Software noise reduction system and method based on environment noise and time period setting

Abstract

The invention discloses a software noise reduction system and method based on environment noise and time period setting, and relates to the technical field of charging piles, and the system comprises a noise collection module which is used for collecting environment noise and noise generated by the charging piles; the data processing module is responsible for performing feature extraction and classification on the collected noise data; and the control decision module comprises a dynamic adjustment algorithm, and determines the output power of the charging pile and the rotating speed of the fan according to the set maximum allowable noise values in different time periods. According to the invention, environmental noise is collected, the environmental noise and equipment noise are separately collected, noise allowable values in different time periods are set by means of software, a three-dimensional mapping relation and cooperative control among power, rotating speed and noise are established, and power regulation and fan rotating speed can be timely fed back through real-time environmental noise. And the purpose of reducing noise is achieved. And the device has important practical significance on the transformation of the existing equipment.

Description

Technical Field

[0001] This invention relates to the field of charging pile technology, specifically a software noise reduction system and method based on environmental noise and time period settings. Background Technology

[0002] With the rapid development of the electric vehicle industry, charging stations, as a key infrastructure, are becoming increasingly powerful and are being installed more and more frequently in densely populated areas such as residential areas. However, the noise generated by high-power charging stations is gradually becoming a significant factor affecting the quality of the surrounding environment. Especially at night or during quiet periods, the noise pollution caused by the charging station's cooling fans running at full speed is particularly prominent, seriously impacting the quality of life for nearby residents.

[0003] Most charging stations on the market currently employ fixed-speed or simple temperature control strategies to manage the operation of their cooling fans. These traditional methods have significant limitations: firstly, during high-power charging, the fans inevitably generate high noise levels as they typically operate at maximum speed; secondly, these fixed power limits and temperature control strategies cannot be dynamically adjusted according to different time periods and environmental conditions. For example, in residential areas where lower noise levels are required at night, existing solutions often fail to meet noise reduction needs. Furthermore, traditional control methods do not incorporate real-time environmental noise data for optimization, making it difficult to achieve an ideal balance between energy efficiency and noise control.

[0004] Existing charging pile cooling fans typically employ fixed speed or simple temperature control strategies, which present the following problems: When charging at high power, the fan runs at full speed, generating noise pollution and affecting the surrounding environment. Fixed power limiting strategies cannot meet the noise reduction needs of different scenarios (such as the need for lower noise levels in residential areas at night). The failure to dynamically adjust operating parameters in conjunction with environmental noise led to an imbalance between energy efficiency and noise control.

[0005] Therefore, this invention proposes a software noise reduction system and method based on environmental noise and time period settings. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and provide a software noise reduction system and method based on environmental noise and time period settings. It collects environmental noise and charging pile noise through a distributed noise sensor array, and processes the collected data using Fast Fourier Transform (FFT) combined with machine learning algorithms, thereby achieving effective separation of environmental noise and equipment noise.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a software noise reduction system based on environmental noise and time period settings, characterized in that it includes: Noise acquisition module: used to collect ambient noise and noise generated by the charging pile itself; Embedded devices installed inside the charging pile: The embedded devices include a data processing module and a control decision module. The data processing module is responsible for feature extraction and classification of the collected noise data. The control decision module contains a dynamic adjustment algorithm that determines the output power and fan speed of the charging pile based on the maximum allowable noise value set for different time periods. The execution module includes a power regulation unit and a variable frequency fan drive unit, which are used to adjust the working status of the charging pile according to the instructions issued by the control decision module.

[0008] Preferably, the noise acquisition module is a distributed noise sensor array.

[0009] Preferably, the data processing module uses a fast Fourier transform combined with a machine learning algorithm to distinguish and separate environmental noise from charging pile noise.

[0010] Preferably, the control decision module further includes a feedback dynamic adjustment mechanism that can adjust the charging power and heat dissipation strategy in reverse based on real-time noise detection results.

[0011] Preferably, the control decision module also includes a self-learning mechanism that can optimize the noise control strategy based on historical data, thereby achieving more accurate noise management.

[0012] Preferably, the system also includes a user interface that allows operators to manually set or adjust the maximum permissible noise value for different time periods, as well as view the system's operating status and noise level.

[0013] A method for using a software noise reduction system based on environmental noise and time period settings, characterized by the following steps: Step S1: Collect ambient noise and the charging pile's own noise through a distributed noise sensor array; Step S2: Use FFT (Fast Fourier Transform) combined with machine learning algorithms to distinguish and separate environmental noise from charging pile noise; Step S3: Establish a time period-maximum noise comparison table and determine the corresponding maximum allowable noise value based on the current time; Step S4: Calculate the required output power and fan speed based on the set noise threshold and the real-time noise data; Step S5: Perform the corresponding power adjustment and fan speed adjustment to ensure that the total noise does not exceed the preset threshold.

