Self-adaptive noise optimization control method and system of air cooling device

Abstract

The invention relates to the technical field of intelligent control, in particular to a self-adaptive noise optimization control method and system for an air cooling device, and the method comprises the steps: obtaining the working environment type and the current working load of the air cooling device, preliminarily estimating the noise level, and determining a noise optimization coefficient; initializing a noise control parameter based on the coefficient, and identifying a current ambient noise level with an ambient sensor; evaluating a noise interference coefficient in combination with the environment noise and the initialization parameters; establishing a dynamic noise control optimization model, analyzing the variation trend of the environment noise and the noise control parameter to the interference coefficient, and generating an optimal noise control parameter; and finally, fan operation parameters are optimized based on the optimal parameters, and real-time self-adaptive control of the noise is achieved. According to the method, through dynamic perception, intelligent optimization and accurate control, the problem that a traditional method cannot dynamically adjust noise control parameters according to the environmental noise level and the working load is solved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control technology, and in particular to an adaptive noise optimization control method and system for an air cooling device. Background Art

[0002] As a heat dissipation device widely used in industry, electronic equipment and daily life, the core function of air cooling devices is to achieve efficient heat dissipation through the operation of fans. However, air cooling devices inevitably generate noise when working. This noise not only interferes with the surrounding environment, but may also affect the user's comfort and work efficiency. Traditional noise control methods usually adopt fixed or static noise suppression strategies, such as reducing noise by reducing fan speed or adding sound insulation materials. However, these methods have obvious limitations: they cannot dynamically adjust noise control parameters according to changes in ambient noise levels and the actual workload of the air cooling device. Therefore, when the ambient noise is high or the workload is large, traditional methods often find it difficult to effectively reduce noise interference, resulting in the noise problem cannot be fundamentally solved.

[0003] The above content is only used to assist in understanding the technical solution of the present invention and does not constitute an admission that the above content is prior art. Summary of the Invention

[0004] The main purpose of the present invention is to provide an adaptive noise optimization control method and system for an air-cooling device, aiming to solve the technical problem that the current noise control method for an air-cooling device cannot dynamically adjust the noise control parameters according to the ambient noise level and workload, resulting in the inability to effectively solve the noise interference problem.

[0005] To achieve the above object, the present invention provides an adaptive noise optimization control method for an air cooling device, the method comprising: Obtain the working environment type and current workload of the air cooling device; Preliminarily estimating a noise level of the air cooling device according to the type of working environment, and determining a noise optimization coefficient of the air cooling device based on the current workload and the preliminarily estimated noise level; Initializing noise control parameters of the air cooling device based on the noise optimization coefficient of the air cooling device, and determining the current environmental noise level based on environmental sensor identification; evaluating a noise interference coefficient of the air cooling device according to the current ambient noise level and the noise control parameters of the initialized air cooling device; Establish a dynamic noise control optimization model to analyze the change trend of the noise interference coefficient of the air cooling device by the current ambient noise level and the noise control parameters of the initialized air cooling device, and generate the optimal noise control parameters; The fan operating parameters of the air cooling device are optimized and controlled based on the optimal noise control parameters.

[0006] Optionally, initializing the noise control parameters of the air cooling device based on the noise optimization coefficient of the air cooling device, and determining the current environmental noise level based on environmental sensor identification includes: According to the working environment type of the air cooling device, several noise characteristics of the working environment type are obtained, and a noise control range corresponding to the noise characteristics of the working environment type is determined, wherein the noise characteristics of the working environment type include: environmental noise type, noise intensity, and noise frequency; Using a noise adjustment algorithm, the noise control range corresponding to the noise characteristics of the working environment type is corrected based on the noise optimization coefficient of the air cooling device, the noise control parameters of the air cooling device are initialized, and the current environmental noise level is determined based on environmental sensor identification.

[0007] Optionally, the noise adjustment algorithm is expressed as:

[0008] Where, To initialize the noise control parameters of the air cooling device, is the maximum noise control range corresponding to the noise characteristics of the working environment type, is the minimum noise control range corresponding to the noise characteristics of the working environment type, The noise optimization factor for air-cooled devices.

[0009] Optionally, the evaluating the noise interference coefficient of the air cooling device according to the current ambient noise level and the noise control parameter of the initialized air cooling device includes: According to the interference risk change trend when the current environmental noise level affects the control of multiple noise characteristics of the air cooling device, an interference risk score is assigned to each noise characteristic to obtain a noise characteristic interference risk score; The noise feature interference risk score is standardized through a standardized formula; According to the standardized noise feature interference risk score, the relative importance of each noise feature interference risk score to the noise interference during control is determined, and an interference risk weight is assigned to each noise feature to obtain the noise feature interference risk weight; Calculate the interference weighted score of the air cooling device based on the noise characteristic interference risk score and the noise characteristic interference risk weight; Based on the interference weighted score of the air cooling device and the noise control parameters of the initialized air cooling device, the noise interference coefficient of the air cooling device is calculated using the interference evaluation formula.

[0010] Optionally, the interference assessment formula is:

[0011] Where, is the noise interference coefficient of the air cooling device, For air cooling device The noise characteristics interfere with the risk weight, For air cooling device Noise features interfere with risk scoring, To initialize the noise control parameters of the air cooling device, is the total number of noise characteristics of the air-cooling device.

