CN116884382A - Noise reduction method and device for unmanned aerial vehicle hangar, computer equipment and storage medium - Google Patents
Noise reduction method and device for unmanned aerial vehicle hangar, computer equipment and storage medium Download PDFInfo
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- G10K11/1781—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound by electro-acoustically regenerating the original acoustic waves in anti-phase characterised by the analysis of input or output signals, e.g. frequency range, modes, transfer functions
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Abstract
The application provides a noise reduction method and device for an unmanned aerial vehicle library, computer equipment and a storage medium. Through set up a plurality of first sound sensor in unmanned aerial vehicle hangar, the noise that each equipment sent in the more comprehensive collection unmanned aerial vehicle hangar to based on the historical noise data that each first sound sensor gathered, establish autoregressive model. The unmanned aerial vehicle library can utilize discrete state space description and utilize coefficients obtained from an autoregressive model to construct the state space model. And predicting a predicted noise output variable of N periods in the future by using the state space model, and constructing an objective function based on an error between the predicted noise output variable and the expected output noise. And finally, calculating the minimum value of the objective function under the preset constraint condition to obtain the offset acoustic wave signal. Noise in the unmanned aerial vehicle hangar can be actively eliminated through the offset acoustic signals, and popularization and deployment of the unmanned aerial vehicle hangar are facilitated.
Description
Technical Field
The present application relates to the field of noise reduction technologies, and in particular, to a noise reduction method and apparatus for an unmanned aerial vehicle library, a computer device, and a storage medium.
Background
Along with development of unmanned aerial vehicle technology, along with popularization and deployment of unmanned aerial vehicle hangars, unmanned aerial vehicle application gradually steps into 'unmanned'. Because the load center is the center of resident aggregation and is also the key point of inspection, in order to quickly find fault points and effectively inspect more lines, the hangar is often deployed on the roofs of resident roofs or power distribution rooms and the like, but the noise of the hangar is more obvious at night, the hangar is already raised to the effects of complaints, reports and the like, the deployment of the fixed hangar is restricted, the simple coverage of sound insulation materials cannot effectively reduce the noise of the fixed hangar, and the noise of the unmanned aerial vehicle fixed hangar needs to be frequently opened and closed during normal operation and cannot be statically eliminated like equipment such as a transformer. Thus, there is a need for a dynamic approach that can reduce noise.
Disclosure of Invention
The present application aims to solve at least one of the above technical drawbacks, especially the technical drawback of the prior art that the noise of the unmanned aerial vehicle is too large.
In a first aspect, the present application provides a noise reduction method for an unmanned aerial vehicle hangar, in which a speaker unit and a plurality of first sound sensors are disposed, the first sound sensors being used for collecting noise data, the noise reduction method comprising:
acquiring historical noise data acquired by a plurality of first sound sensors;
constructing an autoregressive model of the p-order according to the historical noise data; the autoregressive model includes autoregressive coefficients;
constructing a state space model according to the autoregressive model; the state space model comprises a noise state variable and a control input variable, and the autoregressive coefficient is used for generating constant items of the noise state variable;
the method comprises the steps of assigning p noise data comprising the current moment and the moment adjacent to the current moment to noise state variables in a state space model to obtain a predicted state variable of the next moment, assigning the noise state variables in the state space model in a recursion mode based on the predicted state variable of the next moment to obtain predicted state variables of N moments in the future;
obtaining corresponding prediction noise output variables according to each prediction state variable;
constructing an objective function according to each predicted noise output variable and the expected output noise; the objective function is used for reflecting errors between output variables of each prediction noise and expected output noise;
according to the preset constraint condition, solving the object of minimizing the objective function to obtain a solution of the control input variable, and according to the solving result, controlling the loudspeaker unit to generate a cancellation sound wave signal.
In one embodiment, the preset constraints include at least one of:
controlling the magnitude of the input variable not to exceed the maximum output capability of the speaker unit;
predicting that the magnitude of the state variable is within a second limit range;
the magnitude of the predicted noise output variable is within a first limit range.
