Noise reduction method and device of unmanned aerial vehicle hangar, computer device and storage medium

Abstract

The application provides a noise reduction method and device for a UAV hangar, a computer device and a storage medium. A plurality of first sound sensors are arranged in the UAV hangar to comprehensively collect noise emitted by various devices in the UAV hangar, and an autoregressive model is constructed based on historical noise data collected by the first sound sensors. The UAV hangar can use a discrete state space description, use the coefficients obtained from the autoregressive model to construct the state space model. The state space model is used to predict the predicted noise output variable of the next N periods, and a target function is constructed based on the error between the predicted noise output variable and the expected output noise. Finally, the minimum value of the target function is obtained under the preset constraint condition, and the cancellation sound signal is obtained. The noise in the UAV hangar can be actively eliminated through the cancellation sound signal, which is conducive to the popularization and deployment of the UAV hangar.

Description

Technical Field

[0001] This application relates to the field of noise reduction technology, and in particular to a noise reduction method, apparatus, computer equipment, and storage medium for a drone hangar. Background Technology

[0002] With the development of drone technology and the widespread deployment of drone hangars, drone applications are gradually becoming "unmanned." Since load centers are often densely populated areas and key inspection points, hangars are frequently deployed on the rooftops of residential buildings or power distribution rooms to quickly locate faults and effectively inspect more lines. However, this has led to increasingly noticeable noise from these hangars at night, resulting in complaints and reports, thus hindering the deployment of fixed hangars. Simple soundproofing is insufficient to effectively reduce noise from fixed hangars, and because fixed drone hangars need to be frequently opened and closed during normal operation, noise cannot be statically eliminated like that of transformers. Therefore, a dynamic method for noise reduction is urgently needed. Summary of the Invention

[0003] The purpose of this application is to at least address one of the aforementioned technical defects, particularly the technical defect of excessive noise in drone hangars in the prior art.

[0004] In a first aspect, this application provides a noise reduction method for a drone hangar, wherein a speaker unit and multiple first sound sensors are installed inside the drone hangar, the first sound sensors being used to collect noise data, and the noise reduction method includes: Acquire historical noise data collected by multiple primary sound sensors; Based on historical noise data, construct an autoregressive model of order p; the autoregressive model includes autoregressive coefficients; Based on the autoregressive model, a state-space model is constructed; the state-space model includes noisy state variables and control input variables, and the autoregressive coefficients are used to generate the constant term of the noisy state variables; P noise data points, including the current time and the time adjacent to the current time, are assigned to the noise state variables in the state space model to obtain the predicted state variables for the next time step. Based on the predicted state variables for the next time step, the noise state variables in the state space model are recursively assigned to obtain the predicted state variables for the next N time steps. Based on each predicted state variable, the corresponding predicted noise output variable is obtained; Based on each predicted noise output variable and the expected output noise, an objective function is constructed; the objective function is used to reflect the error between each predicted noise output variable and the expected output noise. Based on the preset constraints, the solution is obtained by minimizing the objective function, and the solution of the control input variable is obtained. Based on the solution result, the speaker unit is controlled to generate a canceling sound wave signal.

[0005] In one embodiment, the preset constraints include at least one of the following: The amplitude of the input variable is controlled to not exceed the maximum output capability of the speaker unit; The magnitude of the predicted state variable is within the second limit range; The magnitude of the predicted noise output variable is within the first limit range.

[0006] In one embodiment, an objective function is constructed based on each predicted noise output variable and the expected output noise, including: Calculate the square of the difference between each predicted noise output variable and the expected output noise, and sum them to obtain the objective function.

[0007] In one embodiment, the objective function is solved using linear quadratic programming.

[0008] In one embodiment, the placement of the speaker unit is determined by the following steps: Determine more than one noise reduction direction; For any noise reduction direction, two or more measurement points are set at a preset interval along the noise reduction direction, and a second sound sensor is set at each measurement point; one of the measurement points is located on the surface of the drone hangar; With the drone hangar in operation and the speaker unit off, the data collected at the surface is compared with the data collected at the other measurement points to obtain the first transmission characteristic. With the drone hangar closed, the speaker unit plays a preset audio, and the data collected at the surface is compared with the data collected at the other measurement points to obtain the second transmission characteristic. Based on the difference between the first and second transmission characteristics, the position of the speaker unit is adjusted. After the adjustment is completed, the drone hangar is kept closed, the speaker unit plays the preset audio, and the data collected at the surface is compared with the data collected at the other measurement points. The process of obtaining the second transmission characteristic continues until the difference between the second and first transmission characteristics meets the error requirements.