[0012] Optionally, the establishing of a dynamic noise control optimization model, analyzing the change trend of the noise interference coefficient of the air cooling device by the current ambient noise level and the noise control parameters of the initialized air cooling device, and generating the optimal noise control parameters, includes: The noise control range corresponding to the noise characteristics of the working environment type is used as an adjustable noise control limit condition; Construct a dynamic noise control optimization model; Based on the dynamic noise control optimization model, under the adjustable noise control limit conditions, the current ambient noise level and the noise control parameters of the initialized air-cooling device are used as independent variable inputs for the noise interference coefficient change trend of the air-cooling device. The noise control parameters are gradually increased according to the adjustable noise control limit conditions, and several rounds of iterations are performed to output the noise control parameters that minimize the noise interference coefficient of the air-cooling device as the dependent variable to generate the optimal noise control parameters.

[0013] Optionally, the dynamic noise control optimization model expression is:

[0014] Where, is the optimal noise control parameter, For the The noise control parameters during the round iteration, is the learning efficiency step size, is the gradient of the noise control parameter, is the noise interference coefficient function of the air cooling device, To initialize the noise control parameters, is the current ambient noise level, is the upper limit of the noise control range corresponding to the noise characteristics of the working environment type, is the lower limit.

[0015] In addition, to achieve the above-mentioned purpose, the present invention further provides an adaptive noise optimization control system for an air cooling device, the adaptive noise optimization control system for the air cooling device comprising: An environment acquisition module is used to obtain the working environment type and current workload of the air cooling device; a noise estimation module, configured to preliminarily estimate the noise level of the air cooling device according to the type of working environment, and determine a noise optimization coefficient of the air cooling device based on the current workload and the preliminarily estimated noise level; A parameter initialization module is used to initialize the noise control parameters of the air cooling device based on the noise optimization coefficient of the air cooling device, and determine the current environmental noise level based on environmental sensor identification; an interference evaluation module, configured to evaluate a noise interference coefficient of the air cooling device based on the current ambient noise level and the noise control parameters of the initialized air cooling device; A model building module is used to establish a dynamic noise control optimization model, analyze the change trend of the noise interference coefficient of the air cooling device by the current environmental noise level and the noise control parameters of the initialized air cooling device, and generate the optimal noise control parameters; The parameter optimization module is used to optimize and control the fan operating parameters of the air cooling device based on the optimal noise control parameters.

[0016] In addition, to achieve the above-mentioned purpose, the present invention also provides an adaptive noise optimization control device for an air-cooling device, the device comprising: a memory, a processor, and an adaptive noise optimization control program for the air-cooling device stored on the memory and runnable on the processor, the adaptive noise optimization control program for the air-cooling device being configured to implement the steps of the adaptive noise optimization control method for the air-cooling device as described in any one of the above texts.

[0017] In addition, to achieve the above-mentioned purpose, the present invention also provides a storage medium, on which an adaptive noise optimization control program for an air cooling device is stored. When the adaptive noise optimization control program for the air cooling device is executed by a processor, the steps of the adaptive noise optimization control method for an air cooling device as described in any one of the above descriptions are implemented.

[0018] The present invention provides an adaptive noise optimization control method for an air-cooling device, the method comprising: obtaining the working environment type and current workload of the air-cooling device, preliminarily estimating the noise level and determining a noise optimization coefficient, thereby more accurately predicting and controlling the noise level and avoiding the blindness and inaccuracy of noise control in traditional methods; initializing noise control parameters based on the coefficient and identifying the current ambient noise level using an environmental sensor; evaluating the noise interference coefficient based on the ambient noise and the initialization parameters, so that the air-cooling device can dynamically adjust the control parameters according to the actual ambient noise, thereby improving the environmental adaptability and stability of the system; establishing a dynamic noise control optimization model, analyzing the changing trends of the ambient noise and the noise control parameters on the interference coefficient, generating optimal noise control parameters, achieving dynamic optimization of noise control, and ensuring that the optimal noise control effect can be achieved under different working conditions; and finally optimizing the fan operating parameters based on the optimal parameters, achieving real-time adaptive noise control, capable of timely responding to changes in the environment and workload, and ensuring that noise control is always in an optimal state. The present invention integrates the working environment type, current workload, and real-time ambient noise data to construct a multi-dimensional perception and dynamic adjustment system, which can accurately estimate the noise level and generate optimal control parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 Schematic diagram of the structure of an adaptive noise optimization control device for an air cooling device in a hardware operating environment according to an embodiment of the present invention; Figure 2 This is a flow chart of a first embodiment of the adaptive noise optimization control method for an air cooling device according to the present invention; Figure 3 This is a flow chart of a second embodiment of the adaptive noise optimization control method for an air cooling device according to the present invention; Figure 4 This is a flow chart of a third embodiment of the adaptive noise optimization control method for an air cooling device according to the present invention; Figure 5 This is a structural block diagram of the first embodiment of the adaptive noise optimization control system for an air cooling device of the present invention.

[0020] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0021] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0022] Reference Figure 1 , Figure 1 This is a structural diagram of an adaptive noise optimization control device for an air cooling device in a hardware operating environment according to an embodiment of the present invention.

[0023] like Figure 1 As shown, the adaptive noise optimization control device for an air cooling device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen. Optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. In the present invention, the wired interface of the user interface 1003 may be a USB interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a wireless fidelity (WI-FI) interface). The memory 1005 may be a high-speed random access memory (RAM) or a non-volatile memory (NVM), such as a disk drive. The memory 1005 may also be a storage device independent of the processor 1001.

