A heat dissipation control method, system and switch for domestically produced switches

Abstract

The present invention relates to a heat dissipation control method, system, and switch for a domestically produced switch, and relates to the field of switch heat dissipation management and control, including: performing calculation predictions on future data transmission to obtain predicted data transmission parameters, performing temperature change predictions and environmental noise predictions in combination with real-time temperature and real-time heat dissipation control parameters, obtaining predicted temperature and predicted environmental noise, and calculating a configuration noise coefficient in combination with accumulated switch noise; optimizing and adjusting real-time heat dissipation control parameters based on the predicted temperature and noise coefficient to obtain optimal heat dissipation control parameters for heat dissipation control. The present invention can solve the technical problem that traditional methods often rely on preset heat dissipation control strategies and cannot accurately adjust heat dissipation control parameters according to actual load and environmental changes; it can improve the accuracy and rationality of heat dissipation control parameter settings, achieve dual optimization of temperature control and noise control, effectively improve heat dissipation efficiency, and reduce noise pollution at the same time.

Description

Technical Field

[0001] The present invention relates to the field of heat dissipation control of switches, and in particular to a heat dissipation control method and system for domestically produced switches, and the switches. Background Art

[0002] As a crucial component of modern communications networks, switches' stability and efficiency directly impact the quality of network operation. With the increasing use of network applications, the volatility of switch loads and the complexity of environmental changes are becoming increasingly prominent. Therefore, heat dissipation management and control of switches has become a key factor in ensuring efficient and stable operation.

[0003] Traditional switch cooling control methods typically rely on preset cooling control strategies, which adjust the cooling system based on fixed temperature thresholds or set fan speeds. These strategies are unable to dynamically adjust based on actual load and environmental changes, resulting in over- or under-cooling. Summary of the Invention

[0004] The present invention addresses the technical problem that the heat dissipation control method of traditional switches often relies on preset heat dissipation control strategies and cannot accurately adjust the heat dissipation control parameters according to actual load and environmental changes. It provides a heat dissipation control method, system and switch for domestically produced switches to solve the problem.

[0005] The technical solution of the present invention to solve the above technical problems is as follows:

[0006] In a first aspect, the present invention provides a heat dissipation control method for a domestically produced switch, comprising: monitoring and obtaining the current real-time temperature, real-time heat dissipation control parameters, and accumulated switch noise of the switch; obtaining data transmission records of the switch within a preset time range in the past, performing calculation and prediction of future data transmission, obtaining predicted data transmission parameters, and performing temperature change prediction and environmental noise prediction in combination with the real-time temperature and real-time heat dissipation control parameters, obtaining predicted temperature and predicted environmental noise, and calculating a configured noise coefficient in combination with the accumulated switch noise; optimizing and adjusting the real-time heat dissipation control parameters based on the predicted temperature and noise coefficient, obtaining optimal heat dissipation control parameters, and performing heat dissipation control, wherein the optimization step size is configured according to the change amplitude of the predicted temperature and the real-time temperature.

[0007] Preferably, the heat dissipation control method for a domestically produced switch further includes: monitoring and obtaining the real-time temperature of the switch through a temperature sensor configured on the switch; obtaining the current real-time heat dissipation control parameters of the switch, wherein the heat dissipation control parameters include the fan speed; monitoring and obtaining the switch noise within a preset time interval in the past through a noise sensor configured on the switch, and calculating the average to obtain the cumulative switch noise.

[0008] Preferably, the heat dissipation control method for a domestically produced switch further includes: obtaining a data throughput sequence of the switch within a preset time range in the past; pre-training a data transmission predictor for predicting future data transmission parameters; inputting the data throughput sequence into the data transmission predictor, and predicting the data throughput at a preset time in the future as a predicted data transmission parameter.

[0009] Preferably, the heat dissipation control method for a domestically produced switch further includes: collecting a set of sample data throughput sequences based on the switch operation data in a historical period, and collecting the data throughput of the switch at future moments after different sample data throughput sequences to obtain a set of sample predicted data throughputs; using the sample data throughput sequence set and the sample predicted data throughput set, training a data transmission predictor based on machine learning, and conducting testing until convergence.

