Noise reduction method, apparatus, electronic device, and readable medium

Abstract

To provide a method, a device, an electronic apparatus and a readable medium for reducing noise audible to passengers riding in an elevator in real time without increasing the cost or load of the elevator.SOLUTION: A noise reduction method includes: collecting noise data during elevator operation and elevator operation data; selecting a corresponding noise reduction processing method based on a noise type of the noise data; performing noise reduction processing on the noise data based on the noise reduction processing method to obtain a noise reduction signal; calculating an output delay time of the noise reduction signal based on a noise reduction position within the elevator and a position of a noise reduction device; and outputting the noise reduction signal based on the delay time.SELECTED DRAWING: Figure 4

Description

Technical Field

[0001] This application relates to the field of noise processing technology, and specifically to a noise reduction method, apparatus, electronic device, and readable medium.

Background Art

[0002] Recently, the number of high-rise residential buildings and high-rise office buildings has become extremely large, and people's dependence on elevators has become very high. Elevators belong to high-power equipment, and large noises are generated in each main component of the elevator from the machine room to the car. The noise signal of the elevator mainly concentrates in the low and medium frequency bands, and it is obvious that high-intensity low and medium frequency band noises will damage people's hearing and also cause various degrees of damage to other parts of the human body. Currently, passive noise control (PNC) is generally adopted in elevator noise reduction measures, that is, noise reduction is achieved by additionally installing sound insulation plates. However, the PNC technology only has a good blocking effect on high-frequency band noises. However, since the air noise and structure noise generated during elevator operation belong to low and medium frequency band noises, when using the PNC technology to perform noise reduction processing on the elevator, the noise reduction effect is not significant, and it is necessary to install a large number of sound insulation plates on the elevator, which will further increase the cost and load of the elevator.

Summary of the Invention

Problems to be Solved by the Invention

[0003] This application provides a noise reduction method, apparatus, electronic device, and readable medium in embodiments.

Means for Solving the Problems

[0004] In a first embodiment, the present invention provides a noise reduction method applicable to a noise reduction device, the method comprising: collecting noise data and elevator operation data during elevator operation; selecting a corresponding noise reduction processing method based on the noise type of the noise data; performing noise reduction processing on the noise data based on the noise reduction processing method to obtain a noise reduction signal; calculating the output delay time of the noise reduction signal based on the noise reduction location in the elevator and the location of the noise reduction device; and outputting the noise reduction signal based on the delay time.

[0005] As can be understood, this noise reduction method involves installing multiple noise reduction devices on the inner wall of the elevator car or on the shared wall between the resident's living area and the elevator shaft wall. These noise reduction devices use a software algorithm to reduce the noise generated in the elevator, thereby reducing the noise audible to passengers riding the elevator. Furthermore, the external form of these noise reduction devices may be, for example, thin plate-like, and since they are small in volume and light in mass, the load on elevator operation is reduced. Based on the above noise reduction method, this application can achieve noise reduction in real time by detecting the elevator's operating status, detecting changes in noise data in real time, and generating a noise reduction signal.

[0006] In one possible implementation of the first embodiment described above, the noise type includes air noise data and structural noise data, and the noise reduction device includes a first noise collection unit and a second noise collection unit, the first noise collection unit being used to collect air noise data during elevator operation, and the second noise collection unit being used to collect structural noise data during elevator operation.

[0007] In one possible implementation of the first embodiment described above, the elevator operation data includes at least one of a preset elevator operating speed, a preset acceleration, and preset vibration data of each structure of the elevator that occurs during operation.

[0008] In one possible implementation of the first aspect described above, selecting a corresponding noise reduction processing method based on the noise type of the noise data includes, in the case where the noise type of the noise data is air noise data, the selected noise reduction processing method is a first noise reduction processing method used for noise reduction processing of air noise data, and in the case where the noise type of the noise data is structural noise data, the selected noise reduction processing method is a second noise reduction processing method used to perform noise reduction processing on structural noise data when the structural noise data satisfies the noise reduction conditions, and not to perform noise reduction processing on structural noise data when the structural noise data does not satisfy the noise reduction conditions.

[0009] In one possible implementation of the first embodiment described above, an effective value is calculated for the structural noise data. If the effective value is greater than or equal to a preset threshold, the structural noise data satisfies the noise reduction condition, and noise reduction processing is performed on the structural noise data. If the effective value is less than a preset threshold, the structural noise data does not satisfy the noise reduction condition, and no processing is performed on the structural noise data.

[0010] In one possible implementation of the first embodiment described above, the noise reduction signal includes a Class 1 noise reduction signal corresponding to air noise data, and further, obtaining a noise reduction signal by performing noise reduction processing on noise data based on a noise reduction processing method includes: filtering air noise data in the noise data based on a first noise reduction processing method to obtain a first target noise data; performing a fast Fourier transform on the first target noise data to calculate the center frequency of the first target noise data; determining a first frequency band of the first target noise data based on the center frequency; calculating the median of the first frequency band based on the first frequency band to obtain a first centroid frequency; and combining the first centroid frequencies using a noise reduction algorithm to obtain a Class 1 noise reduction signal.

[0011] In one possible implementation of the first embodiment described above, obtaining a noise reduction signal by performing noise reduction processing on noise data based on a noise reduction processing method includes: obtaining effective structural noise data by calculating an average value or performing fuzzy processing on the elevator operation data and structural noise data in the noise data based on a second noise reduction processing method; obtaining second target noise data by filtering the effective structural noise data; calculating effective values ​​of the second target noise data; and processing the second target noise based on the effective values ​​of the second target noise data.