[0024] Those skilled in the art will understand that Figure 1 The structure shown in does not constitute a limitation on the adaptive noise optimization control device of the air cooling device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0025] like Figure 1 As shown, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an adaptive noise optimization control program for an air cooling device.

[0026] exist Figure 1 In the adaptive noise optimization control device of the air cooling device shown, the network interface 1004 is mainly used to connect to the background server and communicate data with the background server; the user interface 1003 is mainly used to connect to peripheral devices; the adaptive noise optimization control device of the air cooling device calls the adaptive noise optimization control program of the air cooling device stored in the memory 1005 through the processor 1001, and executes the adaptive noise optimization control method of the air cooling device provided in the embodiment of the present invention.

[0027] Based on the above hardware structure, an embodiment of the adaptive noise optimization control method of the air cooling device of the present invention is proposed.

[0028] Reference Figure 2 , Figure 21 is a flow chart of a first embodiment of an adaptive noise optimization control method for an air cooling device according to the present invention, and provides a first embodiment of an adaptive noise optimization control method for an air cooling device according to the present invention.

[0029] In a first embodiment, the adaptive noise optimization control method of the air cooling device includes the following steps: S10: Obtain the working environment type and current workload of the air cooling device.

[0030] It should be noted that the executor of this embodiment can be an air-cooling device with environmental sensors, noise control functions and adaptive adjustment functions of fan operating parameters, which can autonomously perform noise optimization control, or other electronic devices that can achieve the above functions. This embodiment does not limit this.

[0031] It should be noted that the operating environment type refers to the environmental characteristics of the air cooling device. Examples include indoor environments such as offices, data centers, and laboratories; outdoor environments such as outdoor equipment and industrial plants; and special environments such as high temperature, high humidity, and high dust levels. Different environment types correspond to different noise levels and corresponding noise requirements. The current workload refers to the current operating load of the air cooling device, usually expressed as a percentage or a specific value, such as fan speed. The workload directly affects the device's noise level.

[0032] In specific implementations, environmental data is collected through environmental sensors. These sensors may include temperature sensors, humidity sensors, and dust sensors. The environment type is automatically identified based on pre-set environmental classification rules (such as temperature range and humidity range). Alternatively, the environment type can be determined through user input (such as selecting indoor / outdoor). The workload is monitored in real time using the operating parameters of the air cooling device. These operating parameters include fan speed, power consumption, and heat dissipation requirements. Data is collected using sensors or the device's built-in monitoring module. The collected data is converted into standardized workload metrics, such as percentages or specific values.

[0033] S20: Preliminarily estimating a noise level of the air cooling device according to the working environment type, and determining a noise optimization coefficient of the air cooling device based on the current workload and the preliminarily estimated noise level.

[0034] It should be noted that the noise level refers to the noise intensity generated when the air-cooling device is in operation, and the noise level is affected by factors such as fan speed, air flow speed, and mechanical vibration. The noise optimization coefficient is the ratio of workload to noise level, which can intuitively reflect the noise control performance of the air-cooling device. A high noise optimization coefficient indicates that the device can still maintain low-noise operation under high load, which is an important indicator of good noise control effect. For example, when the air-cooling device is running under a high workload, if the noise level is low, it means that the noise control effect of the device is good. At this time, the noise optimization coefficient is high, indicating that the noise control performance is superior. When the air-cooling device is running under a low workload, if the noise level is high, it means that the noise control effect of the device is poor. At this time, the noise optimization coefficient is low, indicating that the noise control performance is poor.

[0035] In practical applications, a high noise optimization factor is suitable for noise-sensitive scenarios requiring efficient heat dissipation, such as data centers and offices. This indicates that the device can maintain low noise operation and excellent performance under high loads. A low noise optimization factor is suitable for scenarios with low noise requirements, such as industrial plants and outdoor equipment, indicating that the device's noise control performance needs improvement.

[0036] S30: Initializing noise control parameters of the air cooling device based on the noise optimization coefficient of the air cooling device, and determining the current environmental noise level based on environmental sensor identification.

[0037] S40: Evaluate the noise interference coefficient of the air cooling device according to the current environmental noise level and the noise control parameter of the initialized air cooling device.

[0038] It should be noted that noise control parameters refer to specific operating parameters used to adjust the noise level of the air cooling device. Examples include fan speed: lower speeds generally result in lower noise levels; air volume control: balancing heat dissipation and noise levels by adjusting air volume; and vibration suppression parameters: reducing mechanical vibrations to lower noise levels. These parameters directly impact the noise level and heat dissipation performance of the air cooling device. Environmental sensors refer to devices used to monitor ambient noise levels, such as noise sensors. Environmental sensors can also include temperature, humidity, and air pressure sensors, providing comprehensive awareness of environmental conditions. The current ambient noise level refers to the intensity of background noise in the air cooling device's environment. The ambient noise level is an important indicator for evaluating the noise interference of the air cooling device.

[0039] In practice, initializing noise control parameters involves setting the initial noise control parameters based on the noise optimization coefficient. A high noise optimization coefficient indicates good noise control performance, and the parameters can be maintained or slightly adjusted. A low noise optimization coefficient indicates poor noise control performance, and the parameters need to be optimized to reduce noise. For example, setting the initial fan speed involves selecting low, medium, or high speed based on the noise optimization coefficient; adjusting air volume control parameters to balance heat dissipation requirements and noise levels; and enabling vibration suppression to reduce mechanical vibration noise.