In one embodiment, constructing an objective function from each of the predicted noise output variables and the expected output noise comprises:
and respectively calculating the squares of the differences between the output variables of the prediction noises and the expected output noises, and summing to obtain an objective function.
In one embodiment, the means for solving the objective function comprises a linear quadratic programming method.
In one embodiment, the set position of the speaker unit is determined by:
determining more than one noise reduction direction;
for any noise reduction direction, setting more than two measuring points in the noise reduction direction according to a preset interval, and setting a second sound sensor at each measuring point; one measuring point is positioned on the surface of the unmanned aerial vehicle library;
maintaining the unmanned aerial vehicle library in an operating state and the loudspeaker unit in a closed state, and comparing acquired data corresponding to the surface with acquired data corresponding to other measuring points to obtain a first transfer characteristic;
keeping the unmanned aerial vehicle library in a closed state, enabling the loudspeaker unit to play preset audio, and comparing acquired data corresponding to the surface with acquired data corresponding to other measuring points to obtain a second transfer characteristic;
according to the difference between the first transfer characteristic and the second transfer characteristic, the setting position of the speaker unit is adjusted, after the adjustment is completed, the unmanned aerial vehicle is returned to be kept in a closed state, the speaker unit plays preset audio, collected data corresponding to the surface is compared with collected data corresponding to other measuring points, and the step of obtaining the second transfer characteristic is continuously executed until the difference between the second transfer characteristic and the first transfer characteristic meets the error requirement.
In one embodiment, the base of the unmanned aerial vehicle hangar is provided with adjustable shock absorbing unit, at the contact surface of the base of unmanned aerial vehicle hangar, is provided with more than one vibration sensor, and vibration sensor is used for gathering vibration data, and the noise reduction method still includes:
adjusting the shock absorption damping of the adjustable shock absorption unit according to the vibration data; wherein, the stronger the vibration data, the greater the shock damping.
In one embodiment, the expected output noise is a signal below a human ear perception threshold.
In a second aspect, the present application provides a noise reduction device for an unmanned aerial vehicle hangar, in which a speaker unit and a plurality of first sound sensors are disposed, the first sound sensors being used for collecting noise data, the noise reduction device comprising:
the data acquisition module is used for acquiring historical noise data acquired by the plurality of first sound sensors;
the first modeling module is used for constructing a p-order autoregressive model according to the historical noise data; the autoregressive model includes autoregressive coefficients;
the second modeling module is used for constructing a state space model according to the autoregressive model; the state space model comprises a noise state variable and a control input variable, and the autoregressive coefficient is used for generating constant items of the noise state variable;
the recursion module is used for assigning the p noise data comprising the current moment and the moment adjacent to the current moment to the noise state variables in the state space model to obtain the predicted state variables of the next moment, and assigning the noise state variables in the state space model in a recursion mode based on the predicted state variables of the next moment to obtain the predicted state variables of the next N moments;
the noise prediction module is used for obtaining corresponding prediction noise output variables according to each prediction state variable;
the objective function construction module is used for constructing an objective function according to each predicted noise output variable and the expected output noise; the objective function is used for reflecting errors between output variables of each prediction noise and expected output noise;
and the output module is used for solving the object of the minimized objective function according to the preset constraint condition and controlling the loudspeaker unit to generate a cancellation sound wave signal according to the solving result.
In a third aspect, the present application provides a computer device comprising one or more processors, and a memory having stored therein computer readable instructions which, when executed by the one or more processors, perform the steps of the method for noise reduction of a drone library in any of the embodiments described above.
In a fourth aspect, the present application provides a storage medium having stored therein computer readable instructions, which when executed by one or more processors, cause the one or more processors to perform the steps of the method for noise reduction of a drone hangar in any of the embodiments described above.
From the above technical solutions, the embodiment of the present application has the following advantages:
based on any embodiment, the light source which can be controlled by the divided areas in the edge folding defect detection system is utilized, when any object edge folding needs to be detected, the light source of the opposite area of the object edge folding is started, the object edge folding is uniformly polished, the problems of edge folding arc angle reflection and uneven imaging are effectively eliminated, the image recognition is carried out based on the first image with low interference noise, and whether the edge folding of the object to be detected has defects can be accurately judged.