[0012] In one possible implementation of the first embodiment described above, the noise reduction signal includes a second type noise reduction signal corresponding to structural noise data, and further processing the second target noise based on the valid values ​​of the second target noise data includes determining whether the second target noise data satisfies the noise reduction conditions based on the valid values ​​of the second target noise data, performing a fast Fourier transform on the second target noise data to calculate the center frequency of the second target noise data, determining a second frequency band of the second target noise data based on the center frequency, calculating a second median of the second frequency band based on the second frequency band to obtain a second centroid frequency, and combining the second centroid frequencies using a noise reduction algorithm to obtain a second type noise reduction signal.

[0013] In one possible implementation of the first embodiment described above, calculating the delay time based on the noise reduction position and the position of the noise reduction device includes, when the noise reduction device is one of a plurality of noise reduction devices, determining the distance between the noise reduction position and the noise reduction device and calculating the delay time at which the noise reduction device outputs a noise reduction signal, wherein the delay time of a noise reduction device farther from the noise reduction position is shorter than the delay time of a noise reduction device closer to the noise reduction position.

[0014] In one possible implementation of the first embodiment described above, the noise reduction device comprises a first noise reduction signal output unit and a second noise reduction signal output unit, wherein the first noise reduction signal output unit of the noise reduction device is used to output a Class 1 noise reduction signal corresponding to air noise data, and the second noise reduction signal output unit of the noise reduction device is used to output a Class 2 noise reduction signal corresponding to structural noise data.

[0015] In one possible embodiment of the first aspect described above, the first noise acquisition unit comprises at least one reference microphone, and the second noise acquisition unit comprises at least one vibration sensor.

[0016] In one possible embodiment of the first aspect described above, the first noise reduction signal output unit comprises at least one diaphragm speaker, and the second noise reduction signal output unit comprises at least one vibration speaker.

[0017] In one possible implementation of the first embodiment described above, the filtering process includes at least one of high-pass filtering, band-pass filtering, and low-pass filtering.

[0018] In one possible implementation of the first embodiment described above, the noise reduction algorithm is the FxLMS algorithm.

[0019] In a second embodiment, the present invention provides a noise reduction device comprising a data acquisition module, a data processing module, and a signal output module, wherein the data acquisition module is used to collect noise data and elevator operation data during elevator operation; the data processing module is used to select a corresponding noise reduction processing method based on the noise type of the noise data and to perform noise reduction processing on the noise data based on the noise reduction processing method to obtain a noise reduction signal; the data processing module is also used to calculate the output delay time of the noise reduction signal based on the noise reduction location in the elevator and the location of the noise reduction device; and the signal output module is used to output the noise reduction signal based on the delay time.

[0020] In one possible implementation of the second embodiment described above, the noise reduction device is one of a plurality of noise reduction devices attached to the elevator, the noise reduction device is any one of the plurality of noise reduction devices, or the noise reduction device is the noise reduction device among the plurality of noise reduction devices that has the highest degree of idle computing power.

[0021] In one possible implementation of the second embodiment described above, the device further comprises an intelligent selection module, which is used to obtain the degree of idle computing power of each of the multiple noise reduction devices and to determine that the noise reduction device is the one with the highest degree of idle computing power among the multiple noise reduction devices.

[0022] In a possible implementation of the second aspect described above, calculating the output delay time of the noise reduction signal based on the noise reduction position in the elevator and the position of the noise reduction device involves the noise reduction device collecting each center frequency obtained from the processing of the noise data of each of the plurality of noise reduction devices, combining each center frequency using a noise reduction algorithm to calculate the corresponding noise reduction signal for each of the plurality of noise reduction devices, and calculating the delay time of the noise reduction signal output by each noise reduction device in the plurality of noise reduction devices based on the noise reduction position and the position of each noise reduction device in the plurality of noise reduction devices.

[0023] In a possible implementation of the second aspect described above, the outer shape of the noise reduction device is in the form of a thin plate.

[0024] In a possible implementation of the second aspect described above, the position of the noise reduction device includes at least one of the inner wall surface of the elevator car and the outer side of the elevator shaft wall.

[0025] In a third aspect, in an embodiment of the present application, an electronic device is provided that includes one or more processors and one or more memories. One or more programs are stored in the one or more memories, and when the one or more programs are executed by the one or more processors, the noise reduction method provided in the first aspect and each possible implementation in the first aspect is executed by the electronic device.

[0026] In a fourth aspect, in an embodiment of the present application, a computer-readable medium is provided. A command is stored in the storage medium, and when the command is executed on a computer, the noise reduction method provided in the first aspect and each possible implementation in the first aspect is executed by the computer.

Brief Description of the Drawings

[0027] [Figure 1] FIG. 1 is a schematic diagram showing a scene where noise interference is received during elevator riding. [Figure 2]FIG. 2 is a schematic diagram showing a scene where noise generated during elevator operation affects residents. [Figure 3] FIG. 3 is a schematic structural diagram of a noise reduction device provided based on an embodiment of the present application. [Figure 4] FIG. 4 is a specific implementation flowchart of a noise reduction method provided based on an embodiment of the present application. [Figure 5] FIG. 5 is a schematic diagram showing modules of a noise reduction device provided based on an embodiment of the present application. [Figure 6] FIG. 6 is a schematic structural diagram of an electronic device provided based on an embodiment of the present application.

Embodiments for Carrying Out the Invention

[0028] Next, specific embodiments of the present application will be described in detail with reference to the drawings. However, it is clear that the described embodiments are only some of the embodiments of the present application, not all of them. Based on the embodiments in the present application, any other embodiments obtained by those skilled in the art without creative work are included in the protection scope of the present application.

[0029] FIG. 1 is a diagram showing a scene where noise interference is received during elevator boarding.

[0030] The scene shown in FIG. 1 includes a guide rail 101, a suspension cable 102, a car 103, a guide shoe 104, and a shaft wall 105. As shown in FIG. 1, when passengers board the elevator, they encounter two types of noise: air noise and structure noise. Here, the air noise is the wind cut noise generated when the air in the gap between the shaft wall 105 rapidly flows during the operation of the car 103. The faster the running speed of the car 103, the greater the corresponding wind cut noise. The structure noise is the noise caused by the vibration of elevator parts, such as the vibration noise caused by the vibration of the guide shoe on the guide rail 101, the vibration noise of the suspension cable 102, and the vibration noise of the car is mentioned.