[0009] In one embodiment, the base of the drone hangar is equipped with an adjustable vibration damping unit, and one or more vibration sensors are installed on the contact surface of the drone hangar base. The vibration sensors are used to collect vibration data, and the noise reduction method further includes: Based on the vibration data, adjust the damping of the adjustable shock absorber unit; the stronger the vibration data, the greater the damping.

[0010] In one embodiment, the output noise is expected to be a signal below the human ear's perception threshold.

[0011] Secondly, this application provides a noise reduction device for a drone hangar, wherein a speaker unit and multiple first sound sensors are installed inside the drone hangar, the first sound sensors being used to collect noise data, and the noise reduction device includes: The data acquisition module is used to acquire historical noise data collected by multiple first sound sensors; The first modeling module is used to construct a p-order autoregressive model based on historical noise data; the autoregressive model includes autoregressive coefficients. The second modeling module is used to construct a state-space model based on the autoregressive model. The state-space model includes noisy state variables and control input variables, and the autoregressive coefficients are used to generate the constant term of the noisy state variables. The recursive module is used to assign values ​​of p noise data points, including the current time and the time adjacent to the current time, to the noise state variables in the state space model to obtain the predicted state variables for the next time step. Based on the predicted state variables for the next time step, values ​​are recursively assigned to the noise state variables in the state space model to obtain the predicted state variables for the next N time steps. The noise prediction module is used to obtain the corresponding predicted noise output variable based on each predicted state variable; The objective function construction module is used to construct an objective function based on each predicted noise output variable and the expected output noise; the objective function is used to reflect the error between each predicted noise output variable and the expected output noise. The output module is used to solve the objective function based on preset constraints, and to control the speaker unit to generate a canceling sound wave signal based on the solution result.

[0012] Thirdly, this application provides a computer device including one or more processors and a memory storing computer-readable instructions. When the computer-readable instructions are executed by one or more processors, they perform the steps of the noise reduction method for the drone hangar in any of the above embodiments.

[0013] Fourthly, this application provides a storage medium storing computer-readable instructions, which, when executed by one or more processors, cause the one or more processors to perform the steps of the noise reduction method for the drone hangar in any of the above embodiments.

[0014] As can be seen from the above technical solutions, the embodiments of this application have the following advantages: Based on any of the above embodiments, multiple first sound sensors are installed within the drone hangar to more comprehensively collect noise emitted by various devices within the hangar. An autoregressive model is constructed based on the historical noise data collected by each first sound sensor. The drone hangar can be described using a discrete state-space model, which is then constructed using the coefficients obtained from the autoregressive model. The state-space model is then used to predict the predicted noise output variable for the next N periods. Based on the error between the predicted noise output variable and the expected output noise, an objective function is constructed. Finally, the objective function is minimized under preset constraints to obtain the canceled acoustic signal. This canceled acoustic signal can actively eliminate noise in the drone hangar, facilitating the widespread deployment of drone hangars. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A flowchart illustrating a noise reduction method for a drone hangar provided in one embodiment of this application; Figure 2 This is an internal structural diagram of a computer device provided in one embodiment of this application. Detailed Implementation

[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0018] This application provides a noise reduction method for a drone hangar, which includes a speaker unit and multiple first sound sensors installed inside the drone hangar. The multiple first sound sensors are arranged in an array around the interior of the drone hangar. Since there are many devices inside the drone hangar, each of which may emit noise, and the perceived noise may vary from direction to direction, this arrangement allows for a more comprehensive assessment of the drone hangar as a whole noise source. The speaker unit is a device used to generate sound wave signals that cancel out the noise from the drone hangar; that is, this embodiment employs active noise cancellation to eliminate noise from the drone hangar. For details, please refer to... Figure 1The noise reduction method includes steps S102 to S114.

[0019] S102, acquire historical noise data collected by multiple first sound sensors.

[0020] It is understood that during the operation of the drone hangar, each first sound sensor will collect the current noise data at each collection moment. In this embodiment, these noise data are stored to form historical noise data.

[0021] S104. Based on historical noise data, construct a p-order autoregressive model. The autoregressive model includes autoregressive coefficients.