[0014] The specific algorithm in step S4 is as follows: The noise level is set to Nset, the current ambient noise is Nenv, the charging pile noise is Ncharge, and the total noise Ntotal is calculated based on the noise superposition principle. The output power P and the fan speed S are functions of Nset. When Ntotal ≤ Nset, the output power and fan speed are determined according to a preset mapping relationship. For example, a two-dimensional table is created, with the horizontal axis representing the noise range and the vertical axis representing the corresponding values ​​of output power and fan speed. Assuming the noise range is divided into [0,40], (40,60], (60,80], etc., when Nset is in the range [0,40], the output power P is P1 and the fan speed S is S1; when Nset is in the range (40,60], the output power P is P2 and the fan speed S is S2 (P2>P1, S2>S1).

[0015] Step S5 also includes: if the real-time ambient noise exceeds a set threshold, the output power and / or fan speed are appropriately increased to adapt to the higher ambient noise level.

[0016] The collected ambient noise is compared with the set noise level. When Nenv > Nset, it indicates that the ambient noise is relatively high. In this case, the output power and fan speed can be appropriately increased. The increase can be determined based on the difference between Nenv and Nset, ΔN = Nenv - Nset. For example, when ΔN is in the range [0,10], the output power is increased by ΔP1 and the fan speed is increased by ΔS1; when ΔN is in the range (10,20], the output power is increased by ΔP2 and the fan speed is increased by ΔS2 (ΔP2 > ΔP1, ΔS2 > ΔS1).

[0017] Beneficial effects: Compared with existing technologies, this software noise reduction system and method based on environmental noise and time period settings has the following advantages: Reduce noise pollution: By precisely controlling the output power and fan speed through software, the noise generated during the operation of the charging pile is effectively reduced, minimizing the impact on the surrounding environment and residents; Improve charging efficiency: When ambient noise levels permit, appropriately increase the output power to ensure the charging efficiency of the charging station; Flexibility and customizability: The maximum noise level can be flexibly set according to different time periods and environmental requirements to achieve personalized noise control; This invention collects ambient noise and separates it from equipment noise. Software is used to set permissible noise levels for different time periods. By establishing a three-dimensional mapping relationship between power, speed, and noise, coordinated control is achieved. Real-time ambient noise data provides timely feedback for power adjustment and fan speed control, ultimately reducing noise. This invention has significant practical implications for the retrofitting of existing equipment. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of the noise acquisition and charging pile control signal of the present invention; Figure 2 The flowchart of the noise data acquisition and time period setting algorithm of this invention is shown below; Figure 3 This is a flowchart of the noise comparison and control process of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Please see Figures 1-3 As shown, the present invention provides a technical solution: a software noise reduction system based on environmental noise and time period settings, comprising: Noise Acquisition Module: A distributed noise sensor array is deployed around the charging station to simultaneously collect ambient noise and noise generated by the charging station itself. The number of sensors is determined based on the specific application scenario; for example, densely populated residential areas may require more sensors to ensure the accuracy of noise data. In the design of the noise acquisition module, we selected high-sensitivity condenser microphones as the core sensors. These microphones have a wide frequency response range (20Hz to 20kHz), enabling them to accurately capture various noise components in and around the charging station. To ensure data accuracy, we placed multiple sensors at different locations on the charging station, forming a distributed array. The distance between each sensor is adjusted according to the size and installation location of the charging station, typically maintaining approximately 1 meter to ensure coverage of the entire working area.

[0022] Embedded devices (software) installed inside the charging pile: These embedded devices include a data processing module and a control decision module. The control decision module, located within the charging pile, is responsible for feature extraction and classification of the collected noise data. First, a Fast Fourier Transform (FFT) is used to convert the time-domain signal into a frequency-domain signal for better noise component analysis. Next, machine learning algorithms (such as Support Vector Machines (SVM) or Neural Networks (NN)) are applied to distinguish and separate ambient noise from charging pile noise. This process involves training a model to identify different types of noise features and establishing a noise classifier accordingly. The control decision module, also located within the embedded device, contains a dynamic adjustment algorithm. Based on the maximum permissible noise values ​​for different time periods (as shown in Table 1), it determines the charging pile's output power and fan speed. Furthermore, this module features a feedback dynamic adjustment mechanism and self-learning capabilities. The feedback dynamic adjustment mechanism monitors noise levels in real time and adjusts the charging power and heat dissipation strategy accordingly. The self-learning mechanism continuously optimizes the noise control strategy using historical data, improving the accuracy of noise management.

[0023] The Fast Fourier Transform (FFT) algorithm is used to convert audio signals in the time domain into a frequency domain representation for easier subsequent analysis. Specifically, the original audio signal is first segmented, with each segment approximately 256 or 512 points in length. Then, a window function (such as the Hanning window) is applied to reduce spectral leakage. Next, the FFT operation is performed to obtain the energy distribution of each frequency component.

