Elevator car noise suppression device

Abstract

The invention discloses a noise suppression device for an elevator car. The noise suppression device comprises a microphone array, a loudspeaker system, a mobile platform and a digital signal processor, the microphone array captures noise in the lift car; the loudspeaker system plays anti-phase sound waves in the lift car; the mobile platform drives the elevator car noise suppression device to move in a car or among different cars; and the digital signal processor analyzes the noise captured by the microphone array in the lift car and generates reverse sound waves. According to the active noise reduction technology, noise can be recognized and offset more accurately, and therefore the overall noise reduction efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of elevators, and particularly to an elevator car noise suppression device. Background Art

[0002] There are various noises during the operation of an elevator, which affect the riding experience of passengers. Existing technologies reduce the noise during elevator operation through passive noise control methods, such as sound insulation materials and sound absorption structures. These methods have limited sound insulation effects for dynamically changing noises. Therefore, how to suppress the noise inside the car according to the real-time change of the noise inside the car is a technical problem currently faced. Summary of the Invention

[0003] To solve the above technical problem, the present invention provides an elevator car noise suppression device, including a microphone array, a speaker system, a moving platform, and a digital signal processor;

[0004] The microphone array captures the noise inside the car; the speaker system plays anti-phase sound waves inside the car; the moving platform drives the elevator car noise suppression device to move inside the car or between different cars; the digital signal processor analyzes the noise inside the car captured by the microphone array and generates anti-phase sound waves.

[0005] Preferably, the microphone array is formed by multiple microphones to form a multi-channel array, which captures noise signals in multiple directions and frequencies.

[0006] Preferably, the digital signal processor uses the fast Fourier transform to identify the frequency components of the noise.

[0007] Preferably, the digital signal processor uses the short-time Fourier transform or wavelet transform to analyze the time-varying characteristics of the noise.

[0008] Preferably, the digital signal processor learns an active noise control parameter adjustment strategy according to historical noise data and noise reduction effects; the active noise control parameters include at least one or more of the following parameters: filter coefficients, microphone sensitivity, feedforward digital gain, feedback digital gain, controller gain, phase and amplitude compensation, proportional band, overshoot, settling time, environmental noise compensation.

[0009] Preferably, the fast Fourier transform is a split-radix Fourier transform or a Winograd Fourier transform.

[0010] Preferably, the digital signal processor has a multi-core architecture and performs parallel processing on the fast Fourier transform.

[0011] Preferably, the elevator car noise suppression device further includes a central control system; the central control system simultaneously receives noise data from multiple cars and dynamically assigns priorities to each car according to the noise intensity, change rate, and duration.

[0012] Preferably, the central control system receives external passenger feedback and adjusts the car priority according to the feedback.

[0013] Preferably, the central control system plans the movement path of the mobile platform according to the car priority.

[0014] Compared with the prior art, the active noise reduction technology of the present invention can more accurately identify and cancel noise, thereby improving the overall noise reduction efficiency. Detailed implementation mode

[0017] The following specific embodiments illustrate the implementation manners of the present invention. Those skilled in the art can fully understand other advantages and technical effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through different specific implementation manners. The details in this specification can also be applied based on different viewpoints, and various modifications or changes can be made without departing from the overall design concept of the invention. It should be noted that, without conflict, the following embodiments and the features in the embodiments can be combined with each other. The following exemplary embodiments of the present invention can be implemented in many different forms and should not be construed as being limited only to the specific embodiments described herein. It should be understood that these embodiments are provided to make the disclosure of the present invention complete and thorough, and to fully convey the technical solutions of these exemplary specific embodiments to those skilled in the art.

[0018] Embodiment 1

[0019] This embodiment provides an elevator car noise suppression device, including a microphone array, a speaker system, a mobile platform, and a digital signal processor; the microphone array captures the noise inside the car; the speaker system plays anti-phase sound waves inside the car; the mobile platform is based on a wheeled or rail-mounted design and drives the elevator car noise suppression device to move inside the car or between different cars; the digital signal processor analyzes the noise inside the car captured by the microphone array and generates anti-phase sound waves.

[0020] Preferably, one implementation of the elevator car noise suppression device is integrated on a mobile robot. The robot also includes: a sensor system, including sensors such as temperature and humidity, air pressure, and light, for environmental monitoring; a control system, integrating autonomous mobile functions such as navigation, obstacle avoidance, and task scheduling; a user interface, having a touch screen or a voice interaction system, providing interaction with passengers; a remote monitoring and diagnosis system, allowing remote monitoring of the robot status, for fault diagnosis and maintenance; a power system, containing a rechargeable battery, supporting long-term operation of the robot; a safety system, including an emergency stop button and a collision detection sensor, ensuring passenger safety.