[0022] An autoregressive model is a statistical model used to describe time series data. It assumes that the data at the current moment can be represented by a linear combination of the data at the previous p moments, where p is a positive integer called the autoregressive order. The noise data collected by each first sound sensor can be described by the corresponding autoregressive model, which can be expressed as:

[0023] in, x ( t () represents the noise data at time point t. c For constant terms, a i For the first i One autoregressive coefficient, p is The order of the autoregressive model, w(t This refers to unpredictable process noise. Traditional techniques already exist that utilize historical data for modeling to obtain autoregressive models, such as least squares and maximum likelihood methods; please refer to existing techniques for details. Furthermore, there are many ways to select the order of the autoregressive model, such as the Akaike information criterion and the Bayesian information criterion.

[0024] S106. Construct a state-space model based on the autoregressive model. The state-space model includes noisy state variables and control input variables, and the autoregressive coefficients are used to generate the constant term of the noisy state variables.

[0025] It can be understood that each noise data point can be represented by a discrete-time state space where the noise state is the state variable, the canceled sound wave signal output by the speaker unit is the control input variable, and the noise data collected by the first sound sensor is the output variable. For example: x(k+1) = A x(k) + u(k) + w(k)

[0026] y(k) = C x(k) + v(k)

[0027] Among them, A x(k) represents the noise state variable. x(k) is a p×1 matrix, where each p element represents the noise state at a given p time step forward from time k. A is a p×p matrix, with the first row containing autoregressive coefficients and the remaining rows being unit lower triangular matrices. u(k) represents the control input variable, a p×1 matrix where the first element represents the canceled acoustic signal emitted into the UAV hangar at time k, and the remaining elements are 0. w(k) is a p×1 vector representing the process noise in the autoregressive model; the first element can be chosen as white noise ε(k), and the remaining elements are zero. Since we assume the noise model is an autoregressive model, and the output of the autoregressive model is the current state, we only need to use the first element of the state vector to represent the noise output, without needing the other elements. This simplifies the output equation of the noise model, making it easier to process. Therefore, C is a 1×p matrix with the first element being 1 and the remaining elements being zero; v(k) is a scalar representing the measurement noise of the first sound sensor.

[0028] S108: P noise data points, including the current time and the time adjacent to the current time, are assigned to the noise state variables in the state space model to obtain the predicted state variables for the next time step. Based on the predicted state variables for the next time step, values ​​are recursively assigned to the noise state variables in the state space model to obtain the predicted state variables for the next N time steps.

[0029] After completing the above modeling work, the state-space model can be used to predict future noise changes. Specifically, for any first sound sensor, substituting the current time and p noise data points before the current time into x(k) in the above expression, the resulting x(k+1) is the predicted state variable for the next time step. Substituting x(k+1) recursively in this way yields multiple predicted state variables x(k+2)...x(k+N). The first element of each predicted state variable represents the noise state containing the cancellation of the sound wave signal as an unknown quantity.

[0030] S110: Based on each predicted state variable, obtain the corresponding predicted noise output variable.

[0031] It is understood that, due to the simplification process in this application, the noise state is the noise data, and the first element in each predicted state variable is the noise data containing the unknown quantity of canceling the acoustic signal.

[0032] S112, Construct an objective function based on each predicted noise output variable and the expected output noise. The objective function is used to reflect the error between each predicted noise output variable and the expected output noise.

[0033] It can be understood that the expected output noise is the noise data after active noise suppression, as desired. Each first sound sensor corresponds to N predicted state variables. The first element of each predicted state variable can be compared with the expected output noise, and all comparison results are summed to obtain the objective function. Assuming there are m first sound sensors, there will be m×N error terms to sum. By calculating and summing the error terms of all first sound sensors separately, the value of the objective function reflects the overall noise level around the entire drone hangar after active noise cancellation. Specifically, to better reflect the magnitude of the error, the square of the difference between each predicted noise output variable and the expected output noise can be calculated separately and summed to obtain the objective function.

[0034] S114, according to the preset constraints, the objective function is solved with the goal of minimizing it, and according to the solution result, the speaker unit is controlled to generate a canceling sound wave signal.

[0035] It is understood that preset constraints are conditions set based on the actual physical characteristics of the noise, the output capability of the loudspeaker unit, etc., to constrain the optimization of the objective function. Specifically, they may include at least one of the following: the amplitude of the input variable is controlled to not exceed the maximum output capability of the loudspeaker unit; the amplitude of the predicted state variable is within a second limit range; and the amplitude of the predicted noise output variable is within a first limit range.

