A method, device, medium and product for measuring helicopter noise in the air

Abstract

The invention discloses a method, device, medium and product for measuring noise of an aerial helicopter, and relates to the technical field of noise measurement. The method comprises: obtaining original noise observation signals of multiple sampling points collected by each unmanned aerial vehicle; converting the original noise observation signals of each sampling point into non-negative values ​​to obtain non-negative noise observation signals of the corresponding sampling point; using a non-negative matrix decomposition algorithm, based on the non-negative noise observation signals of each sampling point, determining the target spatiotemporal correlation matrix estimation of the corresponding sampling point; based on the non-negative noise observation signals of each sampling point and the target spatiotemporal correlation matrix estimation, determining the short-time Fourier transform coefficient vector of the noise of the aerial helicopter to be measured at the corresponding sampling point; based on the short-time Fourier transform coefficient vector of the noise of the aerial helicopter to be measured at each sampling point, determining the intensity of the noise of the aerial helicopter to be measured at the corresponding sampling point. The invention realizes the measurement of the noise of the aerial helicopter.

Description

Technical Field

[0001] The present invention relates to the technical field of noise measurement, and in particular to a method, device, medium and product for measuring noise of an aerial helicopter. Background Art

[0002] Helicopters generally fly at low and ultra-low altitudes, and are suitable for performing various medium and low-speed military or civilian tasks. However, due to the various periodic aerodynamic vibrations of various components such as helicopter rotors, aerodynamic noise is inevitable. Reducing the noise level of helicopters can greatly improve the various performance of helicopters. Therefore, in the new design stage, the study of aerodynamic noise characteristics has become a design factor that cannot be ignored in the design. However, in general, the external noise radiation of helicopters has a large impact range, the noise characteristics are highly directional, the ground and air sound fields are unevenly distributed, and are highly correlated with the flight state, atmospheric conditions, terrain and other sound speeds. The measurement of traditional helicopter aerodynamic noise is mainly divided into two methods: one is to install random microphones on the target helicopter, and the other is to install a ground microphone array on the target helicopter route. The airborne microphone can quickly obtain the near-field noise characteristics of the helicopter, and the environmental interference is relatively small. However, the fixed installation will be affected by mechanical vibration, the requirements for the sensor are high, and the far-field noise cannot be collected, and the perception ability of the rotor thickness noise is weak. The ground microphone array can directly obtain the ground noise radiation level and the overall noise level of the helicopter, but various noise signals will produce superimposed interference in the time and frequency domain. Overall, there is a lack of measurements of airborne noise, and a new method for measuring airborne helicopter noise is needed. Summary of the invention

[0003] The purpose of the present invention is to provide a method, device, medium and product for measuring the noise of an aerial helicopter, so as to realize the measurement of the noise of an aerial helicopter.

[0004] To achieve the above object, the present invention provides the following solutions:

[0005] A method for measuring helicopter noise in the air, comprising:

[0006] Obtaining original noise observation signals of multiple sampling points collected by each drone; each drone is at the same height as the helicopter in the air to be tested, and there is at least one drone; one sampling point corresponds to one time and one frequency; the original noise observation signal includes: the noise of the drone and the noise of the helicopter in the air to be tested;

[0007] The original noise observation signal of each sampling point is converted into a non-negative value to obtain a non-negative noise observation signal of the corresponding sampling point;

[0008] Using the non-negative matrix decomposition algorithm, based on the non-negative noise observation signal of each sampling point, the target spatiotemporal correlation matrix estimation of the corresponding sampling point is determined;

[0009] Based on the non-negative noise observation signal of each sampling point and the target space-time correlation matrix estimation, the short-time Fourier transform coefficient vector of the noise of the helicopter to be tested in the air corresponding to the sampling point is determined;

[0010] Based on the short-time Fourier transform coefficient vector of the noise of the helicopter to be tested at each sampling point, the intensity of the noise of the helicopter to be tested at the corresponding sampling point is determined.