[0021] The microphone array is formed by multiple microphones to form a multi-channel array, capturing noise signals in multiple directions and frequencies. Through beamforming technology, the microphone array can focus on the noise source, improving the signal-to-noise ratio of the noise signal.

[0022] The digital signal processor performs real-time spectral analysis on the noise, using the fast Fourier transform (FFT) to perform spectral analysis on the noise signal and identify the main noise frequency components. At the same time, the short-time Fourier transform (STFT) or wavelet transform is introduced to analyze the time-varying characteristics of the noise.

[0023] At the same time, parameter optimization based on machine learning: Machine learning algorithms (such as support vector machines, neural networks) are used to classify and predict the noise data. The algorithm learns the optimal active noise control (ANC) parameter adjustment strategy according to the historical noise data and the noise reduction effect. For example, by training a neural network model, predict the optimal cut-off frequency and gain coefficient of the filter at different noise frequencies and intensities.

[0024] The active noise control parameters include at least one or more of the following parameters: filter coefficients, microphone sensitivity, feedforward digital gain, feedback digital gain, controller gain, phase and amplitude compensation, proportional band, overshoot, settling time, environmental noise compensation.

[0025] The digital signal processor uses an adaptive filter (such as the LMS algorithm or the RLS algorithm) to dynamically adjust the filter parameters. The filter automatically updates its weights according to the statistical characteristics of the real-time noise signal to minimize the error signal. The noise in the environment after noise reduction is continuously monitored through the microphone array, and the error between the actual noise reduction effect and the target noise reduction effect is calculated. If the error exceeds the preset threshold, the algorithm automatically adjusts the ANC system parameters for fine-tuning until the error is minimized. Multiple monitoring points are set in the car, and the noise reduction effect is monitored simultaneously through multiple microphones. According to the multi-point feedback data, the optimization algorithm comprehensively adjusts the global parameters of the ANC system to ensure uniform noise reduction effects in all areas of the car.

[0026] The elevator car noise suppression device in this embodiment adopts active noise reduction technology, which can dynamically adjust the anti-phase sound wave according to the real-time change of noise, and effectively suppress noises of various frequencies and intensities. Compared with passive methods, active noise reduction technology can more accurately identify and cancel noises, thereby improving the overall noise reduction efficiency. By reducing the noise level in the car, the riding experience of passengers will be significantly improved, and discomfort and irritability will be reduced.

[0027] Embodiment 2

[0028] This embodiment further explains the Adaptive Fast Noise Cancellation Algorithm (AFNCA) of the digital signal processor on the basis of Embodiment 1.

[0029] The algorithm optimizes the Fast Fourier Transform (FFT), adopts improved FFT algorithms such as split-radix FFT or Winograd FFT, reduces the computational complexity, and at the same time improves the spectral resolution of the noise signal.

[0030] The digital signal processor DSP has a multi-core architecture and performs parallel processing on the fast Fourier transform to further improve the calculation speed.

[0031] The adaptive fast noise cancellation algorithm also performs dynamic phase compensation and amplitude adaptive adjustment. A dynamic phase compensation mechanism is introduced. By real-time monitoring the phase change of the noise signal, the phase of the anti-phase sound wave is dynamically adjusted to ensure complete alignment with the noise signal. According to the change of the noise intensity, the amplitude of the anti-phase sound wave is adaptively adjusted. For example, the adaptive gain control (AGC) algorithm is adopted to ensure that the amplitude of the anti-phase sound wave always matches the noise signal.

[0032] The adaptive fast noise cancellation algorithm also performs real-time feedback correction. The ambient noise after noise reduction is continuously monitored through a microphone array, and the error between the actual noise reduction effect and the target noise reduction effect is calculated. If the error exceeds the preset threshold, the algorithm automatically adjusts the parameters of the anti-phase sound wave for correction. An adaptive filter (such as the LMS algorithm or the RLS algorithm) is used to dynamically adjust the spectral characteristics of the anti-phase sound wave to ensure that the anti-phase sound wave can accurately cancel the noise signal.

[0033] Through the optimized algorithm in this embodiment, the anti-phase sound wave can be generated in an extremely short time to ensure the real-time performance of noise reduction. Through dynamic phase compensation and real-time feedback correction, the algorithm can accurately cancel noises of various frequencies and intensities.