[0024] For the machine learning algorithm, we employed a convolutional neural network (CNN)-based approach to distinguish between environmental noise and charging station noise. During training, we used a large dataset of labeled samples derived from actual tests and simulations. After training and cross-validation, the model achieved a classification accuracy of over 90%.

[0025] The core of the dynamic adjustment algorithm lies in establishing a mapping relationship between output power P, fan speed S, and the set maximum allowable noise value Nset. This mapping relationship is not fixed but dynamically adjusted with changes in time and environmental conditions. To this end, we introduce a feedback mechanism to monitor the total noise level Ntotal in real time and adjust the charging power and heat dissipation strategy accordingly. The self-learning mechanism continuously optimizes this process using historical data, finding the optimal parameter combination through a genetic algorithm.

[0026] The execution module includes a power regulation unit and a variable frequency fan drive unit, which are used to adjust the working state of the charging pile according to the instructions issued by the control decision module. For example, when the ambient noise is detected to exceed the preset threshold, the output power and / or fan speed are appropriately increased to adapt to the higher ambient noise level.

[0027] The software noise reduction system, which is based on ambient noise and time period settings, also includes a user interface that allows operators to manually set or adjust the maximum permissible noise value for different time periods, as well as view the system's operating status and noise level.

[0028] Please refer to the following carefully. Figure 1 The noise acquisition module is a distributed noise sensor array, and the data processing module uses fast Fourier transform combined with machine learning algorithms to distinguish and separate environmental noise from charging pile noise. The control decision module further includes a feedback dynamic adjustment mechanism, which can adjust the charging power and heat dissipation strategy in reverse based on real-time noise detection results. The control decision module also includes a self-learning mechanism, which can optimize the noise control strategy based on historical data, thereby achieving more accurate noise management.

[0029] A method for using a software noise reduction system based on ambient noise and time period settings includes the following steps: Step S1: Collect ambient noise and the charging pile's own noise through a distributed noise sensor array; Step S2: Use FFT (Fast Fourier Transform) combined with machine learning algorithms to distinguish and separate environmental noise from charging pile noise; Step S3: Establish a time period-maximum noise comparison table and determine the corresponding maximum allowable noise value based on the current time; Step S4: Calculate the required output power and fan speed based on the set noise threshold and the real-time noise data; Step S5: Perform the corresponding power adjustment and fan speed adjustment to ensure that the total noise does not exceed the preset threshold.

[0030] Time-period noise setting and matching algorithm: Based on the set noise level, different output powers and fan speeds are matched. The specific algorithm is as follows: The noise level is set to Nset, the current ambient noise is Nenv, the charging pile noise is Ncharge, and the total noise Ntotal is calculated based on the noise superposition principle. Output power P and fan speed S are functions of Nset. When Ntotal ≤ Nset, the output power and fan speed are determined according to a preset mapping relationship. For example, a two-dimensional table can be created, with the horizontal axis representing the noise range and the vertical axis representing the corresponding values ​​of output power and fan speed. Assuming the noise range is divided into [0,40], (40,60], (60,80], etc., when Nset is in the [0,40] range, the output power P is P1, and the fan speed S is S1; when Nset is in the (40,60] range, the output power P is P2, and the fan speed S is S2 (P2>P1, S2>S1).

[0031] Noise comparison and dynamic control algorithm: The charging pile algorithm compares the collected ambient noise with the set noise level in real time. When Nenv > Nset, it indicates that the ambient noise is relatively high, and the output power and fan speed can be appropriately increased. The increase can be determined based on the difference between Nenv and Nset, ΔN = Nenv - Nset. For example, when ΔN is in the range [0, 10], the output power is increased by ΔP1, and the fan speed is increased by ΔS1; when ΔN is in the range (10, 20], the output power is increased by ΔP2, and the fan speed is increased by ΔS2 (ΔP2 > ΔP1, ΔS2 > ΔS1).

[0032] Please refer to the table below for an example of the time period-maximum noise comparison established in step S3:

[0033] Under laboratory conditions, we built a test platform simulating the working environment of a charging pile, including a 50kW charging pile and a corresponding cooling system. We observed the system response by varying the input power and the intensity of external noise sources. The results show that under all test conditions, the system effectively controlled the total noise level to remain below the preset threshold, and in most cases, it even maintained a certain margin.

[0034] During the field trial phase, three different types of locations were selected: a residential area, a commercial area, and an industrial park, and continuous monitoring was conducted for three months in each location. Data analysis showed that the system's noise control effect was very ideal both day and night, and user satisfaction scores also showed a significant improvement compared to the past.

