the other component, or one or more other components may intervene. Conversely, when an element is described as being "directly connected to" or "directly coupled to" another element, no other elements may intervene. Similarly, similar expressions, such as "between" and "immediately between" and "adjacent" and "immediately adjacent", should also be interpreted in the same manner. As used herein, the term "and / or" includes any one or any combination of any two or more of the associated listed items.

[0034] Although terms such as "first," "second," and "third" may be used herein to describe various components, parts, regions, layers, or portions, these components, parts, regions, layers, or portions are not limited by these terms. Instead, these terms are only used to distinguish one component, part, region, layer, or portion from another component, part, region, layer, or portion. Therefore, without departing from the teachings of the examples described herein, the first component, part, region, layer, or portion mentioned in the examples may also be referred to as the second component, part, region, layer, or portion.

[0035] Figure 1 is a configuration diagram for describing an urban air traffic (UAM) noise reduction system according to an embodiment of the present disclosure.

[0036] The UAM noise reduction system according to an embodiment of the present disclosure includes a UAM 100 and a noise cancellation device 200 .

[0037] The UAM 100 is a transportation device capable of vertical takeoff or landing using a plurality of rotors, and detects revolutions per minute (RPM) information and position coordinate information of a propeller and provides the RPM information and the position coordinate information to the noise cancellation apparatus 200 .

[0038] The noise cancellation device 200 predicts the noise cancellation sound wave amplitude based on the RPM information and the position coordinate information of the propeller received through the UAM 100 , and outputs the noise cancellation sound wave corresponding to the predicted noise cancellation sound wave amplitude to the UAM 100 .

[0039] In an embodiment of the present disclosure, the noise cancellation device 200 includes a communicator 210, a deep learning machine 220, a speaker 230, and an output controller 240. Figure 2 shown.

[0040] The communicator 210 receives the PRM information and the position information of the propeller through communication with the UAM 100. In the present embodiment, the communicator 210 is not limited, and various types of communicator devices may be used as the communicator 210.

[0041] The deep learning machine 220 predicts the noise cancellation sound wave amplitude from the RPM information of the propeller and the position information of the UAM 100 received through the communicator 210 .

[0042] The speaker 230 outputs noise cancellation sound waves, and a huge audio system can be formed by installing one or more speakers 230 in the mobile transfer station.

[0043] The output controller 240 controls at least one speaker 230 to output the noise cancellation sound wave amplitude predicted by the deep learning machine 220 .

[0044] According to an embodiment of the present disclosure, the noise generated by a UAM that is taking off or landing at a mobile transfer station can be reduced.

[0045] According to an embodiment of the present disclosure, certain levels of noise generated by a UAM, including rotor noise, motor rotation noise, engine noise, etc., may be significantly reduced.

[0046] Furthermore, according to an embodiment of the present disclosure, noise may be automatically controlled according to the user's location or surrounding conditions.

[0047] Figure 3 is a block diagram for describing a UAM noise reduction system according to another embodiment of the present disclosure.

[0048] In another embodiment of the present disclosure, the UAM noise reduction system may further include a weather information inputter 300, and the deep learning machine 220 may predict the noise cancellation sound wave amplitude by further considering the weather information. In this embodiment, the weather information inputter 300 may collect weather information directly from sensing devices such as thermometers, hygrometers, air flow meters, and precipitation meters, or collect weather information by web crawling on the website of the Korea Meteorological Administration (KMA).

[0049] As described above, when predicting the noise cancellation sound wave amplitude, weather information may be further considered to increase the noise reduction effect by accurately offsetting the change in the noise cancellation sound wave amplitude according to weather conditions during takeoff or landing of the same UAM.

[0050] In another embodiment of the present disclosure, the UAM noise reduction system may further include a microphone 400 and a noise cancellation sound wave calculator 500 .

[0051] Microphone 400 receives actual noise generated when the UAM takes off or lands.