[0036] The objective function reflects the overall noise level around the drone hangar after active noise cancellation; a smaller value indicates better noise reduction. The objective function is a function with control input variables as unknowns. Solving for this function minimizes the objective, allowing us to determine which cancellation signal to play inside the drone hangar to minimize the error between the canceled noise data and the expected output noise. The solution method depends on the objective function and the type of preset constraints. For example, using the sum of squares of the differences, linear quadratic programming can be employed.

[0037] Based on the noise reduction method in this embodiment, multiple first sound sensors are installed within the drone hangar to more comprehensively collect noise emitted by various devices within the hangar. An autoregressive model is constructed based on the historical noise data collected by each first sound sensor. The drone hangar can be described using a discrete state-space model, which is then constructed using the coefficients obtained from the autoregressive model. The state-space model is then used to predict the predicted noise output variable for the next N periods. Based on the error between the predicted noise output variable and the expected output noise, an objective function is constructed. Finally, the objective function is minimized under preset constraints to obtain the canceled sound wave signal. This canceled sound wave signal can actively eliminate noise in the drone hangar, which is beneficial for the widespread deployment of drone hangars.

[0038] In one embodiment, the placement of the speaker unit is determined by the following steps: (1) Determine one or more noise reduction directions.

[0039] It is understandable that, with the drone hangar as the center, the noise reduction direction refers to the direction in which the noise impact should be eliminated. For example, if some directions of the drone hangar are residential areas, while other directions are where noise is permissible, then the direction where the residential area is located is determined as the noise reduction direction.

[0040] (2) For any noise reduction direction, set two or more measurement points at a preset interval along the noise reduction direction, and set a second sound sensor at each measurement point. One of the measurement points is located on the surface of the UAV hangar.

[0041] It is understandable that the second sound sensor is used to locate the microphone unit. At least two measurement points need to be set in each noise reduction direction. The noise data collected by the second sound sensor installed on the surface of the drone hangar can reflect the waveform of the sound before it propagates in the noise reduction direction after it is generated from the drone hangar. The other measurement points reflect the waveform of the sound after it has traveled a certain distance in the noise reduction direction.

[0042] (3) Keep the UAV hangar in operation and the speaker unit in the off state, compare the data collected at the surface with the data collected at the other measurement points to obtain the first transmission characteristic.

[0043] It can be understood that the collected data corresponding to the surface represents the original noise before it propagates when the drone hangar generates noise, while the other collected data represents the noise after the original noise has propagated a certain distance in the noise direction. By comparing each propagated noise with the original noise, the influence of the noise in the drone hangar on the original noise in the noise reduction direction can be obtained, which is the first transmission characteristic.

[0044] (4) Keep the UAV hangar closed, play the preset audio on the speaker unit, compare the data collected on the surface with the data collected at the other measurement points, and obtain the second transmission characteristic.

[0045] It can be understood that the data collected corresponding to the surface represents the preset audio that has not yet propagated when only the speaker unit is emitting sound, while the other data collected represents the audio after the preset audio has propagated a certain distance in the noise direction. By comparing each propagated audio with the audio, the influence of the sound emitted by the speaker unit on the sound emitted in the noise reduction direction can be obtained, which is the second transmission characteristic.

[0046] (5) Adjust the setting position of the speaker unit according to the difference between the first transmission characteristic and the second transmission characteristic, and after the adjustment is completed, return to keep the UAV hangar in the closed state, make the speaker unit play the preset audio, compare the collected data corresponding to the surface with the collected data corresponding to the other measurement points, and continue to execute the step of obtaining the second transmission characteristic until the difference between the second transmission characteristic and the first transmission characteristic meets the error requirements.

[0047] It is understandable that in order to ensure that the sounds emitted by the drone hangar and the speaker unit can still cancel each other out well after propagating in the air, the two sound sources need to have similar transmission characteristics in the noise reduction direction. Therefore, by continuously adjusting the position of the microphone unit and comparing the new second transmission characteristic after the position adjustment with the first transmission characteristic, a position can be found that allows the microphone unit to have similar transmission characteristics to the drone hangar, and the microphone unit can be kept in that position.