Drawings
In order to more clearly illustrate the embodiments of the application or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the application, and that other drawings can be obtained from these drawings without inventive faculty for a person skilled in the art.
Fig. 1 is a schematic flow chart of a noise reduction method for an unmanned aerial vehicle library according to an embodiment of the present application;
fig. 2 is an internal structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The application provides a noise reduction method for an unmanned aerial vehicle hangar. It will be appreciated that the plurality of first sound sensors will be arranged around the interior of the unmanned aerial vehicle hangar in an array manner, as there are a plurality of devices in the unmanned aerial vehicle hangar, each device may emit noise, and the noise perceived in each direction may be different, by this arrangement manner, the unmanned aerial vehicle hangar can be collected more comprehensively as the condition of the whole noise source. The speaker unit is a device for generating an acoustic signal that cancels out noise of the unmanned aerial vehicle library, that is, the active noise cancellation method adopted in the present embodiment cancels noise of the unmanned aerial vehicle library. Specifically, referring to fig. 1, the noise reduction method includes steps S102 to S114.
S102, acquiring historical noise data acquired by a plurality of first sound sensors.
It can be appreciated that when the unmanned aerial vehicle hangar is running, each first sound sensor will collect current noise data at each collection time, and this embodiment stores these noise data to form historical noise data.
S104, constructing a p-order autoregressive model according to the historical noise data. The autoregressive model includes autoregressive coefficients.
An autoregressive model is a statistical model used to describe time series data, assuming that the data at the current time instant can be represented by a linear combination of data at p times before, where p is a positive integer, called an autoregressive order. The noise data collected by each first sound sensor can be described by a corresponding autoregressive model, and the description of the expression is as follows:
where x (t) is noise data at time point t, c is a constant term, a i For the ith autoregressive coefficient, p is the order of the autoregressive model and w (t) is unpredictable process noise. Many mature techniques exist in the prior art for modeling by using historical data to obtain autoregressive models, such as least squares, maximumLikelihood method, etc., reference is made specifically to the prior art. The number of the order selection modes of the autoregressive model is also quite large, for example, the red pool information criterion, the Bayesian information criterion and the like.
S106, constructing a state space model according to the autoregressive model. The state space model includes noise state variables and control input variables, and autoregressive coefficients are used to generate constant terms for the noise state variables.
It will be appreciated that each noise data may use a state of noise as a state variable, the cancellation sound wave signal output by the speaker unit as a control input variable, and the noise data collected by the first sound sensor is a state space of discrete time of the output variable, such as:
x(k+1)＝A*x(k)+u(k)+w(k)
y(k)＝C*x(k)+v(k)
wherein a x (k) is a noise state variable, x (k) is a p×1 matrix, p elements in the matrix represent noise states from k time to p times, a is a p×p matrix, the first row is an autoregressive coefficient, and the rest rows are triangular matrices under unit. u (k) is a control input variable, which is a p×1 matrix, wherein the first element in the control input variable represents the counteracting sound wave signals emitted to the unmanned aerial vehicle pool at the moment k, and the rest elements are 0.w (k) is a p×1 vector representing the process noise in the autoregressive model, the first element is chosen to be white noise ε (k), and the remaining elements are zero. Since the noise model is assumed to be an autoregressive model, the output of the autoregressive model is the state at the current time. Therefore, we need only represent the noise output with the first element of the state vector, and no other elements are needed. In this way, we can simplify the output equation of the noise model, making it easier to handle. Thus, C is a 1×p matrix, with the first element being 1 and the remaining elements being zero; v (k) is a scalar quantity representing the measurement noise of the first acoustic sensor.
S108, assigning the p noise data including the current moment and the moment adjacent to the current moment to the noise state variables in the state space model to obtain the predicted state variables of the next moment, and assigning the noise state variables in the state space model in a recursion mode based on the predicted state variables of the next moment to obtain the predicted state variables of the next N moments.