[0031] Furthermore, Figure 2 is a schematic diagram illustrating how noise generated during elevator operation affects residents.

[0032] The scene shown in Figure 2 includes a shaft 201, an elevator 202, a shaft wall 203, and a living area 204. As shown in Figure 2, when a passenger boards the elevator, the air noise and structural noise generated between the structures described in Figure 1 are transmitted through the shaft wall 203 to the living area 204, resulting in significant noise interference for the occupants. In some scenes, the shaft wall 203 may also be the wall of the occupants' living area 204.

[0033] As mentioned above, elevator noise is usually reduced physically by adding soundproofing panels to the elevator. For example, noise is reduced by temporarily installing soundproofing panels such as rectifier covers or sound-absorbing materials on the elevator. However, the effectiveness of this physical reduction method using soundproofing panels is not guaranteed.

[0034] To solve the above problems, the present invention provides a noise reduction method, which involves installing multiple noise reduction devices on the inner wall of the elevator car or on the shared wall between the resident's living area and the elevator shaft wall. These noise reduction devices use a software algorithm to reduce the noise generated in the elevator, thereby reducing the noise audible to passengers riding the elevator. Furthermore, the external form of these noise reduction devices may be, for example, a thin plate, and since they are small in volume and light in mass, the load on elevator operation is reduced.

[0035] Specifically, this method collects noise data during elevator operation and elevator operation data, and can select a corresponding noise reduction processing method based on the noise type of the collected noise data. For example, the noise type determined by this method is air noise or structural noise, and the corresponding noise reduction processing method differs depending on the noise type. Next, the collected noise data is processed, such as by filtering, to obtain target noise data, and then noise reduction processing is performed on the target noise data based on the selected noise reduction processing method to obtain a noise reduction signal. Furthermore, this method calculates the delay time at which each noise reduction device outputs its noise reduction signal, based on noise reduction locations such as the location of the ears of passengers riding the elevator or the location of each noise reduction device. Finally, each noise reduction device can output a noise reduction signal after the corresponding delay time has elapsed. For example, a noise reduction device far from the noise reduction location outputs the noise reduction signal first, and a noise reduction device close to the noise reduction location outputs the noise reduction signal after a certain delay time has elapsed, thereby ensuring that the noise reduction signals reach the same noise reduction location simultaneously and achieving a good noise reduction effect.

[0036] In several embodiments, specific implementations of the noise reduction processing method in this application are illustrated. For example, for target noise data in the collected noise data that actually requires noise reduction processing, the center frequency of the target noise data can be calculated, and a corresponding frequency band can be determined based on the center frequency. Next, based on the corresponding frequency band, the corresponding median value can be calculated to obtain the centroid frequency of each noise reduction device. Furthermore, based on the centroid frequency of each noise reduction device, the method can calculate the corresponding noise reduction signal for each noise reduction device using the FxLMS (filters-x least mean square) algorithm. Here, the above noise reduction signal and the noise signal corresponding to the noise data have the same amplitude value and opposite phase, and in this way, the noise reduction signal output by each noise reduction device cancels out the corresponding target noise data, thereby achieving a noise reduction effect.

[0037] In some embodiments, the noise reduction position is the area where the ears of elevator passengers are located, for example, an area 1.0m to 1.8m above the bottom plate of the elevator car. Based on the noise reduction position, the distance between each noise reduction device and the noise reduction position is determined, and the delay time for outputting the noise reduction signal between each noise reduction device is calculated. Specifically, in this application, the propagation time required for the noise reduction signal output by each noise reduction device to propagate to the noise reduction position can be calculated based on the speed of sound and the noise reduction position. Furthermore, based on the difference between the above propagation times, the delay time for outputting the noise reduction signal between each noise reduction device is determined, thereby ensuring that the noise reduction signals output by each noise reduction device can propagate to the noise reduction position simultaneously.

[0038] As can be understood, elevator noise data constantly changes over time. Therefore, by analyzing the elevator noise data in real time, the corresponding target noise frequency can be determined. Based on this, the center frequency value, the number of center frequencies, and the filter frequency are all set based on real-time conditions, enabling directional filtering of the constantly changing noise signal in the elevator noise data.

[0039] As can be seen from the above, based on the noise reduction method described above, the present invention can achieve noise reduction in real time by detecting the elevator's operating status, detecting changes in noise data in real time, and generating a noise reduction signal.

[0040] Next, the noise reduction device provided in the embodiment of the present application will be described. Specifically, Figure 3 is a schematic diagram of the structure of the noise reduction device provided based on the embodiment of the present application.

[0041] As shown in Figure 3(A) or (B), Figure 3(A) is a schematic front view of the noise reduction device provided in this application, and Figure 3(B) is a schematic side view of the noise reduction device. As shown in Figure 3, the device comprises a reference microphone array 301, an error microphone array 302, a vibration sensor 303, a diaphragm speaker 304, a calculation unit 305, and a vibration speaker 306. It should be understood that the position and number of each structure in the noise reduction device in this application can be set according to the actual needs and are not limited thereto. For example, the external shape of the noise reduction device shown in Figure 3 may be a thin plate shape.

[0042] Here, the reference microphone array 301 is oriented outwards from the elevator car and can be used to collect air noise during elevator operation.

[0043] The error microphone array 302 is directed towards the inner wall of the elevator car and can be used to measure whether the noise reduction signal corresponding to the output air noise matches the predicted output noise signal, and to perform error detection and correction on the noise reduction signal before output based on the measurement results to ensure the output of an accurate noise reduction signal.

[0044] The vibration sensor 303 is used to collect structural noise during elevator operation.