[0052] The noise cancellation sound wave calculator 500 may digitize the actual noise generated by the UAM 100 and received through the microphone 400 and calculate a shifted wave having an inverse wavelength.

[0053] Therefore, in an embodiment of the present disclosure, the UAM noise reduction system may further include a verification unit or verifier 600.

[0054] The verification unit or verifier 600 may verify the noise cancellation sound wave amplitude predicted from the noise cancellation sound wave amplitude calculated by the noise cancellation sound wave calculator 500. In an example, the verification unit or verifier 600 may verify the noise cancellation sound wave amplitude predicted from the offset wave calculated by the noise cancellation sound wave calculator 500. The verification unit or verifier 600 may manage the verified noise cancellation sound wave amplitude by storing the verified noise cancellation sound wave amplitude in a database, thereby predicting the noise cancellation sound wave amplitude more accurately.

[0055] The following will refer to Figure 4 A UAM noise reduction method according to an embodiment of the present disclosure is described.

[0056] First, RPM information and position information of the propeller are received from the UAM (S110).

[0057] Next, it is determined whether weather information is input (S120).

[0058] When it is determined that weather information is input (Yes) in determining whether weather information is input (S120), the alternating effects between factors are analyzed to determine whether the accumulated data is sufficient (S130). The factors analyzed in the embodiment of the present disclosure may include RPM information and position information of the propeller, or may include RPM information, position information, and weather information of the propeller.

[0059] When it is determined that the accumulated data is sufficient (Yes) in determining whether the accumulated data is sufficient ( S130 ), the noise cancellation sound wave amplitude is predicted by applying the input factors to the deep learning machine ( S140 ).

[0060] Thereafter, the predicted noise cancellation sound wave amplitude is output through the speaker (S150).

[0061] When it is determined that weather information is not input (No) in determining whether weather information is input (S120), weather information is received from a server of the Meteorological Bureau through web crawling or weather information is learned through deep learning by using a database of the UAM noise reduction system (S121).

[0062] After the noise cancellation sound wave amplitude is predicted (S140), the predicted noise cancellation sound wave amplitude is managed by storing it in a database. To this end, it is determined whether the accumulated data satisfies the reference reliability.

[0063] When it is determined that the accumulated data satisfies the reference reliability in determining whether the accumulated data satisfies the reference reliability, information about factors for predicting the noise cancellation sound wave amplitude is stored in the database. The noise cancellation sound wave amplitude stored in the database and learned can be verified by comparing it with the noise cancellation sound wave amplitude calculated from the noise collected by the microphone 400.

[0064] When it is determined that the accumulated data is insufficient (No) in determining whether the accumulated data is sufficient (S130), noise information is collected through the microphone 400 (S160), and the noise cancellation sound wave amplitude is calculated using the information about the noise of the UAM 100 (S170), and then the noise cancellation sound wave amplitude is output through the speaker (S150).

[0065] Figure 5 is a block diagram for describing a UAM noise reduction system according to another embodiment of the present disclosure.

[0066] like Figure 5 As shown, the UAM noise reduction system according to an embodiment of the present disclosure includes a microphone 400 , a noise cancellation sound wave calculator 500 , and a speaker 230 .

[0067] Microphone 400 receives actual noise generated when the UAM takes off or lands.

[0068] The noise cancellation sound wave calculator 500 may calculate an offset wave using actual noise generated by the UAM 100 and received through the microphone 400 .

[0069] A plurality of speakers 230 may be provided at the mobile transfer station to output a deviation wave for deflecting the noise generated by the UAM 100 .

[0070] The output controller 240 controls the calculated offset wave to be output to the UAM 100 through the plurality of speakers 230 .

[0071] As described above, according to another embodiment of the present disclosure, a UAM noise reduction system may be provided at a mobile transfer station to receive through a microphone the actual noise generated when the UAM takes off or lands, calculate an offset wave based on the actual noise received through the microphone, and output the calculated offset wave to the UAM through a plurality of speakers, thereby reducing the noise generated during takeoff or landing of the UAM.