[0011] Optionally, a non-negative matrix decomposition algorithm is used to determine the target spatiotemporal correlation matrix estimate of the corresponding sampling point based on the non-negative noise observation signal of each sampling point, including:

[0012] Determine any sampling point as the current sampling point;

[0013] Determine the actual spatiotemporal correlation matrix of the current sampling point according to the non-negative noise observation signal of the current sampling point;

[0014] Initialize the spatiotemporal correlation matrix estimation of the current sampling point; the spatiotemporal correlation matrix estimation is determined based on the spatial characteristic matrix and the weights, amplitudes and gains of the NMF basis;

[0015] Using the gradient descent method, based on the actual spatiotemporal association matrix of the current sampling point, the spatiotemporal association matrix estimate of the current sampling point is iteratively updated multiple times to obtain the target spatiotemporal association matrix estimate of the current sampling point.

[0016] Optionally, the update process at any current iteration number includes:

[0017] Based on the actual spatiotemporal correlation matrix of the current sampling point and the spatiotemporal correlation matrix estimate at the current number of iterations, the cost at the current number of iterations is calculated;

[0018] Determine the gradient at the current iteration based on the cost at the previous iteration and the cost at the current iteration;

[0019] Determine whether the gradient at the current iteration number is less than the threshold;

[0020] If yes, the spatiotemporal correlation matrix estimate at the current iteration number is determined as the target spatiotemporal correlation matrix estimate;

[0021] If not, then based on the actual space-time association matrix and the estimated space-time association matrix at the current number of iterations, determine the estimated space-time association matrix at the next number of iterations, update the estimated space-time association matrix at the current number of iterations to the estimated space-time association matrix at the next number of iterations, update the cost at the current number of iterations to the cost at the current number of iterations, and return "based on the actual space-time association matrix of the current sampling point and the estimated space-time association matrix at the current number of iterations, calculate the cost at the current number of iterations."

[0022] Optionally, determining the estimated space-time correlation matrix at the next iteration number based on the actual space-time correlation matrix and the estimated space-time correlation matrix at the current iteration number includes:

[0023] Determining a weight of the NMF basis at a next iteration number based on the actual spatiotemporal correlation matrix, the spatiotemporal correlation matrix estimate at the current iteration number, the amplitude of the NMF basis at the current iteration number, and the gain of the NMF basis at the current iteration number;

[0024] Determining the amplitude of the NMF basis at the next iteration number based on the actual spatiotemporal correlation matrix, the spatiotemporal correlation matrix estimate at the current iteration number, the weight of the NMF basis at the current iteration number, and the gain of the NMF basis at the current iteration number;

[0025] Determining a gain of the NMF basis at the next iteration number based on the actual spatiotemporal correlation matrix, the spatiotemporal correlation matrix estimate at the current iteration number, the weight of the NMF basis at the current iteration number, and the amplitude of the NMF basis at the current iteration number;

[0026] Determine a spatial characteristic matrix at a next iteration number based on the actual spatiotemporal correlation matrix, an estimate of the spatiotemporal correlation matrix at a current iteration number, a weight of an NMF basis at a next iteration number, an amplitude of the NMF basis at the next iteration number, and a gain of the NMF basis at the next iteration number;

[0027] An estimate of a spatiotemporal correlation matrix at a next iteration number is determined based on a weight of an NMF basis at a next iteration number, an amplitude of an NMF basis at a next iteration number, a gain of an NMF basis at a next iteration number, and a spatial characteristic matrix at a next iteration number.

[0028] Optionally, eight microphone sensors are arranged on the drone, and each of the microphone sensors collects the original noise of one channel respectively, thereby constituting the original noise observation signal.

[0029] Optionally, determining the intensity of the noise of the helicopter to be tested in the air corresponding to the sampling point based on the short-time Fourier transform coefficient vector of the noise of the helicopter to be tested in the air at each sampling point includes:

[0030] The short-time Fourier transform coefficient vector of the noise of the helicopter to be tested in the air at each sampling point is subjected to short-time Fourier inverse transform to obtain the intensity of the noise of the helicopter to be tested in the air at the corresponding sampling point.

[0031] A computer device comprises: a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any one of the above-mentioned methods for measuring helicopter noise in the air.

[0032] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements any of the above-mentioned methods for measuring helicopter noise in the air.