[0045] The diaphragm speaker 304 is used to output a noise reduction signal that corresponds to air noise.

[0046] The calculation unit 305 is used to calculate noise reduction signals corresponding to each noise type based on the noise data.

[0047] The vibration speaker 306 is used to output a noise reduction signal that corresponds to structural noise.

[0048] In several selectable embodiments, the present invention allows for the installation of one or more noise reduction devices for a single elevator, and different noise reduction devices can be installed in different locations. For example, the multiple noise reduction devices may be installed on the same car wall of the elevator car, on different car walls, or on the shaft wall 203 as shown in Figure 2, and there are no limitations thereon.

[0049] In some embodiments, the present invention allows for the selection of one noise reduction device from a plurality of installed noise reduction devices to be the main device for executing the noise reduction method, and this noise reduction device may be referred to as the main device. As can be understood, the main device can be used to collect noise data, such as the centroid frequency, processed by other noise reduction devices, and calculates the noise reduction signal output by each noise reduction device and the delay time for outputting the noise reduction signal based on the noise reduction algorithm and the centroid frequency.

[0050] To make it clear, one of the one or more noise reduction devices described above may be designated as the main device or main equipment, and may all of the one or more noise reduction devices described above have computing capabilities and all of them may be the main device or main equipment; there are no limitations here.

[0051] To further illustrate the noise reduction method provided in the embodiments of this application, the specific process of the noise reduction method provided in the embodiments of this application will now be described in detail based on the relevant flowchart.

[0052] Referring to the related explanation of the structure of the noise reduction device shown in Figure 3, Figure 4 is a specific implementation flowchart of the noise reduction method provided based on the embodiment of the present application.

[0053] As can be understood, in some embodiments of the present invention, the entities that perform each step of the flow shown in Figure 4 may be the plurality of noise reduction devices or noise reduction equipment described above.

[0054] Specifically, the flow shown in Figure 4 may include the following steps.

[0055] 401: Collect noise data and elevator operation data during elevator operation.

[0056] Here, there may be multiple noise reduction devices, and they may be installed, for example, on the elevator car wall where the guide shoe is located, or at the location where the noise is generated.

[0057] As can be understood, during elevator operation, each noise reduction device can collect noise data during elevator operation, including both air noise and structural noise data. Specifically, a reference microphone can be installed in each noise reduction device to collect air noise data during elevator operation, and a vibration sensor can be installed to collect structural noise data during elevator operation. At the same time, the noise reduction device can acquire predicted elevator operating speed, acceleration, and vibration data generated by each structure of the elevator during operation from the elevator's control terminal.

[0058] 402: Select the appropriate noise reduction processing method based on the noise type of the noise data.

[0059] As described above, air noise and structural noise are present during elevator operation, and different structures in the noise reduction device or equipment collect air noise and structural noise respectively. Therefore, the noise type of the collected noise data can be classified based on different noise collection structures. Furthermore, different noise types correspond to different noise reduction processing methods. For example, when the noise type is air noise, it corresponds to the first noise reduction processing method, and when the noise type is structural noise, it corresponds to the second noise reduction processing method. The specific processing processes of the first and second noise reduction processing methods will be explained in detail below, and will not be explained here.

[0060] 403: Based on the selected noise reduction processing method, the noise data is processed to obtain a noise-reduced signal.

[0061] As can be understood, when the selected noise reduction processing method is the first noise reduction processing method, the corresponding noise type is air noise. Typically, when air noise is collected, noise reduction processing is performed on the collected air noise; that is, the first noise reduction processing method continuously performs noise reduction processing on the collected air noise. In this case, it is necessary to first filter the air noise data to determine the target noise data. Specifically, in the above filtering process, for example, high-pass filtering, band-pass filtering, or low-pass filtering can be performed on the noise data. As can be understood, the elevator noise data can be filtered to retain the target frequency in the elevator noise data and filter out other frequencies. Alternatively, frequencies around the target frequency can be retained and other frequencies filtered out, or other filtering methods may be used. With such methods, noise data within the range of decibel values ​​that passengers can perceive in the noise signal during elevator operation can be effectively filtered out. Furthermore, a Fast Fourier Transform is performed on the noise data collected by the reference microphone in the noise reduction device to calculate the maximum value of the corresponding noise value frequency for the noise data collected by the reference microphone in each noise reduction device. This frequency is then used as the center frequency to determine the corresponding noise band. In addition, a median value calculation is performed for each noise band; that is, all noise values ​​are ranked in descending order, and the one in the middle is found and set as the median. If the total number of noise values ​​in a noise band is odd, the median is the average of the two middle noise values ​​after all noise values ​​have been ranked in descending order. The above median value is also set as the centroid frequency corresponding to the area where the noise reduction device is located.

[0062] In some embodiments, the embodiments of the present invention can receive the centroid frequency of other noise reduction devices from a main device or main equipment, and calculate the corresponding noise reduction signals output by each noise reduction device using the FxLMS algorithm, where the above noise reduction signals and the air noise signals have the same amplitude and opposite phase. Here, the FxLMS algorithm is a filtering algorithm used to obtain a corresponding noise reduction signal based on noise data.

[0063] Furthermore, in some embodiments, the present invention can select the noise reduction device with the highest degree of idle state as the main device or main equipment based on the computational power of the idle state of multiple noise reduction devices at that time. For example, in the present invention, the noise reduction device with the highest degree of idle state at that time can be determined as the main device by randomly selecting one of the multiple noise reduction devices or a default noise reduction device.

[0064] In some other embodiments, the present invention may also allow an operator to manually set any noise reduction device or equipment as the main device or main equipment, or to randomly select one noise reduction device or equipment as the main device or main equipment; there are no limitations thereon. Here, data transmission between the main device and other noise reduction devices can be achieved by communication methods such as wireless communication technology (wireless fidelity, Wi-Fi) or short-range wireless communication technology (Bluetooth).