[0072] According to an embodiment of the present disclosure, the noise problem, which is the biggest problem of the air traffic system when taking off or landing, can be solved using artificial intelligence and active noise control (ANC) of the HUB audio system.

[0073] As described above, an urban air traffic (UAM) noise reduction device is provided in which a mobile hub (HUB) is configured as a single large audio device to reduce noise during UAM takeoff or landing using active noise control (ANC) and artificial intelligence.

[0074] Although the configurations of the present disclosure have been described in detail above with reference to the accompanying drawings, these configurations are merely examples, and a person skilled in the art in the art to which the present disclosure relates may make various modifications and changes within the scope of the present disclosure. Therefore, the scope of the present disclosure is not limited to the above embodiments, and should be defined by the appended claims.

[0075] Each step included in the above learning method may be implemented as a software module, a hardware module, or a combination thereof executed by a computing device.

[0076] Furthermore, the elements for executing each step may be implemented as first to second operation logics of the processor, respectively.

[0077] For the sake of clarity, the exemplary method according to the embodiment may be represented as a series of operations, but such steps do not limit the sequence in which the operations are performed. Depending on the circumstances, the steps may be performed simultaneously or in different orders.

[0078] In order to implement the method according to the embodiment, the disclosed steps may additionally include another step, include steps other than some steps, or include another additional step other than some steps.

[0079] The various embodiments of the present disclosure do not list all available combinations but are used to describe representative aspects of the present disclosure, and the description of the various embodiments may be applied independently or may be applied by a combination of two or more.

[0080] The devices, units, modules and components described herein are implemented by hardware components. Where appropriate, examples of hardware components that can be used to perform the operations described in this application include controllers, sensors, generators, drivers, memories, comparators, arithmetic logic units, adders, subtractors, multipliers, dividers, integrators, and any other electronic components configured to perform the operations described in this application. In other examples, one or more hardware components that perform the operations described in this application are implemented by computing hardware, such as by one or more processors or computers. A processor or computer can be implemented by one or more processing elements, such as a logic gate array, a controller and an arithmetic logic unit, a digital signal processor, a microcomputer, a programmable logic controller, a field programmable gate array, a programmable logic array, a microprocessor, or any other device or device combination, which is configured to respond and execute instructions in a defined manner to achieve the desired result. In one example, a processor or computer includes or is connected to one or more memories storing instructions or software executed by a processor or computer. The hardware components implemented by a processor or computer can execute instructions or software, such as an operating system (OS) and one or more software applications running on the OS, to perform the operations described in this application. In response to the execution of instructions or software, hardware components can also access, manipulate, process, create and store data. For simplicity, the singular term "processor" or "computer" can be used in the examples described in this application, but in other examples, multiple processors or computers can be used, or the processor or computer can include multiple processing elements, or multiple types of processing elements, or both. For example, a single hardware component or two or more hardware components can be implemented by a single processor, or two or more processors, or a processor and a controller. One or more hardware components can be implemented by one or more processors or a processor and a controller, and one or more other hardware components can be implemented by one or more other processors or another processor and another controller. One or more processors or a processor and a controller can implement a single hardware component, or two or more hardware components. The hardware components may have any one or more of different processing configurations, examples of which include a single processor, an independent processor, a parallel processor, a single instruction single data (SISD) multiprocessing, a single instruction multiple data (SIMD) multiprocessing, a multiple instruction single data (MISD) multiprocessing, a multiple instruction multiple data (MIMD) multiprocessing, a controller and an arithmetic logic unit (ALU), a DSP, a microcomputer, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a programmable logic unit (PLU), a central processing unit (CPU), a graphics processing unit (GPU), a neural processing unit (NPU), or any other device capable of responding to and executing instructions in a prescribed manner.