[0033] A computer program product comprises a computer program, wherein when the computer program is executed by a processor, the method for measuring helicopter noise in the air as described in any one of the above is implemented.

[0034] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0035] The invention discloses an aerial helicopter noise measurement method, device, medium and product. Firstly, original noise observation signals of multiple sampling points collected by each unmanned aerial vehicle are obtained; each unmanned aerial vehicle is at the same height as the aerial helicopter to be measured, and there is at least one unmanned aerial vehicle; one sampling point corresponds to one time and one frequency; the original noise observation signal includes: the noise of the unmanned aerial vehicle and the noise of the aerial helicopter to be measured; the original noise observation signal of each sampling point is converted into a non-negative value to obtain a non-negative noise observation signal of the corresponding sampling point; secondly, a non-negative matrix decomposition algorithm is used to determine the target spatiotemporal correlation matrix estimation of the corresponding sampling point based on the non-negative noise observation signal of each sampling point; thirdly, based on the non-negative noise observation signal of each sampling point and the target spatiotemporal correlation matrix estimation, a short-time Fourier transform coefficient vector of the noise of the aerial helicopter to be measured at the corresponding sampling point is determined; finally, based on the short-time Fourier transform coefficient vector of the noise of the aerial helicopter to be measured at each sampling point, the intensity of the noise of the aerial helicopter to be measured at the corresponding sampling point is determined. Compared with the general noise signal separation algorithm, the present invention adopts the negative matrix decomposition algorithm, which has lower cost and better directivity. It can effectively separate the noise of the UAV and the noise of the aerial helicopter to be measured, thereby realizing the measurement of the noise of the aerial helicopter. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0037] Figure 1 A schematic diagram of a method for measuring helicopter noise in the air provided in Example 1 of the present invention;

[0038] Figure 2 This is a schematic diagram of the array architecture for measuring helicopter noise in the air;

[0039] Figure 3 This is a schematic diagram of the UAV structure.

[0040] Explanation of symbols:

[0041] Fuselage—1, low-noise blades—2, coaxial variable pitch control mechanism—3, acquisition board—4, microphone measurement array—5, landing gear—6, Beidou antenna—7, fixed center axis—8, automatic tilt device—9. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] The purpose of the present invention is to provide a method, device, medium and product for measuring the noise of an aerial helicopter, so as to realize the measurement of the noise of an aerial helicopter.

[0044] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0045] Example 1

[0046] like Figure 1 As shown, the method for measuring helicopter noise in the air in this embodiment includes:

[0047] Step 1: Obtain the original noise observation signals of multiple sampling points collected by each UAV.

[0048] Among them, each UAV is at the same altitude as the aerial helicopter to be tested, and there is at least one UAV; one sampling point corresponds to a time and a frequency; the original noise observation signal includes: the noise of the UAV and the noise of the aerial helicopter to be tested.

[0049] As an optional implementation, eight microphone sensors are arranged on the drone, and each microphone sensor collects the original noise of one channel respectively, thereby forming an original noise observation signal.

[0050] Specifically, when four drones are set up for collection, the four drones can form a rectangle in the air with a front width of 50m and a depth of 25m as follows: Figure 2 The aerial measurement array shown, the flight path of the aerial helicopter to be tested passes through the center point. Four drones are tested at different locations. The flight path is set according to the test requirements and can be a straight line, vertical lift, or arc.

[0051] like Figure 3As shown, the UAV includes: a fuselage 1, low-noise blades 2, a coaxial variable pitch control mechanism 3, a collection board 4, a microphone measurement array 5, a landing gear 6, a Beidou antenna 7, a fixed central axis 8 and an automatic tilt device 9.

[0052] The fuselage 1 is composed of carbon fiber composite materials and aluminum profiles, and adopts a "stem"-shaped coaxial counter-rotating rotor configuration. It is connected to the low-noise blades 2 through a motor shaft. The fuselage 1 is connected to the coaxial pitch control mechanism 3 through a fixed central axis 8. The coaxial pitch control mechanism 3 is connected to the low-noise blades 2 through two pairs of automatic tilt devices 9 to achieve cyclic pitch control of the blades.