[0035] This system collects ambient noise and charging pile noise from a distributed noise sensor array. It then processes the collected data using Fast Fourier Transform (FFT) combined with machine learning algorithms to effectively separate ambient noise from equipment noise. Furthermore, the system's control decision module automatically adjusts the charging pile's output power and fan speed based on preset maximum permissible noise values ​​for different time periods, ensuring that the total noise does not exceed a set threshold. Simultaneously, the system possesses a feedback dynamic adjustment mechanism and self-learning capabilities, continuously optimizing noise control strategies based on historical data to achieve more precise and personalized noise management. This innovation not only helps reduce the impact of charging piles on the surrounding environment but also provides a feasible technical path for upgrading existing equipment.

[0036] This patent uses environmental noise collection and software settings to form a power control algorithm. The software algorithm limits the power of the charging pile and adjusts the fan speed to control noise, so that the noise of the charging pile meets the noise requirements within a set time period.

[0037] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0038] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A software noise reduction system based on ambient noise and time period settings, characterized in that: include: Noise acquisition module: used to collect ambient noise and noise generated by the charging pile itself; Embedded devices installed inside the charging pile: The embedded devices include a data processing module and a control decision module. The data processing module is responsible for feature extraction and classification of the collected noise data. The control decision module contains a dynamic adjustment algorithm that determines the output power and fan speed of the charging pile based on the maximum allowable noise value set for different time periods. The execution module includes a power regulation unit and a variable frequency fan drive unit, which are used to adjust the working status of the charging pile according to the instructions issued by the control decision module.

2. The software noise reduction system based on environmental noise and time period settings according to claim 1, characterized in that: The noise acquisition module is a distributed noise sensor array.

3. The software noise reduction system based on environmental noise and time period settings according to claim 1, characterized in that: The data processing module uses Fast Fourier Transform combined with machine learning algorithms to distinguish and separate environmental noise from charging pile noise.

4. The software noise reduction system based on environmental noise and time period settings according to claim 1, characterized in that: The control decision module further includes a feedback dynamic adjustment mechanism, which can adjust the charging power and heat dissipation strategy in reverse based on real-time noise detection results.

5. The software noise reduction system based on environmental noise and time period settings according to claim 1, characterized in that: The control decision module also includes a self-learning mechanism that can optimize noise control strategies based on historical data, thereby achieving more precise noise management.

6. The software noise reduction system based on environmental noise and time period settings according to claim 1, characterized in that: The system also includes a user interface that allows operators to manually set or adjust the maximum permissible noise value for different time periods, as well as view the system's operating status and noise level.

7. A method for using a software noise reduction system based on environmental noise and time period settings, characterized in that: Includes the following steps: Step S1: Collect ambient noise and the charging pile's own noise through a distributed noise sensor array; Step S2: Use Fast Fourier Transform combined with machine learning algorithms to distinguish and separate ambient noise from charging pile noise; Step S3: Establish a time period-maximum noise comparison table and determine the corresponding maximum allowable noise value based on the current time; Step S4: Calculate the required output power and fan speed based on the set noise threshold and the real-time noise data; Step S5: Perform the corresponding power adjustment and fan speed adjustment to ensure that the total noise does not exceed the preset threshold.

8. The method of using a software noise reduction system based on environmental noise and time period settings according to claim 1, characterized in that: The specific algorithm in step S4 is as follows: The noise level is set to Nset, the current ambient noise is Nenv, the charging pile noise is Ncharge, and the total noise Ntotal is calculated based on the noise superposition principle. The output power P and the fan speed S are functions of Nset. When Ntotal ≤ Nset, the output power and fan speed are determined according to a preset mapping relationship. For example, a two-dimensional table is created, with the horizontal axis representing the noise range and the vertical axis representing the corresponding values ​​of output power and fan speed. Assuming the noise range is divided into [0,40], (40,60], (60,80], etc., when Nset is in the range [0,40], the output power P is P1 and the fan speed S is S1; when Nset is in the range (40,60], the output power P is P2 and the fan speed S is S2 (P2>P1, S2>S1).

9. The method of using a software noise reduction system based on environmental noise and time period settings according to claim 1, characterized in that: Step S5 also includes: if the real-time ambient noise exceeds a set threshold, the output power and / or fan speed are appropriately increased to adapt to the higher ambient noise level.

10. The method of using a software noise reduction system based on environmental noise and time period settings according to claim 9, characterized in that: The collected ambient noise is compared with the set noise level. When Nenv > Nset, it indicates that the ambient noise is relatively high. In this case, the output power and fan speed can be appropriately increased. The increase can be determined based on the difference between Nenv and Nset, ΔN = Nenv - Nset. For example, when ΔN is in the range [0,10], the output power is increased by ΔP1 and the fan speed is increased by ΔS1; when ΔN is in the range (10,20], the output power is increased by ΔP2 and the fan speed is increased by ΔS2 (ΔP2 > ΔP1, ΔS2 > ΔS1).

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