After the modeling work is completed, a state space model can be utilized to predict future noise changes. Specifically, for any one of the first sound sensors, p pieces of noise data at the current time and p pieces of noise data before the current time are substituted into x (k) of the above expression, and the obtained x (k+1) is the predicted state variable at the next time. And then, sequentially recursively substituting x (k+1) to obtain a plurality of prediction state variables x (k+2). X (k+N). The first element in each predicted state variable is a noise state that contains an unknown quantity that is the cancellation of the acoustic signal.
S110, obtaining corresponding prediction noise output variables according to each prediction state variable.
It will be appreciated that, due to the simplified processing performed by the present application, the noise state is noise data, and the first element in each predicted state variable is noise data containing an unknown quantity of canceling acoustic signals.
S112, constructing an objective function according to 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.
It can be understood that the expected output noise is the noise data after active noise suppression in the expectation. Each first sound sensor corresponds to N predicted state variables, a first element in each predicted state variable can be compared with expected output noise, and all comparison results are summed to obtain an objective function. Assuming m first sound sensors, there will be m x N error terms to sum. And respectively calculating and then summing error items of all the first sound sensors, wherein the value of the objective function can reflect the overall noise level around the whole unmanned aerial vehicle hangar after active noise cancellation. Specifically, to better reflect the error magnitude, the squares of the differences between the respective prediction noise output variables and the expected output noise may be calculated separately and summed to obtain the objective function.
And S114, solving the minimum objective function according to a preset constraint condition, and controlling the loudspeaker unit to generate a cancellation sound wave signal according to a solving result.
It is understood that the preset constraint condition is a condition that constrains optimization of the objective function, which is set according to the actual physical characteristics of noise, the output capability of the speaker unit, and the like. Specifically, at least one of the following may be included: the magnitude of the control input variable does not exceed the maximum output capability of the speaker unit. The magnitude of the predicted state variable is within a second limit range. The magnitude of the predicted noise output variable is within a first limit range.
The value of the objective function can reflect the overall noise level around the whole unmanned aerial vehicle hangar after active noise cancellation, and the smaller the value is, the better the representative noise reduction effect is. The objective function is a function taking the control input variable as an unknown quantity, and the objective function is taken as a minimum objective function to solve, so that the offset acoustic wave signal played into the unmanned aerial vehicle library can be obtained, and the error between the offset noise data and the expected output noise can be minimized. The manner of solution may be determined based on the objective function and the type of preset constraints. Linear quadratic programming can be employed to solve for, for example, using the sum of squares of the differences described above.
Based on the noise reduction method in the embodiment, a plurality of first sound sensors are arranged in the unmanned aerial vehicle library, noise emitted by each device in the unmanned aerial vehicle library is collected more comprehensively, and an autoregressive model is built based on historical noise data collected by each first sound sensor. The unmanned aerial vehicle library can utilize discrete state space description and utilize coefficients obtained from an autoregressive model to construct the state space model. And predicting a predicted noise output variable of N periods in the future by using the state space model, and constructing an objective function based on an error between the predicted noise output variable and the expected output noise. And finally, calculating the minimum value of the objective function under the preset constraint condition to obtain the offset acoustic wave signal. Noise in the unmanned aerial vehicle hangar can be actively eliminated through the offset acoustic signals, and popularization and deployment of the unmanned aerial vehicle hangar are facilitated.
In one embodiment, the set position of the speaker unit is determined by:
(1) More than one noise reduction direction is determined.
It will be appreciated that, with the unmanned aerial vehicle library as the center, the noise reduction direction refers to a direction in which the influence of noise should be eliminated, for example, some directions of the unmanned aerial vehicle library are residential areas, and other directions may allow noise to exist, and then the direction in which the residential areas exist is determined as the noise reduction direction.
(2) For any noise reduction direction, more than two measuring points are arranged in the noise reduction direction according to a preset interval, and a second sound sensor is arranged at each measuring point. One of the measuring points is located on the surface of the unmanned aerial vehicle hangar.
It can be understood that the second sound sensor is a sound sensor used when positioning the microphone unit, at least two measuring points need to be set in each noise reduction direction, and noise data collected by the second sound sensor installed on the surface of the unmanned aerial vehicle can reflect waveforms when sound is generated from the unmanned aerial vehicle library and is not transmitted in the noise reduction direction. The other measuring points reflect waveforms of sound after a certain distance is transmitted in the noise reduction direction.