[0053] There is a lithium battery inside the body 1 to provide power for the system.

[0054] A microphone measurement array 5 and an acquisition board 4 are mounted under the fuselage 1 , and the acquisition board 4 is directly connected to the microphone measurement array 5 through an internal data line.

[0055] The microphone measurement array 5 consists of 8 microphone sensors and is installed under the fuselage 1 through a set of carbon fiber tubes.

[0056] The Beidou antenna 7 is installed on the installation platform on the top of the fuselage 1 to achieve synchronous standard timing among multiple platforms.

[0057] Step 2: Convert the original noise observation signal of each sampling point into a non-negative value to obtain the non-negative noise observation signal of the corresponding sampling point.

[0058] Specifically, take the square value of the original noise observation signal to obtain a non-negative noise observation signal. The calculation formula is:

[0059]

[0060] in, is the non-negative noise observation signal of the ith frequency at the lth moment, and the ith frequency at the lth moment corresponds to a sampling point; is the original noise observation signal of the ith frequency at the lth moment, is the original noise of the first channel, is the original noise of the Mth channel, M is the number of microphone sensors, i.e. the number of channels of the original noise, M=8; For x il The transpose of .

[0061] Step 3: Using the non-negative matrix decomposition algorithm, based on the non-negative noise observation signal of each sampling point, determine the target spatiotemporal correlation matrix estimate of the corresponding sampling point.

[0062] As an optional implementation, step 3 includes:

[0063] Step 31: Determine any sampling point as the current sampling point.

[0064] Step 32: Determine the actual spatiotemporal correlation matrix of the current sampling point based on the non-negative noise observation signal of the current sampling point.

[0065] Specifically, the expression of the actual space-time correlation matrix is:

[0066]

[0067] Among them, X il is the actual spatiotemporal correlation matrix of the ith frequency at the lth moment; for The transpose of .

[0068] In fact, the diagonal elements of the spatiotemporal correlation matrix consist of the amplitudes of the non-negative noise observation signals, and the off-diagonal elements represent the phase difference (imaginary part) and amplitude correlation between the two microphone sensors.

[0069] Step 33: Initialize the spatiotemporal correlation matrix estimation of the current sampling point; the spatiotemporal correlation matrix estimation is determined based on the spatial characteristic matrix and the weights, amplitudes and gains of the NMF basis.

[0070] Specifically, the expression of spatiotemporal correlation matrix estimation is:

[0071]

[0072] in, is the spatiotemporal correlation matrix estimation of the ith frequency at the lth moment; K is the number of non-negative matrix factorization (NMF) bases; P is the number of signal sources, including drones and helicopters to be tested, P = 2; H ip is the spatial characteristic matrix of the pth signal source at the ith frequency; z pk is the weight of the kth NMF basis belonging to the pth signal source; t ik is the amplitude of the kth NMF basis at the ith frequency; v kl is the gain of the kth NMF basis at the lth moment.

[0073] Step 34: Using the gradient descent method, based on the actual spatiotemporal association matrix of the current sampling point, the spatiotemporal association matrix estimate of the current sampling point is iteratively updated multiple times to obtain the target spatiotemporal association matrix estimate of the current sampling point.

[0074] As an optional implementation, the update process at any current iteration number includes:

[0075] Step 341: Calculate the cost at the current number of iterations based on the actual spatiotemporal correlation matrix of the current sampling point and the spatiotemporal correlation matrix estimate at the current number of iterations.

[0076] Specifically, to evaluate With X il The approximation degree between them is determined by defining the cost function according to different measurement criteria. The expression of the cost function is:

[0077]

[0078] Where f(T,V,H,Z) is the cost, T is the amplitude matrix of the NMF basis, V is the gain matrix of the NMF basis, H is the matrix composed of each spatial characteristic matrix, and Z is the weight matrix of the NMF basis; tr(·) is the trace of the matrix; for The inverse of ; det is the determinant of the matrix.

[0079] Step 342: Determine the gradient at the current iteration number based on the cost at the previous iteration number and the cost at the current iteration number.