[0081] The methods for performing the operations described in this application are performed by computing hardware, such as by one or more processors or computers implemented as described above, executing instructions or software to perform the operations performed by these methods described in this application. For example, a single operation or two or more operations can be performed by a single processor, or two or more processors, or a processor and a controller. One or more operations can be performed by one or more processors or a processor and a controller, and one or more other operations can be performed by one or more other processors or another processor and another controller. One or more processors or a processor and a controller can perform a single operation or two or more operations.

[0082] The instructions or software for controlling a processor or computer to implement the above-mentioned hardware components and perform the above-mentioned methods are written as computer programs, code segments, instructions or any combination thereof, which are used to individually or collectively instruct or configure a processor or computer to operate as a machine or a special-purpose computer to perform the operations performed by the above-mentioned hardware components and methods. In one example, the instructions or software include machine code directly executed by a processor or computer, such as machine code generated by a compiler. In one example, the instructions or software include at least one of an applet, a dynamic link library (DLL), middleware, firmware, a device driver, and an application program storing a method for reducing noise in urban air traffic (UAM). In another example, the instructions or software include high-level code executed by a processor or computer using an interpreter. Those of ordinary skill in the art can easily write instructions or software based on the block diagrams and flow charts shown in the accompanying drawings and the corresponding descriptions in the specification, which discloses algorithms for performing operations performed by hardware components and methods as described above.

[0083] Instructions or software for controlling a processor or computer to implement the above-mentioned hardware components and perform the above-mentioned methods, as well as any associated data, data files and data structures, are recorded, stored or fixed in or on one or more non-transitory computer-readable storage media. Examples of non-transitory computer-readable storage media include read-only memory (ROM), random access programmable read-only memory (PROM), electrically erasable programmable read-only memory (EEPROM), random access memory (RAM), magnetic memory (MRAM), spin transfer torque (STT)-MRAM, static random access memory (SRAM), thyristor RAM (T-RAM), zero capacitance RAM (Z-RAM), two-transistor RAM (TTRAM), conductive bridge RAM (CBRAM), ferroelectric RAM (FeRAM), phase change random access memory (PRA) M), resistive RAM (RRAM), nanotube RRAM, polymer RAM (PoRAM), nano floating gate memory (NFGM), holographic memory, molecular electronic memory device), insulator resistance change memory, dynamic random access memory (DRAM), static random access memory (SRAM), flash memory, non-volatile memory, optical disk, CD-ROM, CD-R, CD+R, CD-RW, CD+RW, DVD-ROM, DVD-R, DVD+R, DVD-RW, DVD+RW, DVD-RAM, BD-ROM, BD-R, BD-R LTH, BD-RE, Blu-ray or optical disk storage, hard disk drive (HDD), solid state drive (SSD), flash memory, card-type memory such as a multimedia card micro or card (e.g., secure digital (SD) or extreme digital (XD)), magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid state disk, and any other device configured to store instructions or software and any related data, data files and data structures in a non-transitory manner and provide the instructions or software and any related data, data files and data structures to a processor or computer so that the processor or computer can execute the instructions. In one example, the instructions or software and any associated data, data files and data structures are distributed on a network-coupled computer system so that the instructions and software and any associated data, data files and data structures are stored, accessed and executed by one or more processors or computers in a distributed manner.

[0084] Although the present disclosure includes specific examples, it will be apparent after understanding the disclosure of the present application that various changes in form and detail may be made in these examples without departing from the spirit and scope of the claims and their equivalents. The examples described herein are to be considered illustrative only and not for purposes of limitation. The description of features or aspects in each example should be considered to apply to similar features or aspects in other examples. Suitable results may be achieved if the described techniques are performed in a different order, and / or if the components in the described systems, architectures, devices, or circuits are combined in different ways, and / or replaced or supplemented by other components or their equivalents.