(3) And keeping the unmanned aerial vehicle library in an operating state and the loudspeaker unit in a closed state, and comparing the acquired data corresponding to the surface with the acquired data corresponding to other measuring points to obtain a first transfer characteristic.
It can be understood that when the collected data corresponding to the surface represents noise generated by the unmanned aerial vehicle hangar, the original noise which is not transmitted yet, and other collected data represent noise after the original noise is transmitted for a certain distance in the noise direction, and the noise after each transmission is compared with the original noise, so that the influence of the noise in the unmanned aerial vehicle hangar on the original noise generated by the transmission in the noise reduction direction, namely the first transmission characteristic, can be obtained.
(4) And keeping the unmanned aerial vehicle library in a closed state, enabling the loudspeaker unit to play preset audio, and comparing acquired data corresponding to the surface with acquired data corresponding to other measuring points to obtain a second transfer characteristic.
It can be understood that the collected data corresponding to the surface represents the preset audio which is not transmitted yet when only the speaker unit is sounding, while the other collected data represents the audio which is transmitted by the preset audio for a certain distance in the noise direction, and the influence of the transmission of the sound emitted by the speaker unit on the sound emitted by the speaker unit in the noise reduction direction, namely the second transmission characteristic, can be obtained by comparing the transmitted audio with the audio.
(5) According to the difference between the first transfer characteristic and the second transfer characteristic, the setting position of the speaker unit is adjusted, after the adjustment is completed, the unmanned aerial vehicle is returned to be kept in a closed state, the speaker unit plays preset audio, collected data corresponding to the surface is compared with collected data corresponding to other measuring points, and the step of obtaining the second transfer characteristic is continuously executed until the difference between the second transfer characteristic and the first transfer characteristic meets the error requirement.
It will be appreciated that in order to ensure that the sounds emitted by the unmanned aerial vehicle library and the loudspeaker unit cancel each other well after being propagated in air, it is necessary to have similar transfer characteristics in the noise reduction direction for the two sound sources, and therefore, by continuously adjusting the position of the microphone unit and comparing the new second transfer characteristic after the position adjustment with the first transfer characteristic, a position where the microphone unit can have similar transfer characteristics to the unmanned aerial vehicle library is finally found, and the microphone unit is kept at that position.
In one embodiment, the base of the unmanned aerial vehicle hangar is provided with an adjustable shock absorbing unit, and the contact surface of the base of the unmanned aerial vehicle hangar is provided with more than one vibration sensor which is used for collecting vibration data. It can be appreciated that, considering that vibration is generated when equipment in the unmanned aerial vehicle hangar runs, the vibration also generates noise, so this embodiment sets up the shock-absorbing unit with adjustable shock-absorbing damping size, which can be used for eliminating the vibration, the noise reduction method further includes: and adjusting the shock absorption damping of the adjustable shock absorption unit according to the vibration data. Wherein, the stronger the vibration data, the greater the shock damping. Namely, the shock absorbing unit in the embodiment can automatically adjust the magnitude of shock absorbing damping according to the intensity of vibration.
In one embodiment, the expected output noise is a signal below a human ear perception threshold.
In one embodiment, in order to ensure that each component works normally in the actual aircraft nest operation, an air conditioner needs to be installed inside, the higher the external temperature is, the higher the refrigeration requirement is, the more frequent the operation such as a compressor is, the more obvious the noise is generated, a temperature sensor can be additionally arranged in the unmanned aerial vehicle aircraft base, and the temperature record can be added in the historical noise data together, so that the noise characteristics can be analyzed systematically.
The application provides a noise reduction device of an unmanned aerial vehicle hangar, wherein a loudspeaker unit and a plurality of first sound sensors are arranged in the unmanned aerial vehicle hangar, the first sound sensors are used for collecting noise data, and the noise reduction device comprises 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.
The data acquisition module is used for acquiring historical noise data acquired by the plurality of first sound sensors.