[0065] As can be understood, when the selected noise reduction method is the second noise reduction method, the corresponding noise type is structural noise. Typically, for structural noise, the second noise reduction method must first determine whether the structural noise at that time requires noise reduction, that is, whether the structural noise has a significant impact on passengers in the elevator, and then perform noise reduction on the structural noise that actually requires noise reduction. As an example, this method can determine whether the collected structural noise requires noise reduction by the following method. That is, first, the operation data provided by the elevator manufacturer is compared with the real-time elevator operation data collected by sensors installed in the noise reduction device, and the average value is calculated or the two operation data are fuzzy processed to obtain an effective low-to-mid-band vibration signal. Furthermore, after filtering the effective low-to-mid-band vibration signal, the effective value (rms value, RMS) is calculated. Here, the effective value is the square root of the vibration signal and is used to represent the magnitude of the energy in the vibration signal. Specifically, it is obtained by calculating using equation (1). In the equation, k+1 represents the total number of sample points in the calculation interval.

[0066]

number

[0067] The following formula represents the sum of squares of each amplitude value in the calculation interval.

[0068]

number

[0069] Thus, the effective value is calculated using the above formula (1), and a preset threshold is referenced to determine whether the structural noise requires noise reduction processing. If the effective value is lower than the preset threshold, it indicates that the noise value generated by the low-to-mid-band vibrations is low, and noise reduction processing is not required. If the effective value is higher than the preset threshold, it indicates that the noise value generated by the low-to-mid-band vibrations is high, and noise reduction processing is required. Here, the effective value is the real-time effective value of the noise data corresponding to the structural noise during elevator operation.

[0070] To make it understandable, when the effective value of the noise data corresponding to structural noise during elevator operation is higher than a preset threshold and noise reduction processing is required, first, a fast Fourier transform is performed on the low-to-mid-band vibration data of each sensor in each noise reduction device to determine the maximum value of the vibration frequency, and this frequency is used as the center frequency to determine the corresponding noise band. Furthermore, a median value calculation is performed for each noise band, that is, after ranking all noise values ​​in descending order, the one in the middle is found and set as the median value. If the total number of noise values ​​in a noise band is odd, the above median value is the average of the two middle noise values ​​after ranking all noise values ​​in descending order. The above median value is also set as the centroid frequency corresponding to the area where the noise reduction device is located.

[0071] Furthermore, the noise reduction device can select the noise reduction device with the highest degree of idle state as the main device or main equipment based on the computational power of the idle state of multiple noise reduction devices at that time. The main device or main equipment also receives the centroid frequencies of the other noise reduction devices and calculates the noise reduction signal of each noise reduction device using the FxLMS algorithm. Here, the above noise reduction signal and the air noise signal have the same amplitude value but opposite phase.

[0072] 404: Based on the noise reduction location and the location of each noise reduction device, calculate the delay time at which each noise reduction device outputs a noise reduction signal.

[0073] To ensure understanding, when outputting noise reduction signals, there is a delay time between each noise reduction device. For example, a noise reduction device far from the noise reduction location outputs the noise reduction signal at a first timing, while a noise reduction device close to the noise reduction location outputs the noise reduction signal at a second timing, thereby ensuring that the noise reduction signals reach the same noise reduction location simultaneously.

[0074] Specifically, the system calculates the delay time for outputting noise reduction signals between noise reduction devices based on noise reduction locations, such as the area 1.0m to 1.8m above the elevator car floor (i.e., the area where passengers' ears are located), and the location of each noise reduction device. More specifically, it calculates the propagation time required for the noise reduction signal output by each noise reduction device to propagate to the noise reduction location, and determines the delay time for outputting noise reduction signals between each noise reduction device based on the difference between these propagation times. This ensures that the noise reduction signals output by each noise reduction device can propagate simultaneously to the noise reduction location.

[0075] 405: Each noise reduction device outputs a noise reduction signal in accordance with the corresponding delay time of each noise reduction equipment.

[0076] As can be understood, the corresponding noise reduction devices output noise reduction signals in accordance with the delay time. For example, at the first timing, a diaphragm speaker in a noise reduction device far from the noise reduction position outputs a noise reduction signal corresponding to air noise, and a vibration speaker outputs a noise reduction signal corresponding to vibration noise. After the delay time has elapsed, i.e., at the second timing, a diaphragm speaker in a noise reduction device close to the noise reduction position outputs a noise reduction signal corresponding to air noise, and a vibration speaker outputs a noise reduction signal corresponding to vibration noise.

[0077] As can be understood, when the corresponding noise type of the noise reduction signal is air noise, each noise reduction device measures whether the noise reduction signal corresponding to the output air noise matches the predicted output noise signal by installing an error microphone, and based on the measurement result, performs error detection and correction on the noise reduction signal to ensure the output of an accurate noise reduction signal.

[0078] To make it easier to understand, with reference to the relevant explanation of the noise reduction method in Figure 4 above, Figure 5 is a schematic diagram of a module of a noise reduction device provided based on an embodiment of the present application.

[0079] As shown in Figure 5, the noise reduction device comprises a data acquisition module 501, a data processing module 502, an intelligent selection module 503, and a signal output module 504.

[0080] Here, the data acquisition module 501 is used to collect noise data and elevator operation data during elevator operation. The specific collection process is described in the relevant explanation in step 401 above, and is omitted here.

[0081] The data processing module 502 is used to select a corresponding noise reduction processing method based on the noise type of the noise data, and to perform noise reduction processing on the noise data based on the noise reduction processing method in order to obtain a noise-reduced signal.

[0082] The data processing module 502 is further used to calculate the output delay time of the noise reduction signal based on the noise reduction location and the location of the noise reduction device within the elevator.