[0080] Step 343: Determine whether the gradient at the current iteration number is less than a threshold.

[0081] Specifically, a gradient less than a threshold value indicates that the NMF bases can be well clustered.

[0082] Step 344: If yes, the spatiotemporal correlation matrix estimate at the current iteration number is determined as the target spatiotemporal correlation matrix estimate.

[0083] Step 345: If not, then based on the actual spatiotemporal correlation matrix and the spatiotemporal correlation matrix estimate at the current iteration number, determine the spatiotemporal correlation matrix estimate at the next iteration number, update the spatiotemporal correlation matrix estimate at the current iteration number to the spatiotemporal correlation matrix estimate at the next iteration number, update the cost at the current iteration number to the cost at the current iteration number, and return to step 341.

[0084] As an optional implementation, determining the spatiotemporal correlation matrix estimate at the next iteration number based on the actual spatiotemporal correlation matrix and the spatiotemporal correlation matrix estimate at the current iteration number includes:

[0085] Step 3451: Determine the weight of the NMF basis at the next iteration number based on the actual spatiotemporal correlation matrix, the spatiotemporal correlation matrix estimate at the current iteration number, the amplitude of the NMF basis at the current iteration number, and the gain of the NMF basis at the current iteration number.

[0086] Specifically, the update formula of the NMF basis weight is:

[0087]

[0088] Among them, z pk (j+1) is the weight of the k-th NMF basis belonging to the p-th signal source in the j+1-th iteration; z pk (j) is the weight of the k-th NMF basis belonging to the p-th signal source in the j-th iteration; t ik (j) is the amplitude of the kth NMF basis at the i-th frequency in the j-th iteration; v kl (j) is the gain of the kth NMF basis at the lth moment in the jth iteration; is the jth iteration The inverse of H ip (j) is the spatial characteristic matrix of the pth signal source at the ith frequency in the jth iteration.

[0089] Step 3452: Determine the amplitude of the NMF basis at the next iteration number based on the actual spatiotemporal correlation matrix, the spatiotemporal correlation matrix estimate at the current iteration number, the weight of the NMF basis at the current iteration number, and the gain of the NMF basis at the current iteration number.

[0090] Specifically, the update formula of the amplitude of the NMF basis is:

[0091]

[0092] Among them, t ik (j+1) is the amplitude of the kth NMF basis of the ith frequency at the j+1th iteration.

[0093] Step 3453: Determine the gain of the NMF basis at the next iteration number based on the actual spatiotemporal correlation matrix, the spatiotemporal correlation matrix estimate at the current iteration number, the weight of the NMF basis at the current iteration number, and the amplitude of the NMF basis at the current iteration number.

[0094] Specifically, the update formula of the NMF-based gain is:

[0095]

[0096] Among them, v kl (j+1) is the gain of the kth NMF basis at the lth moment in the j+1th iteration.

[0097] Step 3454: Determine the spatial characteristic matrix at the next iteration number based on the actual spatiotemporal correlation matrix, the spatiotemporal correlation matrix estimate at the current iteration number, the weight of the NMF basis at the next iteration number, the amplitude of the NMF basis at the next iteration number, and the gain of the NMF basis at the next iteration number.

[0098] Specifically, the update formula of the spatial characteristic matrix is:

[0099] H ip (j+1)AH ip (j+1)=B.

[0100]

[0101] Among them, H ip (j+1) is the spatial characteristic matrix of the pth signal source at the ith frequency in the j+1th iteration; A and B are intermediate matrices.

[0102] Step 3455: Determine the spatiotemporal correlation matrix estimate for the next iteration number based on the weight of the NMF basis for the next iteration number, the amplitude of the NMF basis for the next iteration number, the gain of the NMF basis for the next iteration number, and the spatial characteristic matrix for the next iteration number.

[0103] Step 4: Based on the non-negative noise observation signal of each sampling point and the target space-time correlation matrix estimation, determine the short-time Fourier transform coefficient vector of the noise of the helicopter under test at the corresponding sampling point.

[0104] Specifically, the calculation formula of the short-time Fourier transform coefficient vector is:

[0105]

[0106] in, is the short-time Fourier transform (STFT) coefficient vector of the ith frequency at the lth moment of the pth signal source.