[0040] Among them, identifying the current environmental noise level based on environmental sensors specifically includes: deploying environmental sensors to monitor the environmental noise level in real time; collecting and processing environmental noise data, and using filtering algorithms to remove interference signals, such as instantaneous noise; calculating the average environmental noise level as an evaluation benchmark; comparing the environmental noise level with the noise level of the air cooling device, evaluating the degree of noise interference, and determining the noise interference coefficient of the air cooling device.

[0041] S50: establishing a dynamic noise control optimization model, analyzing the change trend of the noise interference coefficient of the air cooling device affected by the current environmental noise level and the noise control parameters of the initialized air cooling device, and generating optimal noise control parameters.

[0042] S60: Optimizing and controlling fan operating parameters of the air cooling device based on the optimal noise control parameters.

[0043] It should be noted that by analyzing the current ambient noise level and initializing the noise control parameters of the air cooling device, the changing trend of the noise interference coefficient is predicted, and the optimal noise control parameters are generated, ultimately achieving optimized control of the fan operating parameters of the air cooling device.

[0044] In a specific implementation, a dynamic noise control optimization model is established, in which the model input is the current ambient noise level, the initialized noise control parameters, and the historical data of the noise interference coefficient. The current ambient noise level is the decibel value collected in real time by the ambient noise sensor. The initialized noise control parameters include fan speed, air volume control, vibration suppression parameters, etc. The historical data of the noise interference coefficient is used to analyze the change trend. The model output is the noise interference coefficient change trend and the optimal noise control parameters. The noise interference coefficient change trend is used to predict the future change of the noise interference coefficient. The optimal noise control parameters are used to generate the optimal parameters based on the prediction results. The model construction process includes filtering the collected ambient noise level and noise interference coefficient to remove outliers. The data is standardized to facilitate model calculation. Time series analysis (such as ARIMA, LSTM) or regression models (such as linear regression, support vector machine) are used to predict the change trend of the noise interference coefficient. For example, based on the historical noise interference coefficient and the ambient noise level, the future noise interference coefficient is predicted. Optimization objective function: The goal is to minimize the noise interference coefficient while meeting the heat dissipation requirements, where the objective function is:

[0045] in, is a weight coefficient used to balance noise control and heat dissipation performance, for example The smaller it is, the priority is to optimize noise interference. The larger it is, the priority is to optimize heat dissipation; This can be a temperature threshold constraint, a minimum air volume constraint, or a heat dissipation power balance constraint. For example, a temperature threshold constraint can require the ambient temperature to be less than a preset threshold, a minimum air volume constraint can require the cooling fan speed to be greater than a preset minimum speed, and a heat dissipation power balance constraint can require the fan heat dissipation power to be no less than the device's heat generation power. An optimization algorithm (such as a genetic algorithm or particle swarm optimization) is used to solve the objective function and generate the optimal noise control parameters.

[0046] Furthermore, the fan operating parameters are optimized based on the optimal noise control parameters. The optimization goal is to adjust the fan operating parameters according to the optimal noise control parameters to achieve the best balance between noise and heat dissipation. Specifically, according to the optimal noise control parameters, the fan speed is set to low, medium or high speed. For example, if the noise interference coefficient is high, reduce the fan speed to reduce noise. Adjust the air volume and airflow direction according to the heat dissipation requirements and noise control parameters. For example, reduce the air volume to reduce noise while ensuring heat dissipation. Enable or adjust vibration suppression parameters such as shock pads and mechanical damping according to the optimal noise control parameters. For example, when the noise interference coefficient is high, enhance the vibration suppression function. Deploy sensors to monitor noise levels and heat dissipation performance in real time. Dynamically adjust the fan operating parameters based on the monitoring results to ensure the optimal balance between noise and heat dissipation.

[0047] The present embodiment provides an adaptive noise optimization control method for an air cooling device, the method comprising: obtaining a working environment type and a current workload of the air cooling device; preliminarily estimating the noise level of the air cooling device according to the working environment type, and determining a noise optimization coefficient of the air cooling device based on the current workload and the preliminarily estimated noise level; initializing noise control parameters of the air cooling device based on the noise optimization coefficient of the air cooling device, and determining the current environmental noise level based on environmental sensor identification; evaluating the noise interference coefficient of the air cooling device based on the current environmental noise level and the noise control parameters of the initialized air cooling device; establishing a dynamic noise control optimization model, analyzing the effect of the current environmental noise level and the noise control parameters of the initialized air cooling device on the noise interference coefficient of the air cooling device, and generating optimal noise control parameters; optimizing and controlling the fan operating parameters of the air cooling device based on the optimal noise control parameters, thereby realizing real-time adaptive optimization of the noise of the air cooling device and effectively solving the technical problem that traditional methods cannot dynamically adjust noise control parameters.

[0048] Reference Figure 3 , Figure 3 This is a flow chart of a second embodiment of the adaptive noise optimization control method for an air cooling device according to the present invention. In the second embodiment, initializing the noise control parameters of the air cooling device based on the noise optimization coefficient of the air cooling device includes: S301: Acquire, based on a working environment type of an air cooling device, several noise characteristics of the working environment type, and determine a noise control range corresponding to the noise characteristics of the working environment type, wherein the noise characteristics of the working environment type include: environmental noise type, noise intensity, and noise frequency; It should be noted that noise characteristics refer to the specific properties of the working environment noise, including the ambient noise type, noise intensity, and noise frequency. Ambient noise types can include mechanical noise, airflow noise, electromagnetic noise, and so on. Noise intensity is the ambient noise level in decibels (dB). Noise frequency refers to the frequency distribution of noise, such as low-frequency noise, medium-frequency noise, and high-frequency noise. The noise control range refers to the permissible range of noise control for the air cooling device under a specific working environment. The noise control range should be determined based on the ambient noise characteristics and heat dissipation requirements.