[0083] The intelligent selection module 503 is used to determine the noise reduction device with the highest degree of idle computing power among multiple noise reduction devices as the main device. Here, the main device collects multiple centroid frequencies obtained from processing the noise data of each noise reduction device, combines the multiple centroid frequencies using a noise reduction algorithm to calculate a corresponding noise reduction signal, and calculates the output delay time of the noise reduction signal based on the noise reduction position in the elevator and the position of the noise reduction device.

[0084] The signal output module 504 is used to output the noise reduction signal based on the delay time.

[0085] The electronic device 100 further provided in the embodiments of the present application is used to carry out the noise reduction method shown in Figure 4 above. Specifically, Figure 6 is a schematic diagram of the structure of the electronic device provided based on the embodiments of the present application.

[0086] As shown in Figure 6, the electronic device 100 includes one or more processors 101, system internal memory 102, non-volatile memory (NVM) 103, input / output (I / O) equipment 104, communication interface 105, and system control logic 106 for connecting the processors 101, system internal memory 102, non-volatile memory 103, communication interface 105, and input / output (I / O) equipment 104.

[0087] Here, the processor 101 may include one or more processing units, for example, a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), a micro-programmed control unit (MCU), an artificial intelligence (AI) processor or FPGA (field programmable gate array), a neural network processing unit (NPU), or a processing circuit may include one or more single-core or multi-core processors.

[0088] The system's internal memory 102 is a volatile memory such as random-access memory (RAM) or double data-rate synchronous dynamic random-access memory (DDR SDRAM). The system's internal memory is used to temporarily store data and / or commands. For example, in some embodiments, the system's internal memory 102 can be used to store analyzed control commands.

[0089] The non-volatile memory 103 may comprise one or more tangible, non-transient, computer-readable media for storing data and / or commands. In some embodiments, the non-volatile memory 103 may include any suitable non-volatile memory such as flash memory and / or any suitable non-volatile storage device such as a hard disk drive (HDD), compact disc (CD), digital versatile disc (DVD), or solid-state drive (SSD). In some embodiments, the non-volatile memory 103 may also be a portable storage medium such as a secure digital (SD) memory card.

[0090] In particular, the system internal memory 102 and the non-volatile memory 103 may contain a temporary copy and a persistent copy of the command 107, respectively. The command 107 may include, when executed by at least one of the processors 101, causing the electronic device 100 to implement the noise reduction method provided in each embodiment of the present invention.

[0091] The communication interface 105 may include transceivers for providing a wired or wireless communication interface to the electronic device 100 to communicate with any other suitable equipment via one or more networks. In some embodiments, the communication interface 105 may be integrated into other elements of the electronic device 100, for example, the communication interface 105 may be integrated into the processor 101. In some embodiments, the electronic device 100 can communicate with other equipment via the communication interface 105.

[0092] The input / output (I / O) device 104 may be equipped with input devices such as a keyboard and mouse, and output devices such as a display, allowing the user to interact with the electronic device 100 via the input / output (I / O) device 104.

[0093] The system control logic 106 may include any compatible interface controller to provide any compatible interface to other modules of the electronic device 100. For example, in some embodiments, the system control logic 106 may include one or more memory controllers to provide interfaces connected to the system internal memory 102 and the non-volatile memory 103.

[0094] In some embodiments, at least one of the processors 101 can be packaged together with the logic used for one or more controllers of the system control logic 106 to form a system in package (SiP). In another embodiment, at least one of the processors 101 can be further integrated onto the same chip as the logic used for one or more controllers of the system control logic 106 to form a system-on-chip (SoC).

[0095] As can be understood, the structure of the electronic device 100 shown in Figure 6 is only one example, and in other embodiments, the electronic device 100 may include more or fewer components than those shown, or combinations of some components, or divisions of some components, or various arrangements of components. The illustrated components can be implemented as hardware, software, or a combination of software and hardware, and there are no limitations thereto.

[0096] The embodiments of this application further provide a chip that includes, for example, a noise reduction device.

[0097] In the embodiments of this application, a computer program product for realizing the noise reduction method provided in each of the above embodiments is also provided.

[0098] Each embodiment of the mechanism disclosed herein can be implemented in hardware, software, firmware, or a combination thereof. The embodiments of the herein can be implemented by computer modules or module code running on a programmable system, the programmable system comprising at least one processor, a storage system (including volatile and non-volatile memory and / or memory elements), at least one input device, and at least one output device.

[0099] Module codes can be applied to input commands to execute the functions described in this application and generate output information. The output information can be applied to one or more output devices in known ways. For the purposes of this application, the processing system includes any system having a processor of the type such as a digital signal processor (DSP), a microcontroller, an application-specific integrated circuit (ASIC), or a microprocessor.

[0100] Module code can be implemented using a high-level module language or an object-oriented programming language to communicate with the processing system. If necessary, module code can also be implemented using assembly language or machine code. In practice, the mechanism described herein is not limited to any particular programming language. In any case, the language may be a compiled language or an interpreted language.

[0101] In some cases, the disclosed embodiments can be implemented by hardware, firmware, software, or a combination thereof. The disclosed embodiments can further be implemented by being stored in or on one or more transient or non-transient machine-readable (e.g., computer-readable) storage media, or in commands thereon, which can be read and executed by one or more processors. For example, the commands can be distributed via a network or other computer-readable media. Therefore, machine-readable media may include, but are not limited to, any mechanism for storing or transmitting information in a machine (e.g., computer)-readable format, such as floppy disks, compact disks, optical disks, CD-ROMs, magneto-optical disks, read-only memory (ROM), random access memory (RAM), EPROM (erasable programmable read-only memory), EEPROM (electrically erasable programmable read-only memory), magnetic or optical cards, flash memory, or tangible machine-readable memory for transmitting information via the Internet in the form of electrical, optical, audio or other propagating signals (e.g., carrier waves, infrared signals, digital signals, etc.). Therefore, machine-readable media may include any type of machine-readable media suitable for storing or transmitting electronic commands or information in a machine (e.g., computer)-readable format.