The first modeling module is used for constructing an autoregressive model of the order p according to the historical noise data. The autoregressive model includes autoregressive coefficients.
And the second modeling module is used for constructing a state space model according to the autoregressive model. The state space model includes noise state variables and control input variables, and autoregressive coefficients are used to generate constant terms for the noise state variables.
And the recursion module is used for assigning the p noise data comprising the current moment and the moment adjacent to the current moment to the noise state variables in the state space model to obtain the predicted state variables of the next moment, and assigning the noise state variables in the state space model in a recursion mode based on the predicted state variables of the next moment to obtain the predicted state variables of the next N moments.
And the noise prediction module is used for obtaining corresponding prediction noise output variables according to each prediction state variable.
And the objective function construction module is used for constructing an objective function according to 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.
And the output module is used for solving the object of the minimized objective function according to the preset constraint condition to obtain a solution of the control input variable, and generating a counteracting sound wave signal according to the solving result.
The application provides a computer device, comprising one or more processors and a memory, wherein the memory stores computer readable instructions, and the computer readable instructions execute the steps of the noise reduction method of the unmanned aerial vehicle library in any embodiment when being executed by the one or more processors.
Schematically, as shown in fig. 2, fig. 2 is a schematic diagram of an internal structure of a computer device according to an embodiment of the present application. Referring to FIG. 2, a computer device 200 includes a processing component 202 that further includes one or more processors, and memory resources represented by memory 201, for storing instructions, such as application programs, executable by the processing component 202. The application program stored in the memory 201 may include one or more modules each corresponding to a set of instructions. Further, the processing component 202 is configured to execute instructions to perform the steps of the method of noise reduction of the drone hangars of any of the embodiments described above.
The computer device 200 may also include a power 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 based on an operating system stored in the memory 201, such as Windows Server TM, mac OS XTM, unix TM, linux TM, free BSDTM, or the like.
It will be appreciated by persons skilled in the art that the architecture shown in fig. 2 is merely a block diagram of some of the architecture relevant to the present inventive arrangements and is not limiting as to the computer device to which the present inventive arrangements are applicable, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
The present application provides a storage medium having stored therein computer readable instructions which, when executed by one or more processors, cause the one or more processors to perform the steps of the method for noise reduction of a drone hangar in any of the embodiments described above.
Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In the present specification, each embodiment is described in a progressive manner, and each embodiment focuses on the difference from other embodiments, and may be combined according to needs, and the same similar parts may be referred to each other.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended 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 (10)
1. The noise reduction method for the unmanned aerial vehicle hangar is characterized in that a loudspeaker unit and a plurality of first sound sensors are arranged in the unmanned aerial vehicle hangar, the first sound sensors are used for collecting noise data, and the noise reduction method comprises the following steps:
acquiring historical noise data acquired by the plurality of first sound sensors;
constructing an autoregressive model of the p-order according to the historical noise data; the autoregressive model includes autoregressive coefficients;
constructing a state space model according to the autoregressive model; the state space model comprises a noise state variable and a control input variable, and the autoregressive coefficients are used for generating constant terms of the noise state variable;
assigning values to the noise state variables in the state space model, including the current time and p times adjacent to the current time, to obtain a predicted state variable at the next time, and assigning values to the noise state variables in the state space model in a recursive manner based on the predicted state variable at the next time to obtain the predicted state variables at the next N times;
obtaining corresponding prediction noise output variables according to each prediction state variable;
constructing an objective function according to each predicted noise output variable and the expected output noise; the objective function is used for reflecting errors between each predicted noise output variable and the expected output noise;
and solving the target of minimizing the objective function according to a preset constraint condition to obtain a solution of the control input variable, and controlling the loudspeaker unit to generate a cancellation sound wave signal according to a solving result.
2. The noise reduction method according to claim 1, wherein the preset constraint condition includes at least one of:
the magnitude of the control input variable does not exceed the maximum output capability of the speaker unit;
the magnitude of the predicted state variable is within a second limit range;
the magnitude of the predicted noise output variable is within a first limit range.