[0049] Specifically, based on the actual deployment scenario of the air cooling device, identify the type of working environment, such as offices, data centers, and industrial plants. Use environmental noise sensors to collect environmental noise data, including: Environmental noise type: Identify the noise source (such as mechanical noise and airflow noise) through spectrum analysis. Noise intensity: Measure the average decibel value of the environmental noise. Noise frequency: Analyze the frequency distribution of noise through Fourier transform. Set the allowable range of noise control based on the type of working environment and noise characteristics. For example, in an office environment, the noise control range can be set to 30-40dB. In an industrial plant environment, the noise control range can be set to 60-70dB.

[0050] S302: Using a noise adjustment algorithm, the noise control range corresponding to the noise characteristics of the working environment type is corrected based on the noise optimization coefficient of the air cooling device, the noise control parameters of the initialization air cooling device are determined, and the current environmental noise level is determined based on environmental sensor identification.

[0051] It should be noted that the noise adjustment algorithm refers to an algorithm used to correct the noise control range, such as a linear regression algorithm or a fuzzy logic algorithm; the linear regression algorithm predicts the noise control range based on the noise optimization coefficient and noise characteristics; the fuzzy logic algorithm combines the noise characteristics and noise optimization coefficient to generate fuzzy rules and correct the noise control range.

[0052] Specifically, based on the noise optimization coefficient and noise characteristics, a noise adjustment algorithm is used to correct the noise control range. For example, if the noise optimization coefficient is high, it indicates that the noise control performance is good, and the noise control range can be appropriately relaxed. If the noise optimization coefficient is low, it indicates that the noise control performance is poor, and the noise control range needs to be narrowed. According to the corrected noise control range, the initial operating parameters of the air cooling device are set. For example, fan speed: select low, medium or high speed mode according to the noise control range. Air volume control: adjust the air volume to balance the noise and heat dissipation requirements. Vibration suppression parameters: enable or adjust parameters such as shock pads and mechanical damping. Deploy the air cooling device and monitor the noise level and heat dissipation performance in real time. Further optimize the noise control parameters based on the monitoring results to ensure the best balance between noise and heat dissipation.

[0053] The noise adjustment algorithm is expressed as follows:

[0054] Where, To initialize the noise control parameters of the air cooling device, is the maximum noise control range corresponding to the noise characteristics of the working environment type, is the minimum noise control range corresponding to the noise characteristics of the working environment type, The noise optimization factor for air-cooled devices.

[0055] It should be noted that To initialize the noise control parameters of the air cooling device, This is the final result of the algorithm calculation, representing the noise control parameters of the air cooling device during the initialization phase. This can be specific operating parameters such as fan speed, air volume control, and vibration suppression parameters. It is the lower limit of the noise control parameter, which indicates the minimum allowable value of the air cooling device noise control in a specific working environment. For example, in an office environment, It can be 30 dB. It is the upper limit of the noise control parameter, which indicates the maximum allowable value of the air cooling device noise control in a specific working environment. For example, in an industrial plant environment, It can be 70 dB. Is a dimensionless coefficient used to evaluate the noise control performance of the air cooling device. Its value is usually between 0 and 1, indicating the degree of optimization of noise control. For example, if =1, indicating the best noise control performance, can be used directly .like =0, indicating the worst noise control performance, and requires The noise adjustment algorithm expression is used to optimize the noise coefficient of the air cooling device according to , in the noise control range [ , ], dynamically calculate and initialize noise control parameters .when The bigger it is, The closer , indicating that the noise control performance is good and the noise limit can be appropriately relaxed. The smaller the time, The closer , indicating that the noise control performance is poor and strict noise control is required.

[0056] Reference Figure 4 , Figure 4 This is a flow chart of a third embodiment of the adaptive noise optimization control method for an air cooling device of the present invention. In the third embodiment, evaluating the noise interference coefficient of the air cooling device based on the current ambient noise level and the noise control parameters of the initialized air cooling device includes: S401: assigning an interference risk score to each noise feature according to a change trend of interference risk when the current environmental noise level affects the control of multiple noise features of the air cooling device, thereby obtaining a noise feature interference risk score; It should be noted that the interference risk trend refers to the potential interference risk associated with controlling the noise characteristics of air cooling devices under a specific ambient noise level. For example, as the ambient noise level increases, the noise control risk may increase. The interference risk score is a score assigned to each noise characteristic, quantifying its interference risk in noise control. A higher score indicates a greater interference risk.

[0057] Specifically, based on the noise characteristics of the air cooling system, key noise features such as noise intensity, noise frequency, and noise type are identified. Based on the current ambient noise level, each noise feature is analyzed to determine the potential interference risk trends that may arise when controlled. For example, when noise intensity is high in ambient noise, the interference risk may increase. Each noise feature is assigned an interference risk score ranging from 0 to 10. For example, the interference risk score for noise intensity is 8, and the interference risk score for noise frequency is 5.

[0058] S402: Standardizing the noise feature interference risk score using a standardized formula; It should be noted that normalization refers to converting scores of different dimensions or ranges into a unified standard range to facilitate subsequent calculations. Common normalization methods include Min-Max normalization and Z-score normalization. For example, using the Min-Max normalization method, the scores are converted to a range of 0 to 1. The normalization formula is:

[0059] in, is the original score, and are the minimum and maximum values of the score, respectively. For example, the interference risk score of 8 for noise intensity is converted to a standardized score of 0.8.