[0107] Step 5: Based on the short-time Fourier transform coefficient vector of the noise of the helicopter in the air to be tested at each sampling point, determine the intensity of the noise of the helicopter in the air to be tested at the corresponding sampling point.

[0108] As an optional implementation, step 5 includes:

[0109] The short-time Fourier transform coefficient vector of the noise of the helicopter to be tested in the air at each sampling point is subjected to short-time Fourier inverse transform to obtain the intensity of the noise of the helicopter to be tested in the air at the corresponding sampling point.

[0110] Furthermore, when the aerial helicopter to be tested flies through the measurement array formed by the drones, the distribution characteristics of the spatial aerodynamic noise of the aerial helicopter to be tested can be analyzed through the noise intensity of the aerial helicopter to be tested at all sampling points corresponding to the drones at different positions.

[0111] Technical effects of the present invention:

[0112] (1) The present invention provides a coaxial drone solution for measuring helicopter noise in the air. The coaxial inversion layout can achieve a lower blade tip speed than the quad-rotor layout, and the overall slenderness can make the microphone sensor as far away from the noise source as possible, thereby reducing the body noise. The use of low-noise blades further reduces the interference of the drone body noise on signal acquisition.

[0113] (2) The present invention adopts a sound source separation algorithm based on negative matrix decomposition and an eight-microphone array design based on algorithm requirements. Compared with general noise signal separation algorithms, the present invention has lower cost and better directivity, and can effectively separate the noise of the UAV body and the noise of the aerial helicopter to be tested. It also adopts an aerial array measurement test method to effectively solve the noise collection and measurement problems in the design process of new helicopters.

[0114] (3) The present invention adopts the method of carrying the acquisition module on the drone, so that the signal acquisition point can be higher from the ground, which ensures the flight safety during the test and can also expand the spatial scope of data acquisition.

[0115] Example 2

[0116] A computer device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for measuring helicopter noise in the air in the first embodiment.

[0117] Example 3

[0118] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the method for measuring helicopter noise in the air in embodiment 1.

[0119] Example 4

[0120] A computer program product includes a computer program, which implements the method for measuring helicopter noise in the air in embodiment 1 when executed by a processor.

[0121] Example 5

[0122] A computer device, which may be a database. The computer device includes a processor, a memory, an input / output interface (I / O for short) and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store pending transactions. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the method for measuring helicopter noise in the air in Example 1 is implemented.

[0123] It should be noted that the object information (including but not limited to object device information, object personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the present invention are all information and data authorized by the object or fully authorized by all parties, and the collection, use and processing of relevant data need to comply with relevant laws, regulations and standards of relevant countries and regions.

[0124] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided by the present invention may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited thereto. The processor involved in each embodiment provided by the present invention may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited thereto.