[0034] Embodiment 3

[0035] This embodiment adds a central control system on the basis of Embodiment 1; the central control system simultaneously receives noise data from multiple carriages and dynamically assigns priorities to each carriage according to the noise intensity, change rate, and duration.

[0036] The robot monitors the noise level inside the carriage in real time through a microphone array and uploads the data to the central control system. The central control system simultaneously receives noise data from multiple carriages. The collected noise data is preprocessed, including filtering, denoising, and feature extraction. Through the feature extraction algorithm, key features such as the frequency, intensity, and change rate of the noise are extracted.

[0037] The assignment of priorities considers dynamic priority assessment and passenger demand priorities. According to the noise intensity, change rate, and duration, priorities are dynamically assigned to each carriage. The carriage with higher noise intensity, faster change rate, and longer duration has a higher priority. Combining passenger feedback and demands, the priorities are further adjusted. For example, if there is a passenger complaint about excessive noise in a certain carriage, the priority of that carriage will be increased.

[0038] At the same time, the central control system plans the movement path of the robot according to the carriage priority. Based on the priority, the intelligent scheduling system plans the optimal path for the robot. Using the A algorithm or Dijkstra algorithm, combined with the current position and movement time of the robot, the shortest path to the high-priority carriage is calculated. In a multi-robot system, the task assignment and path planning of multiple robots are coordinated through a distributed scheduling algorithm. For example, a master-slave or peer-to-peer communication architecture is adopted to avoid conflicts between robots and improve the overall noise reduction efficiency.

[0039] During the movement of the robot, if it detects a change in the noise priority of other carriages, the intelligent scheduling system adjusts the task plan of the robot in real time. For example, if a sudden high-noise event occurs in a certain carriage, the system immediately replans the path and preferentially schedules the robot to go to that carriage.

[0040] Through machine learning algorithms, the intelligent scheduling system can learn historical noise data and task scheduling results, optimize the priority assignment and path planning strategies, and improve the scheduling efficiency.

[0041] This embodiment controls the movement of the robot between different carriages through intelligent scheduling and preferentially processes the carriages with excessive noise. It enables the robot to automatically adjust the work plan according to the noise situation, preferentially handle the noise problem, and improve the management efficiency.

[0042] The above has described the present invention in detail through specific embodiments and examples, but these do not constitute a limitation to the present invention. Without departing from the principle of the present invention, those skilled in the art can also make many deformations and improvements, which should also be regarded as the protection scope of the present invention.

Claims

1. An elevator car noise suppression device, characterized in that, It includes a microphone array, a speaker system, a mobile platform, and a digital signal processor; The microphone array captures the noise inside the car; the speaker system plays anti-sound waves inside the car; the mobile platform drives the elevator car noise suppression device to move inside the car or between different cars; the digital signal processor analyzes the noise inside the car captured by the microphone array and generates anti-sound waves.

2. The elevator car noise suppression device according to claim 1, characterized in that The microphone array is formed by multiple microphones to form a multi-channel array, capturing noise signals in multiple directions and frequencies.

3. The elevator car noise suppression device according to claim 2, characterized in that, The digital signal processor uses the fast Fourier transform to identify the frequency components of the noise.

4. The elevator car noise suppression device according to claim 2, characterized in that, The digital signal processor uses the short-time Fourier transform or wavelet transform to analyze the time-varying characteristics of the noise.

5. The elevator car noise suppression device according to claim 2, wherein The digital signal processor learns the active noise control parameter adjustment strategy according to historical noise data and noise reduction effect; the active noise control parameters include at least one or more of the following parameters: filter coefficient, microphone sensitivity, feedforward digital gain, feedback digital gain, controller gain, phase and amplitude compensation, proportional band, overshoot, settling time, ambient noise compensation.

6. The elevator car noise suppression device according to claim 3, characterized in that, The fast Fourier transform is a split-radix Fourier transform or a Winograd Fourier transform.

7. The elevator car noise suppression device according to claim 6, characterized in that, The digital signal processor has a multi-core architecture and performs parallel processing on the fast Fourier transform.

8. The elevator car noise suppression device according to claim 1, wherein The elevator car noise suppression device further includes a central control system; the central control system simultaneously receives noise data from multiple cars and dynamically assigns priorities to each car according to the noise intensity, change rate, and duration.

9. The elevator car noise suppression device according to claim 8, wherein The central control system receives external passenger feedback and adjusts the car priority according to the feedback.

10. The elevator car noise suppression device according to claim 8, wherein, The central control system plans the movement path of the mobile platform according to the car priority.