[0060] S403: Determine the relative importance of each noise feature interference risk score to the noise interference during control based on the standardized noise feature interference risk score, assign an interference risk weight to each noise feature, and obtain a noise feature interference risk weight; It should be noted that the interference risk weight refers to the weight assigned to each noise feature, which is used to indicate the relative importance of that feature in the noise interference. The higher the weight, the greater the impact of the feature on the noise interference. Specifically, the relative importance of each noise feature in the noise interference is analyzed based on the standardized score. For example, a higher standardized score for noise intensity indicates its greater importance. Each noise feature is assigned a weight, and the total weight is 1. For example, the weight of noise intensity is 0.5, and the weight of noise frequency is 0.3.

[0061] S404: Calculating a weighted interference score for the air cooling device based on the noise characteristic interference risk score and the noise characteristic interference risk weight; It should be noted that the interference weighted score refers to the comprehensive score obtained by multiplying the interference risk score of each noise feature by its weight and summing them up. It is used to quantify the overall interference risk of the air cooling device. The weighted score calculation formula is:

[0062] in, For the The normalized score of the noise features, For the For example, if the normalized score of noise intensity is 0.8 and the weight is 0.5, and the normalized score of noise frequency is 0.5 and the weight is 0.3, then W = 0.8 × 0.5 + 0.5 × 0.3 = 0.4 + 0.15 = 0.55.

[0063] S405: Calculating a noise interference coefficient of the air cooling device using an interference evaluation formula based on the interference weighted score of the air cooling device and the initialized noise control parameters of the air cooling device.

[0064] It should be noted that the interference assessment formula is:

[0065] Where, is the noise interference coefficient of the air cooling device, For air cooling device The noise characteristics interfere with the risk weight, For air cooling device Noise features interfere with risk scoring, To initialize the noise control parameters of the air cooling device, is the total number of noise characteristics of the air-cooling device.

[0066] It should be understood that This is the final result of the formula calculation, used to quantify the degree to which the noise generated by the air cooling device interferes with the ambient noise. The noise interference coefficient is a dimensionless number; higher values indicate more severe noise interference. is the weight assigned to the i-th noise feature, indicating the relative importance of the feature in the noise interference. The higher the weight, the greater the impact of the feature on the noise interference. is the score assigned to the i-th noise feature, which is used to quantify the interference risk of this feature in noise control. A higher score indicates a greater interference risk. It is the noise control parameter of the air cooling device during the initialization phase, which can be specific operating parameters such as fan speed, air volume control, vibration suppression parameters, etc. It directly affects the size of the noise interference coefficient. is the total number of air-cooling device noise signatures, representing the number of noise signatures to be calculated in the formula. The interference assessment formula comprehensively evaluates the noise interference coefficient of the air-cooling device, taking into account the interference risk score and weight of each noise signature, as well as the initial noise control parameters. The final noise interference coefficient is calculated by multiplying the interference risk score of each noise signature by its weight using a weighted summation method, and then multiplying it by the initial noise control parameters.

[0067] Furthermore, the dynamic noise control optimization model is established to analyze the change trend of the noise interference coefficient of the air cooling device caused by the current ambient noise level and the noise control parameters of the initialized air cooling device, and to generate the optimal noise control parameters, including: The noise control range corresponding to the noise characteristics of the working environment type is used as an adjustable noise control limit condition; Construct a dynamic noise control optimization model; Based on the dynamic noise control optimization model, under the adjustable noise control limit conditions, the current ambient noise level and the noise control parameters of the initialized air-cooling device are used as independent variable inputs for the noise interference coefficient change trend of the air-cooling device. The noise control parameters are gradually increased according to the adjustable noise control limit conditions, and several rounds of iterations are performed to output the noise control parameters that minimize the noise interference coefficient of the air-cooling device as the dependent variable to generate the optimal noise control parameters.

[0068] It should be noted that the noise control range refers to the permissible range of air-cooling device noise control in a specific working environment. Adjustable noise control constraints refer to the dynamic adjustment constraints within the noise control range during the optimization process. These constraints restrict the range of noise control parameters, ensuring that the optimization results meet the requirements of the actual working environment. Specifically, the lower and upper limits of the noise control range are determined based on the working environment type and noise characteristics. For example, the noise control range for an office environment is 30-40 dB.

[0069] In the optimization model, the noise control range is used as a constraint to limit the range of noise control parameters. The dynamic noise control optimization model is a mathematical model used to minimize the noise interference coefficient of the air cooling system while dynamically adjusting the noise control parameters. This model incorporates factors such as the noise control range, the current ambient noise level, and the initialization of the noise control parameters.

[0070] Based on the dynamic noise control optimization model, under the adjustable noise control limit conditions, the current ambient noise level and the noise control parameters of the initialized air-cooling device are used as independent variable inputs for the noise interference coefficient change trend of the air-cooling device. The noise control parameters are gradually increased according to the adjustable noise control limit conditions, and several rounds of iterations are performed to output the noise control parameters that minimize the noise interference coefficient of the air-cooling device as the dependent variable to generate the optimal noise control parameters.