[0125] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0126] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for measuring helicopter noise in the air, characterized in that: The method comprises: Obtaining original noise observation signals of multiple sampling points collected by each drone; each drone is at the same height as the helicopter in the air to be tested, and there is at least one drone; one sampling point corresponds to one time and one frequency; the original noise observation signal includes: the noise of the drone and the noise of the helicopter in the air to be tested; The original noise observation signal of each sampling point is converted into a non-negative value to obtain a non-negative noise observation signal of the corresponding sampling point; Using the non-negative matrix decomposition algorithm, based on the non-negative noise observation signal of each sampling point, the target spatiotemporal correlation matrix estimation of the corresponding sampling point is determined; Based on the non-negative noise observation signal of each sampling point and the target space-time correlation matrix estimation, the short-time Fourier transform coefficient vector of the noise of the helicopter to be tested in the air corresponding to the sampling point is determined; Determine the intensity of the noise of the helicopter under test at the corresponding sampling point based on the short-time Fourier transform coefficient vector of the noise of the helicopter under test at each sampling point; Using the non-negative matrix decomposition algorithm, based on the non-negative noise observation signal of each sampling point, the target spatiotemporal correlation matrix estimation of the corresponding sampling point is determined, including: Determine any sampling point as the current sampling point; Determine the actual spatiotemporal correlation matrix of the current sampling point according to the non-negative noise observation signal of the current sampling point; Initialize the spatiotemporal correlation matrix estimation of the current sampling point; the spatiotemporal correlation matrix estimation is determined based on the spatial characteristic matrix and the weights, amplitudes and gains of the NMF basis; Using the gradient descent method, based on the actual spatiotemporal association matrix of the current sampling point, the spatiotemporal association matrix estimate of the current sampling point is iteratively updated multiple times to obtain the target spatiotemporal association matrix estimate of the current sampling point; The update process at any current iteration number includes: Based on the actual spatiotemporal correlation matrix of the current sampling point and the spatiotemporal correlation matrix estimate at the current number of iterations, the cost at the current number of iterations is calculated; Determine the gradient at the current iteration based on the cost at the previous iteration and the cost at the current iteration; Determine whether the gradient at the current iteration number is less than the threshold; If yes, the spatiotemporal correlation matrix estimate at the current iteration number is determined as the target spatiotemporal correlation matrix estimate; If not, then based on the actual spatiotemporal association matrix and the estimated spatiotemporal association matrix at the current number of iterations, determine the estimated spatiotemporal association matrix at the next number of iterations, update the estimated spatiotemporal association matrix at the current number of iterations to the estimated spatiotemporal association matrix at the next number of iterations, update the cost at the previous number of iterations to the cost at the current number of iterations, and return "calculate the cost at the current number of iterations based on the actual spatiotemporal association matrix of the current sampling point and the estimated spatiotemporal association matrix at the current number of iterations"; Determining the spatiotemporal association matrix estimate at the next iteration number based on the actual spatiotemporal association matrix and the spatiotemporal association matrix estimate at the current iteration number includes: Determining a weight of the NMF basis at a next iteration number based on the actual spatiotemporal correlation matrix, the spatiotemporal correlation matrix estimate at the current iteration number, the amplitude of the NMF basis at the current iteration number, and the gain of the NMF basis at the current iteration number; Determining the amplitude of the NMF basis at the next iteration number based on the actual spatiotemporal correlation matrix, the spatiotemporal correlation matrix estimate at the current iteration number, the weight of the NMF basis at the current iteration number, and the gain of the NMF basis at the current iteration number; Determining a gain of the NMF basis at the next iteration number based on the actual spatiotemporal correlation matrix, the spatiotemporal correlation matrix estimate at the current iteration number, the weight of the NMF basis at the current iteration number, and the amplitude of the NMF basis at the current iteration number; Determine a spatial characteristic matrix at a next iteration number based on the actual spatiotemporal correlation matrix, an estimate of the spatiotemporal correlation matrix at a current iteration number, a weight of an NMF basis at a next iteration number, an amplitude of the NMF basis at the next iteration number, and a gain of the NMF basis at the next iteration number; An estimate of a spatiotemporal correlation matrix at a next iteration number is determined based on a weight of an NMF basis at a next iteration number, an amplitude of an NMF basis at a next iteration number, a gain of an NMF basis at a next iteration number, and a spatial characteristic matrix at a next iteration number.

2. The method for measuring helicopter noise in the air according to claim 1, characterized in that: The drone is provided with 8 microphone sensors, and each of the microphone sensors collects the original noise of one channel respectively, thereby forming the original noise observation signal.

3. The method for measuring helicopter noise in the air according to claim 1, characterized in that: Based on the short-time Fourier transform coefficient vector of the noise of the helicopter under test at each sampling point, the intensity of the noise of the helicopter under test at the corresponding sampling point is determined, including: The short-time Fourier transform coefficient vector of the noise of the helicopter to be tested in the air at each sampling point is subjected to short-time Fourier inverse transform to obtain the intensity of the noise of the helicopter to be tested in the air at the corresponding sampling point.

4. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for measuring helicopter noise in the air as claimed in any one of claims 1 to 3.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for measuring helicopter noise in the air as described in any one of claims 1 to 3 is implemented.

6. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method for measuring helicopter noise in the air as described in any one of claims 1 to 3 is implemented.

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