[0102] In the figures, certain structural or method features may be shown in a specific arrangement and / or order. However, it should be understood that such a specific arrangement and / or order may not always be necessary. Rather, in some embodiments, these features may be arranged in a different manner and / or order than those shown in the explanatory figures. Furthermore, the inclusion of structural or method features in a particular figure does not imply that such features are required in all embodiments, and in some embodiments, these features may be omitted or combined with other features. Any reference to “one embodiment” or “an embodiment” in the specification means that the specific features, structures, or characteristics described in combination with the embodiment are included in at least one exemplary embodiment or technique disclosed based on the embodiments of this application. The phrase “in one embodiment” appearing in various parts of the specification does not necessarily refer to the same embodiment.

[0103] The present invention further relates to an operating device for execution in the specification. Such device may include a general-purpose computer that is specifically constructed for a desired purpose or selectively activated or reconfigured by a computer program stored in the computer. Such computer programs may be stored on a computer-readable medium, including, but not limited to, floppy disks, compact disks, CD-ROMs, magneto-optical disks, read-only memory (ROM), random access memory (RAM), EPROMs, EEPROMs, magnetic or optical cards, application-specific integrated circuits (ASICs), or any type of disk suitable for storing electronic commands, each of which may be coupled to a system bus of the computer. Furthermore, the computers presented in the specification may have a single processor or may employ a configuration involving multiple processors for increasing computing power.

[0104] To further clarify, in the embodiments of this application, the numbering of the steps in the methods and flows is for the convenience of citation and does not restrict the order in which they are performed. If there is a specific order between the steps, the textual description shall be the standard.

[0105] Furthermore, the language used herein has been selected primarily for readability and teaching purposes, and may not be selected to describe or limit the disclosed subject matter. Therefore, the disclosure of embodiments herein is intended for illustrative purposes only and does not limit the scope of the concepts discussed herein.

Claims

1. A noise reduction method applicable to a noise reduction device, To collect noise data and elevator operation data during elevator operation, Based on the noise type of the aforementioned noise data, a corresponding noise reduction processing method is selected. Based on the noise reduction processing method, noise reduction processing is performed on the noise data to obtain a noise reduction signal. Based on the noise reduction position within the elevator and the position of the noise reduction device, the output delay time of the noise reduction signal is calculated. Based on the aforementioned delay time, the noise reduction signal is output, Includes, The noise type includes air noise data and structural noise data. The noise reduction device includes a first noise collection unit and a second noise collection unit. The first noise collection unit is used to collect air noise data during elevator operation. The second noise collection unit is used to collect structural noise data during elevator operation. Selecting a corresponding noise reduction processing method based on the noise type of the aforementioned noise data involves, In cases where the noise type of the noise data is air noise data, the noise reduction processing method to be selected is a first noise reduction processing method used to perform noise reduction processing on the air noise data. In response to the case where the noise type of the noise data is the structural noise data, the selected noise reduction processing method is a second noise reduction processing method used to perform noise reduction processing on the structural noise data when the structural noise data satisfies the noise reduction conditions, and not to perform noise reduction processing on the structural noise data when the structural noise data does not satisfy the noise reduction conditions. Includes, The noise reduction signal includes a Class 1 noise reduction signal corresponding to the air noise data. Based on the noise reduction processing method described above, in order to perform noise reduction processing on the noise data and obtain a noise-reduced signal, Based on the first noise reduction processing method, filtering is performed on the air noise data in the noise data to obtain the first target noise data. The first target noise data is subjected to a Fast Fourier Transform to calculate the center frequency of the first target noise data, and the first frequency band of the first target noise data is determined based on the center frequency. Based on the first frequency band, the median value of the first frequency band is calculated to obtain the first centroid frequency, Using a noise reduction algorithm, the first centroid frequency is combined to obtain the first type of noise reduction signal, Based on the second noise reduction processing method, the elevator operation data and the structural noise data in the noise data are subjected to average value calculation or fuzzy processing to obtain effective structural noise data. The effective structural noise data is subjected to filtering to obtain second target noise data. To calculate the effective value of the second target noise data, This includes processing the second target noise data based on the valid values ​​of the second target noise data, The noise reduction signal includes a second-class noise reduction signal corresponding to the structural noise data. Processing the second target noise data based on the valid values ​​of the second target noise data means that The noise reduction condition, which includes the fact that the effective value of the second target noise data is greater than or equal to a preset threshold, is determined to be satisfied by the second target noise data. The process involves performing a Fast Fourier Transform on the second target noise data to calculate the center frequency of the second target noise data, and determining the second frequency band of the second target noise data based on the center frequency. Based on the second frequency band, the second median of the second frequency band is calculated to obtain the second centroid frequency, This includes using a noise reduction algorithm to combine the second centroid frequencies and obtain the second type of noise reduction signal, The noise reduction device comprises a first noise reduction signal output unit and a second noise reduction signal output unit. The first noise reduction signal output unit is used to output the first type noise reduction signal corresponding to the air noise data. The second noise reduction signal output unit is used to output the second type noise reduction signal corresponding to the structural noise data. The first noise acquisition unit comprises at least one reference microphone, and the second noise acquisition unit comprises at least one vibration sensor. The method is characterized in that the first noise reduction signal output unit comprises at least one diaphragm speaker, and the second noise reduction signal output unit comprises at least one vibration speaker.

2. The method according to Claim 1, characterized in that the elevator operation data includes at least one of a preset elevator operating speed, a preset acceleration, and preset vibration data generated during operation in each structure of the elevator.

3. In order to calculate the delay time based on the noise reduction position and the position of the noise reduction device, When the noise reduction device is one of a plurality of noise reduction devices, the distance between the noise reduction position and the noise reduction device is determined, and the delay time at which the noise reduction device outputs the noise reduction signal is calculated. The method according to claim 1, characterized in that, among the plurality of noise reduction devices, the delay time of the noise reduction device furthest from the noise reduction position is shorter than the delay time of the noise reduction device closest to the noise reduction position.