[0071] It should be noted that independent variable input refers to the parameters used as input variables during the optimization process, including the current ambient noise level, the initialized noise control parameters, and the trend of the noise interference coefficient. Incremental adjustment refers to the gradual adjustment of noise control parameters during the optimization process, using a specific step size, for example, by 1 dB at a time. Iterations refer to the repetition of multiple calculations during the optimization process until convergence conditions are met. Dependent variable output refers to the parameters used as the output of the optimization process, namely, the optimal noise control parameters.

[0072] The dynamic noise control optimization model expression is:

[0073] Where, is the optimal noise control parameter, For the The noise control parameters during the round iteration, is the learning efficiency step size, is the gradient of the noise control parameter, is the noise interference coefficient function of the air cooling device, To initialize the noise control parameters, is the current ambient noise level, is the upper limit of the noise control range corresponding to the noise characteristics of the working environment type, is the lower limit.

[0074] It should be understood that It is the final result obtained by the optimization model calculation, which represents the noise control parameter that minimizes the noise interference coefficient while meeting the noise control range limit conditions. is the noise control parameter adjusted in each iteration during the optimization process. For example, in the first iteration, represents the noise control parameters of the first round. is a positive number that controls the amplitude of the noise control parameter adjustment. A larger step size results in faster parameter adjustment but may affect convergence; a smaller step size results in slower parameter adjustment but better convergence. is the noise interference coefficient function Noise control parameters The partial derivative of represents the rate of change of the noise interference coefficient with the change of the noise control parameters. Is a function used to calculate the noise interference coefficient of the air cooling device, whose input is the noise control parameter and current ambient noise level . It is the noise control parameter of the air cooling device during the initialization stage, which can be specific operating parameters such as fan speed, air volume control, vibration suppression parameters, etc. is the noise level in the current working environment. It is the upper limit of the noise control parameter, indicating the maximum allowable value of the air cooling device noise control under a specific working environment. It is the lower limit of the noise control parameter, indicating the minimum allowable value of the air cooling device noise control under a specific working environment.

[0075] The first line of the dynamic noise control optimization model is used to express the noise control parameters The noise control range restriction conditions must be met, i.e. Must be greater than the lower limit and less than or equal to the upper limit The second line of expression is used to express the optimal noise control parameters The noise control parameters are iteratively adjusted and the gradient descent method is used to minimize the noise interference coefficient.

[0076] In addition, an embodiment of the present invention also proposes a storage medium, on which an adaptive noise optimization control program for an air cooling device is stored. When the adaptive noise optimization control program for the air cooling device is executed by a processor, the steps of the adaptive noise optimization control method for the air cooling device as described above are implemented.

[0077] In addition, refer to Figure 5 The embodiment of the present invention further provides an adaptive noise optimization control system for an air cooling device, the adaptive noise optimization control system for the air cooling device comprising: An environment acquisition module 10 is used to obtain the working environment type and current workload of the air cooling device; a noise estimation module 20 for preliminarily estimating a noise level of the air cooling device according to the type of the working environment, and determining a noise optimization coefficient of the air cooling device based on the current workload and the preliminarily estimated noise level; a parameter initialization module 30 for initializing noise control parameters of the air cooling device based on the noise optimization coefficient of the air cooling device and determining the current ambient noise level based on environmental sensor identification; An interference evaluation module 40 is configured to evaluate a noise interference coefficient of the air cooling device according to the current ambient noise level and the noise control parameters of the initialized air cooling device; A model building module 50 is used to establish a dynamic noise control optimization model, analyze the current ambient noise level and the noise control parameters of the initialized air cooling device on the noise interference coefficient of the air cooling device, and generate the optimal noise control parameters; The parameter optimization module 60 is used to optimize and control the fan operating parameters of the air cooling device based on the optimal noise control parameters.

[0078] Other embodiments or specific implementations of the adaptive noise optimization control system of the air cooling device of the present invention can refer to the above-mentioned method embodiments and will not be repeated here.

[0079] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or system comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or system. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or system comprising the element.

[0080] The serial numbers of the embodiments of the present invention are for descriptive purposes only and do not represent superiority or inferiority of the embodiments. In a unit claim that lists several systems, several of these systems may be embodied by the same item of hardware. The use of the terms first, second, and third, etc., does not denote any order and should be interpreted as designations.

[0081] Through the above description of the embodiments, those skilled in the art will clearly understand that the above-mentioned embodiments and methods can be implemented using software plus the necessary general hardware platform. Of course, hardware can also be used, but in many cases, the former is a more preferred embodiment. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as a read-only memory image (ROM) / random access memory (RAM), a magnetic disk, or an optical disk) and includes a number of instructions for enabling an end-user device (such as a mobile phone, computer, server, air conditioner, or network user device, etc.) to execute the methods described in various embodiments of the present invention.

[0082] The above are only preferred embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. An adaptive noise optimization control method for an air cooling device, characterized in that: The method comprises: Obtain the working environment type and current workload of the air cooling device; Preliminarily estimating a noise level of the air cooling device according to the type of working environment, and determining a noise optimization coefficient of the air cooling device based on the current workload and the preliminarily estimated noise level; Initializing noise control parameters of the air cooling device based on the noise optimization coefficient of the air cooling device, and determining the current environmental noise level based on environmental sensor identification; evaluating a noise interference coefficient of the air cooling device according to the current ambient noise level and the noise control parameters of the initialized air cooling device; Establish a dynamic noise control optimization model to analyze the change trend of the noise interference coefficient of the air cooling device by the current ambient noise level and the noise control parameters of the initialized air cooling device, and generate the optimal noise control parameters; The fan operating parameters of the air cooling device are optimized and controlled based on the optimal noise control parameters.