[0048] In one embodiment, the base of the drone hangar is equipped with an adjustable shock-absorbing unit. One or more vibration sensors are installed on the contact surface of the drone hangar base to collect vibration data. It is understood that considering the vibrations generated during the operation of equipment in the drone hangar, which also produce noise, this embodiment includes a shock-absorbing unit with adjustable damping. This unit can be used to eliminate vibrations. The noise reduction method further includes adjusting the damping of the adjustable shock-absorbing unit based on the vibration data. The stronger the vibration, the greater the damping. That is, in this embodiment, the shock-absorbing unit can automatically adjust the damping according to the intensity of the vibration.

[0049] In one embodiment, the output noise is expected to be a signal below the human ear's perception threshold.

[0050] In one embodiment, in order to ensure the normal operation of each component during actual drone hangar operation, an air conditioner needs to be installed inside. The higher the external temperature, the higher the cooling demand, the more frequently the compressor and other components operate, and the more obvious the noise generation. Temperature sensors can also be added inside the drone hangar, and temperature records can be added to historical noise data to facilitate systematic analysis of noise characteristics.

[0051] This application provides a noise reduction device for a drone hangar, which includes a speaker unit and multiple first sound sensors installed inside the drone hangar. The first sound sensors are used to collect noise data. The noise reduction device includes a data acquisition module, a first modeling module, a second modeling module, a recursion module, a noise prediction module, an objective function construction module, and an output module.

[0052] The data acquisition module is used to acquire historical noise data collected by multiple first sound sensors.

[0053] The first modeling module is used to construct a p-order autoregressive model based on historical noisy data. The autoregressive model includes autoregressive coefficients.

[0054] The second modeling module is used to construct a state-space model based on the autoregressive model. The state-space model includes noisy state variables and control input variables, and the autoregressive coefficients are used to generate the constant term of the noisy state variables.

[0055] The recursive module is used to assign values ​​to p noise state variables in the state space model, including the current time and the time adjacent to the current time, to obtain the predicted state variables for the next time step. Based on the predicted state variables for the next time step, values ​​are recursively assigned to the noise state variables in the state space model to obtain the predicted state variables for the next N time steps.

[0056] The noise prediction module is used to obtain the corresponding predicted noise output variable based on each predicted state variable.

[0057] The objective function construction module is used to construct an objective function based on each predicted noise output variable and the expected output noise. The objective function reflects the error between each predicted noise output variable and the expected output noise.

[0058] The output module is used to solve the problem based on preset constraints, with the goal of minimizing the objective function, to obtain the solution for the control input variables, and to generate a canceling acoustic signal based on the solution results.

[0059] This application provides a computer device including one or more processors and a memory storing computer-readable instructions. When executed by one or more processors, the computer-readable instructions perform the steps of the noise reduction method for the drone hangar in any of the above embodiments.

[0060] Indicatively, such as Figure 2 As shown, Figure 2 This is a schematic diagram of the internal structure of a computer device provided in an embodiment of this application. (Refer to...) Figure 2 The computer device 200 includes a processing component 202, which further includes one or more processors, and memory resources represented by memory 201 for storing instructions, such as applications, that can be executed by the processing component 202. The applications stored in memory 201 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 202 is configured to execute instructions to perform the steps of the noise reduction method for the UAV hangar of any of the above embodiments.

[0061] The computer device 200 may also include a power supply component 203 configured to perform power management of the computer device 200, a wired or wireless network interface 204 configured to connect the computer device 200 to a network, and an input / output (I / O) interface 205. The computer device 200 may operate on an operating system stored in memory 201, such as Windows Server™, Mac OS X™, Unix™, Linux™, Free BSD™, or similar.

[0062] Those skilled in the art will understand that Figure 2 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0063] This application provides a storage medium storing computer-readable instructions, which, when executed by one or more processors, cause the one or more processors to perform the steps of the noise reduction method for the drone hangar in any of the above embodiments.

[0064] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0065] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referred to each other.

[0066] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A noise reduction method for a drone hangar, characterized in that, A speaker unit and multiple first sound sensors are installed within the drone hangar. The first sound sensors are used to collect noise data. The noise reduction method includes: Acquire historical noise data collected by the plurality of first sound sensors; Based on the historical noise data, an autoregressive model of order p is constructed; the autoregressive model includes autoregressive coefficients; Based on the autoregressive model, a state-space model is constructed; the state-space model includes noise state variables and control input variables, and the autoregressive coefficients are used to generate constant terms for the noise state variables; The p noise data points, including the current time and the time adjacent to the current time, are assigned to the noise state variables in the state space model to obtain the predicted state variables for the next time. Based on the predicted state variables for the next time, the noise state variables in the state space model are recursively assigned to obtain the predicted state variables for the next N time. Based on each of the predicted state variables, the corresponding predicted noise output variables are obtained; Based on the predicted noise output variables and the expected output noise, an objective function is constructed; the objective function is used to reflect the error between the predicted noise output variables and the expected output noise, wherein when there are m first sound sensors, there will be m×N error terms to be summed; Based on preset constraints, the objective function is minimized to obtain the solution for the control input variable. Based on the solution result, the speaker unit is controlled to generate a canceling sound wave signal.