3. The method of noise reduction according to claim 1, wherein said constructing an objective function from each of said predicted noise output variables and expected output noise comprises:
and respectively calculating the squares of the differences between the prediction noise output variables and the expected output noise, and summing to obtain the objective function.
4. A method of noise reduction according to claim 3, wherein solving the objective function comprises a linear quadratic programming method.
5. The noise reduction method according to claim 1, wherein the setting position of the speaker unit is determined by:
determining more than one noise reduction direction;
for any one noise reduction direction, setting more than two measuring points in the noise reduction direction according to a preset interval, and setting a second sound sensor at each measuring point; one measuring point is positioned on the surface of the unmanned aerial vehicle library;
maintaining the unmanned aerial vehicle hangar in an operating state and the loudspeaker unit in a closed state, and comparing acquired data corresponding to the surface with acquired data corresponding to the measuring point Yu Gesuo to obtain a first transfer characteristic;
keeping the unmanned aerial vehicle library in a closed state, enabling the loudspeaker unit to play preset audio, and comparing acquired data corresponding to the surface with acquired data corresponding to the measuring point Yu Gesuo to obtain a second transfer characteristic;
and adjusting the setting position of the speaker unit according to the difference between the first transfer characteristic and the second transfer characteristic, returning to the state of keeping the unmanned aerial vehicle library in a closed state after the adjustment is completed, enabling the speaker unit to play preset audio, comparing the acquired data corresponding to the surface with the acquired data corresponding to the measuring point Yu Gesuo of the speaker unit, and continuing to execute the step of obtaining the second transfer characteristic until the difference between the second transfer characteristic and the first transfer characteristic meets the error requirement.
6. The noise reduction method according to claim 1, wherein the base of the unmanned aerial vehicle hangar is provided with an adjustable shock absorbing unit, and at a contact surface of the base of the unmanned aerial vehicle hangar, more than one vibration sensor is provided, the vibration sensor is used for collecting vibration data, the noise reduction method further comprises:
adjusting the shock absorption damping of the adjustable shock absorption unit according to the vibration data; wherein, the stronger the vibration data is, the greater the shock damping is.
7. The method of noise reduction according to claim 6, wherein the expected output noise is a signal below a human ear perception threshold.
8. Noise reduction device of unmanned aerial vehicle hangar, its characterized in that unmanned aerial vehicle hangar has set up speaker unit and a plurality of first sound sensor, first sound sensor is used for gathering noise data, noise reduction device includes:
the data acquisition module is used for acquiring historical noise data acquired by the plurality of first sound sensors;
the first modeling module is used for constructing an autoregressive model of the p-order according to the historical noise data; the autoregressive model includes autoregressive coefficients;
the second modeling module is used for constructing a state space model according to the autoregressive model; the state space model comprises a noise state variable and a control input variable, and the autoregressive coefficients are used for generating constant terms of the noise state variable;
the recursion module is used for assigning values to the noise state variables in the state space model, including the current time and p times adjacent to the current time, to obtain a predicted state variable of the next time, and assigning values to the noise state variables in the state space model in a recursion mode based on the predicted state variable of the next time to obtain the predicted state variables of N times in the future;
the noise prediction module is used for obtaining corresponding prediction noise output variables according to each prediction state variable;
the objective function construction module is used for constructing an objective function according to each predicted noise output variable and the expected output noise; the objective function is used for reflecting errors between each predicted noise output variable and the expected output noise;
and the solving module is used for solving the object function with the aim of minimizing the objective function according to a preset constraint condition to obtain a solution of the control input variable, and controlling the loudspeaker unit to generate a cancellation sound wave signal according to a solving result.
9. A computer device comprising one or more processors and a memory having stored therein computer readable instructions which, when executed by the one or more processors, perform the steps of the method of noise reduction of a drone base of any one of claims 1-7.
10. A storage medium having stored therein computer readable instructions which, when executed by one or more processors, cause the one or more processors to perform the steps of the method of noise reduction of a drone library according to any one of claims 1-7.
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| CN117496938B (en) * | 2023-12-22 | 2024-03-15 | 浙江恒逸石化有限公司 | Noise processing method and device, electronic equipment and storage medium |
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