2. The adaptive noise optimization control method for an air cooling device according to claim 1, characterized in that: Initializing the noise control parameters of the air cooling device based on the noise optimization coefficient of the air cooling device, and determining the current environmental noise level based on environmental sensor identification, includes: According to the working environment type of the air cooling device, several noise characteristics of the working environment type are obtained, and a noise control range corresponding to the noise characteristics of the working environment type is determined, wherein the noise characteristics of the working environment type include: environmental noise type, noise intensity, and noise frequency; Using a noise adjustment algorithm, the noise control range corresponding to the noise characteristics of the working environment type is corrected based on the noise optimization coefficient of the air cooling device, the noise control parameters of the air cooling device are initialized, and the current environmental noise level is determined based on environmental sensor identification.

3. The adaptive noise optimization control method for an air cooling device according to claim 2, characterized in that: The noise adjustment algorithm is expressed as: Where, To initialize the noise control parameters of the air cooling device, is the maximum noise control range corresponding to the noise characteristics of the working environment type, is the minimum noise control range corresponding to the noise characteristics of the working environment type, The noise optimization factor for air-cooled devices.

4. The adaptive noise optimization control method for an air cooling device according to claim 3, characterized in that: The step of evaluating the noise interference coefficient of the air cooling device according to the current ambient noise level and the noise control parameter of the initialized air cooling device includes: According to the interference risk change trend when the current environmental noise level affects the control of multiple noise characteristics of the air cooling device, an interference risk score is assigned to each noise characteristic to obtain a noise characteristic interference risk score; The noise feature interference risk score is standardized through a standardized formula; According to the standardized noise feature interference risk score, the relative importance of each noise feature interference risk score to the noise interference during control is determined, and an interference risk weight is assigned to each noise feature to obtain the noise feature interference risk weight; Calculate the interference weighted score of the air cooling device based on the noise characteristic interference risk score and the noise characteristic interference risk weight; Based on the interference weighted score of the air cooling device and the noise control parameters of the initialized air cooling device, the noise interference coefficient of the air cooling device is calculated using the interference evaluation formula.

5. The adaptive noise optimization control method for an air cooling device according to claim 4, characterized in that: The interference assessment formula is: Where, is the noise interference coefficient of the air cooling device, For air cooling device The noise characteristics interfere with the risk weight, For air cooling device Noise features interfere with risk scoring, To initialize the noise control parameters of the air cooling device, is the total number of noise characteristics of the air-cooling device.

6. The adaptive noise optimization control method for an air cooling device according to claim 5, characterized in that: The dynamic noise control optimization model is established to analyze the change trend of the noise interference coefficient of the air cooling device in terms of the current ambient noise level and the noise control parameters of the initialized air cooling device, and to generate the optimal noise control parameters, including: The noise control range corresponding to the noise characteristics of the working environment type is used as an adjustable noise control limit condition; Construct a dynamic noise control optimization model; Based on the dynamic noise control optimization model, under the adjustable noise control limit conditions, the current ambient noise level and the noise control parameters of the initialized air-cooling device are used as independent variable inputs for the noise interference coefficient change trend of the air-cooling device. The noise control parameters are gradually increased according to the adjustable noise control limit conditions, and several rounds of iterations are performed to output the noise control parameters that minimize the noise interference coefficient of the air-cooling device as the dependent variable to generate the optimal noise control parameters.

7. The adaptive noise optimization control method for an air cooling device according to claim 6, characterized in that: The dynamic noise control optimization model expression is: Where, is the optimal noise control parameter, For the The noise control parameters during the round iteration, is the learning efficiency step size, is the gradient of the noise control parameter, is the noise interference coefficient function of the air cooling device, To initialize the noise control parameters, is the current ambient noise level, is the upper limit of the noise control range corresponding to the noise characteristics of the working environment type, is the lower limit.

8. An adaptive noise optimization control system for an air cooling device, characterized in that: The adaptive noise optimization control system of the air cooling device includes: An environment acquisition module is used to obtain the working environment type and current workload of the air cooling device; a noise estimation module, configured to preliminarily estimate the noise level of the air cooling device according to the type of working environment, and determine a noise optimization coefficient of the air cooling device based on the current workload and the preliminarily estimated noise level; A parameter initialization module is used to initialize the noise control parameters of the air cooling device based on the noise optimization coefficient of the air cooling device, and determine the current environmental noise level based on environmental sensor identification; an interference evaluation module, configured to evaluate a noise interference coefficient of the air cooling device based on the current ambient noise level and the noise control parameters of the initialized air cooling device; A model building module is used to establish a dynamic noise control optimization model, analyze the change trend of the noise interference coefficient of the air cooling device by the current environmental noise level and the noise control parameters of the initialized air cooling device, and generate the optimal noise control parameters; The parameter optimization module is used to optimize and control the fan operating parameters of the air cooling device based on the optimal noise control parameters.

9. An adaptive noise optimization control device for an air cooling device, characterized in that: The device includes: a memory, a processor, and an adaptive noise optimization control program for an air cooling device stored in the memory and executable on the processor. The adaptive noise optimization control program for the air cooling device is configured to implement the steps of the adaptive noise optimization control method for an air cooling device according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores an adaptive noise optimization control program for an air cooling device, and when the adaptive noise optimization control program for an air cooling device is executed by a processor, the steps of the adaptive noise optimization control method for an air cooling device according to any one of claims 1 to 7 are implemented.

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