2. The noise reduction method according to claim 1, characterized in that, The preset constraints include at least one of the following: The amplitude of the control input variable shall not exceed the maximum output capability of the speaker unit; The magnitude of the predicted state variable is within the second limiting range; The amplitude of the predicted noise output variable is within the first limiting range.

3. The noise reduction method according to claim 1, characterized in that, The step of constructing an objective function based on each of the predicted noise output variables and the expected output noise includes: The objective function is obtained by calculating the square of the difference between each predicted noise output variable and the expected output noise, and summing the squares.

4. The noise reduction method according to claim 3, characterized in that, The objective function can be solved using linear quadratic programming.

5. The noise reduction method according to claim 1, characterized in that, The location of the speaker unit is determined by the following steps: Determine more than one noise reduction direction; For any one of the noise reduction directions, two or more measuring points are set at a preset interval along the noise reduction direction, and a second sound sensor is set at each of the measuring points; one of the measuring points is located on the surface of the drone hangar; With the drone hangar in operation and the speaker unit in off state, the collected data corresponding to the surface is compared with the collected data corresponding to the other measurement points to obtain the first transmission characteristic. Keeping the drone hangar closed, playing a preset audio through the speaker unit, and comparing the collected data corresponding to the surface with the collected data corresponding to the other measurement points, a second transmission characteristic is obtained; Based on the difference between the first transmission characteristic and the second transmission characteristic, the setting position of the speaker unit is adjusted, and after the adjustment is completed, the process of keeping the drone hangar closed, making the speaker unit play a preset audio, comparing the collected data corresponding to the surface with the collected data corresponding to the other measurement points, and obtaining the second transmission characteristic continues until the difference between the second transmission characteristic and the first transmission characteristic meets the error requirements.

6. The noise reduction method according to claim 1, characterized in that, The base of the drone hangar is equipped with an adjustable vibration damping unit. One or more vibration sensors are installed on the contact surface of the drone hangar base. These vibration sensors are used to collect vibration data. The noise reduction method further includes: Based on the vibration data, the damping of the adjustable shock absorption unit is adjusted; wherein, the stronger the vibration data, the greater the damping.

7. The noise reduction method according to claim 6, characterized in that, The expected output noise is a signal below the human ear's perception threshold.

8. A noise reduction device for a drone hangar, characterized in that, The drone hangar is equipped with a speaker unit and multiple first sound sensors, which are used to collect noise data. The noise reduction device includes: The data acquisition module is used to acquire historical noise data collected by the plurality of first sound sensors; The first modeling module is used to construct a p-order autoregressive model based on the historical noise data; the autoregressive model includes autoregressive coefficients. The second modeling module is used to construct a state-space model based on the autoregressive model; the state-space model includes noise state variables and control input variables, and the autoregressive coefficients are used to generate constant terms for the noise state variables; The recursive module is used to assign values ​​to the noise state variables in the state space model, including the current time and the time adjacent to the current time, to obtain the predicted state variables for the next time step, and based on the predicted state variables for the next time step, recursively assign values ​​to the noise state variables in the state space model to obtain the predicted state variables for the next N time steps. The noise prediction module is used to obtain the corresponding predicted noise output variable based on each of the predicted state variables; The objective function construction module is used to construct an objective function based on each of the predicted noise output variables and the expected output noise; the objective function is used to reflect the error between each of the predicted noise output variables and the expected output noise, wherein when there are m first sound sensors, there will be m×N error terms to be summed; The solution module is used to solve the objective function according to preset constraints, obtain the solution of the control input variable, and control the speaker unit to generate a canceling sound wave signal according to the solution result.

9. A computer device, characterized in that, It includes one or more processors and a memory storing computer-readable instructions, which, when executed by the one or more processors, perform the steps of the noise reduction method for a drone hangar as described in any one of claims 1-7.

10. A storage medium, characterized in that, The storage medium stores computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the noise reduction method for the drone hangar as described in any one of claims 1-7.

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