[0085] Therefore, the scope of the disclosure is defined not by the detailed description but by the claims and their equivalents, and all variations within the scope of the claims and their equivalents should be construed as being included in the disclosure.

Claims

1. An urban air traffic noise reduction system, comprising: Urban Air Mobility, configured to detect and provide propeller RPM information and position coordinate information; as well as a noise cancellation device configured to predict a noise cancellation sound wave amplitude based on the RPM information and the position coordinate information of the propeller received through the urban air traffic, and output a noise cancellation sound wave corresponding to the predicted noise cancellation sound wave amplitude to the urban air traffic; a microphone configured to receive noise generated in response to a takeoff or landing of the urban air traffic; a noise-canceling sound wave calculator configured to calculate a displacement wave using the noise received through the microphone; as well as A verifier is configured to verify the noise cancellation sound wave amplitude predicted from the offset wave and manage the verified noise cancellation sound wave amplitude by storing the verified noise cancellation sound wave amplitude in a database.

2. The urban air traffic noise reduction system according to claim 1, wherein: The noise cancellation device comprises: a communicator configured to receive the RPM information and the position coordinate information of the propeller through communication with the urban air traffic; a deep learning machine configured to predict the noise cancellation sound wave amplitude based on the RPM information of the propeller and the position coordinate information of the urban air traffic received through the communicator; one or more speakers configured to output the noise cancellation sound waves; and An output controller is configured to control the one or more speakers to output the noise cancellation sound wave amplitude predicted by the deep learning machine.

3. The urban air traffic noise reduction system according to claim 2, further comprising a weather information input device, wherein the weather information input device is configured to receive weather information, in, The deep learning machine predicts the noise cancellation sound wave amplitude based on the RPM information of the propeller, the location coordinate information of the urban air traffic, and the weather information.

4. A processor-implemented urban air traffic noise reduction method, comprising: receiving propeller RPM information and position information from UAM; determining whether accumulated data is sufficient to predict noise cancellation sound wave amplitude by analyzing the trade-off effect between the RPM information and the position information as factors received from the urban air traffic; In response to determining that the accumulated data is sufficient, predicting a noise cancellation sound wave amplitude by applying the received factors to a deep learning machine, and storing the predicted noise cancellation sound wave amplitude in a database for management; In response to determining that the accumulated data satisfies a reference reliability, storing in the database information about the factors for predicting the amplitude of the noise cancellation sound wave; verifying the predicted noise cancellation sound wave amplitude stored in the database and learned by comparing with the noise cancellation sound wave amplitude calculated from the noise collected through the microphone, and The predicted noise cancellation sound wave amplitude is output through a speaker.

5. The urban air traffic noise reduction method of claim 4, further comprising, in response to determining that the accumulated data is insufficient, calculating a noise cancellation sound wave amplitude using noise information collected by a microphone.

6. The urban air traffic noise reduction method according to claim 4, further comprising: Determine whether weather information has been entered; as well as In response to determining that the weather information is input, the alternating effects between the factors are analyzed using the RPM information and the position information of the propeller received from the urban air traffic and the weather information.

7. The urban air traffic noise reduction method according to claim 4, further comprising, in response to determining that no weather information is input, receiving weather information from a server of a meteorological bureau by web crawling or using the database to deeply learn weather information.

8. An urban air traffic noise reduction system, comprising: a deviation wave outputter, disposed at the mobile transfer station and configured to output a deviation wave to deflect noise generated by urban air traffic; a microphone configured to receive noise generated in response to a takeoff or landing of the urban air traffic; a noise-canceling sound wave calculator configured to calculate the displacement wave using the noise received through the microphone; a verifier configured to verify the noise cancellation sound wave amplitude predicted from the offset wave and manage the verified noise cancellation sound wave amplitude by storing the verified noise cancellation sound wave amplitude in a database; as well as An output controller is configured to control the excursion wave to be output to the urban air traffic through a speaker.

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