4. The method according to claim 1, characterized in that the filtering process for the air noise data and the filtering process for the effective structural noise data each include at least one of high-pass filtering, band-pass filtering, and low-pass filtering.

5. The method according to claim 1, characterized in that the noise reduction algorithm used to obtain the Class 1 noise reduction signal is an FxLMS algorithm.

6. A noise reduction device comprising a data acquisition module, a data processing module, and a signal output module, The aforementioned data acquisition module is used to collect noise data and elevator operation data during elevator operation. The data processing module is used to select a corresponding noise reduction processing method based on the noise type of the noise data, and to perform noise reduction processing on the noise data based on the noise reduction processing method to obtain a noise reduction signal. The data processing module is further used to calculate the output delay time of the noise reduction signal based on the noise reduction position within the elevator and the position of the noise reduction device. The signal output module is used to output the noise reduction signal based on the delay time. The noise type includes air noise data and structural noise data. The data acquisition module includes a first noise acquisition unit and a second noise acquisition unit. The first noise collection unit is used to collect air noise data during elevator operation. The second noise collection unit is used to collect structural noise data during elevator operation. Selecting a corresponding noise reduction processing method based on the noise type of the aforementioned noise data involves, In cases where the noise type of the noise data is air noise data, the noise reduction processing method to be selected is a first noise reduction processing method used to perform noise reduction processing on the air noise data. In response to the case where the noise type of the noise data is the structural noise data, the selected noise reduction processing method is a second noise reduction processing method used to perform noise reduction processing on the structural noise data when the structural noise data satisfies the noise reduction conditions, and not to perform noise reduction processing on the structural noise data when the structural noise data does not satisfy the noise reduction conditions. Includes, The noise reduction signal includes a Class 1 noise reduction signal corresponding to the air noise data. Based on the noise reduction processing method described above, in order to perform noise reduction processing on the noise data and obtain a noise-reduced signal, Based on the first noise reduction processing method, filtering is performed on the air noise data in the noise data to obtain the first target noise data. The first target noise data is subjected to a Fast Fourier Transform to calculate the center frequency of the first target noise data, and the first frequency band of the first target noise data is determined based on the center frequency. Based on the first frequency band, the median value of the first frequency band is calculated to obtain the first centroid frequency, Using a noise reduction algorithm, the first centroid frequency is combined to obtain the first type of noise reduction signal, Based on the second noise reduction processing method, the elevator operation data and the structural noise data in the noise data are subjected to average value calculation or fuzzy processing to obtain effective structural noise data. The effective structural noise data is subjected to filtering to obtain second target noise data. To calculate the effective value of the second target noise data, This includes processing the second target noise data based on the valid values ​​of the second target noise data, The noise reduction signal includes a second-class noise reduction signal corresponding to the structural noise data. Processing the second target noise data based on the valid values ​​of the second target noise data means that The noise reduction condition, which includes the fact that the effective value of the second target noise data is greater than or equal to a preset threshold, is determined to be satisfied by the second target noise data. The process involves performing a Fast Fourier Transform on the second target noise data to calculate the center frequency of the second target noise data, and determining the second frequency band of the second target noise data based on the center frequency. Based on the second frequency band, the second median of the second frequency band is calculated to obtain the second centroid frequency, This includes using a noise reduction algorithm to combine the second centroid frequencies and obtain the second type of noise reduction signal, The signal output module comprises a first noise reduction signal output unit and a second noise reduction signal output unit. The first noise reduction signal output unit is used to output the first type noise reduction signal corresponding to the air noise data. The second noise reduction signal output unit is used to output the second type noise reduction signal corresponding to the structural noise data. The first noise acquisition unit comprises at least one reference microphone, and the second noise acquisition unit comprises at least one vibration sensor. The apparatus is characterized in that the first noise reduction signal output unit comprises at least one diaphragm speaker, and the second noise reduction signal output unit comprises at least one vibration speaker.

7. The noise reduction device is one of a plurality of noise reduction devices attached to the elevator, The apparatus according to claim 6, characterized in that the noise reduction device is any one of the plurality of noise reduction devices, or the noise reduction device is the one among the plurality of noise reduction devices that has the highest degree of idle computing power.

8. The apparatus further comprises an intelligent selection module, The apparatus according to claim 6, characterized in that the intelligent selection module is used to acquire the degree of idle state of the computing power of each noise reduction device in a plurality of noise reduction devices, and to determine that the noise reduction device has the highest degree of idle state of computing power among the plurality of noise reduction devices.

9. In calculating the output delay time of the noise reduction signal based on the noise reduction position in the elevator and the position of the noise reduction device, The noise reduction device collects each centroid frequency obtained from the noise data processing of each of the plurality of noise reduction devices, Using a noise reduction algorithm, the respective centroid frequencies are combined to calculate noise reduction signals corresponding to each of the multiple noise reduction devices, and The apparatus according to claim 8, further comprising calculating the delay time of the noise reduction signal output by each noise reduction device in the plurality of noise reduction devices based on the noise reduction position and the position of each noise reduction device in the plurality of noise reduction devices.

10. The apparatus according to claim 6, characterized in that the external shape of the noise reduction device is that of a thin plate.

11. The apparatus according to claim 6, characterized in that the location of the noise reduction device includes at least one of the inner wall surface of the elevator car and the outer surface of the elevator shaft wall.

12. An electronic device comprising one or more processors, one or more memories, and one or more programs stored in the one or more memories, wherein the one or more programs, when executed by the one or more processors, cause the electronic device to execute the noise reduction method described in Claim 1.

13. A computer-readable medium in which a command is stored, and when the command is executed on a computer, the computer is caused to execute the noise reduction method described in Claim 1.

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