[0010] Preferably, the heat dissipation control method for a domestically produced switch further includes: obtaining the current real-time data throughput of the switch; collecting a sample real-time data throughput set, a sample real-time temperature set, a sample real-time heat dissipation control parameter set, and a sample future data throughput set, and collecting the switch temperature at future moments under different sample real-time data throughputs, sample real-time temperatures, sample real-time heat dissipation control parameters, and sample future data throughputs to obtain a sample predicted temperature set; using the sample real-time data throughput set, sample real-time temperature set, sample real-time heat dissipation control parameter set, sample future data throughput set, and sample predicted temperature set to train a temperature change predictor; inputting the real-time temperature, real-time data throughput, real-time heat dissipation control parameter, and predicted throughput into the temperature change predictor, and outputting a prediction to obtain a predicted temperature; performing environmental noise prediction based on the predicted data transmission parameters to obtain predicted environmental noise.

[0011] Preferably, the heat dissipation control method for domestic switches also includes: collecting a sample predicted data transmission parameter set based on noise monitoring data in the environment, and collecting the average environmental noise in the environment under different sample predicted data transmission parameters to obtain a sample environmental noise set; constructing a mapping relationship between the sample predicted data transmission parameter set and the sample environmental noise set to obtain an environmental noise prediction table; inputting the predicted data transmission parameters into the environmental noise prediction table, mapping and classifying to obtain the predicted environmental noise.

[0012] Preferably, the heat dissipation control method for a domestically produced switch further includes: obtaining a historical average noise in the environment; and calculating a configuration noise coefficient based on the accumulated switch noise, the historical average noise, and the predicted environmental noise, as shown in the following formula:

[0013] ;

[0014] in, is the noise coefficient, is the historical average noise, is the cumulative switch noise, To predict environmental noise.

[0015] Preferably, the heat dissipation control method for a domestically produced switch further includes: calculating a temperature deviation between the predicted temperature and the real-time temperature as an optimization step size; adjusting the real-time heat dissipation control parameter using the optimization step size to obtain a first heat dissipation control parameter; inputting the first heat dissipation control parameter into the temperature change predictor in combination with the real-time temperature, real-time data throughput, and predicted throughput, and obtaining a first control temperature as a prediction output; obtaining a first switch noise under the first heat dissipation control parameter, and calculating a heat dissipation control adaptability based on the first control temperature and noise coefficient, as shown in the following formula:

[0016] ;

[0017] in, To control the adaptability of heat dissipation, is the first control temperature, is the standard temperature, is the noise coefficient, is a small real number, For the first switch noise, is standard noise; continue to use the optimization step size to optimize and adjust the first heat dissipation control parameter until convergence, output the optimal heat dissipation control parameter with the maximum heat dissipation control fitness, and perform heat dissipation control.

[0018] In a second aspect, the present invention provides a heat dissipation control system for a domestically produced switch, comprising: a status data monitoring and acquisition module, for monitoring and acquiring the current real-time temperature, real-time heat dissipation control parameters and accumulated switch noise of the switch; a status change data prediction module, for acquiring the data transmission records of the switch within a preset time range in the past, performing calculation and prediction of future data transmission, obtaining predicted data transmission parameters, and performing temperature change prediction and environmental noise prediction in combination with the real-time temperature and real-time heat dissipation control parameters, obtaining predicted temperature and predicted environmental noise, and calculating and configuring the noise coefficient in combination with the accumulated switch noise; a heat dissipation control parameter optimization module, for optimizing and adjusting the real-time heat dissipation control parameters according to the predicted temperature and noise coefficient, obtaining optimal heat dissipation control parameters, and performing heat dissipation control, wherein the optimization step size is configured according to the change amplitude of the predicted temperature and the real-time temperature.

[0019] In a third aspect, the present invention further provides a switch, wherein the heat dissipation of the domestically produced switch can be controlled by using the heat dissipation control method for the domestically produced switch described in any one of the first aspects when the domestically produced switch is in operation.

[0020] The beneficial effects of the present invention are as follows: the current real-time temperature, real-time heat dissipation control parameters and accumulated switch noise of the switch are obtained through monitoring; then the data transmission records of the switch within a preset time range in the past are obtained, and future data transmission calculation predictions are performed to obtain predicted data transmission parameters; then, temperature change predictions and environmental noise predictions are performed based on the predicted data transmission parameters, real-time temperature and real-time heat dissipation control parameters to obtain predicted temperature and predicted environmental noise, and the configuration noise coefficient is calculated in combination with the accumulated switch noise; finally, the real-time heat dissipation control parameters are optimized and adjusted based on the predicted temperature and noise coefficient to obtain optimal heat dissipation control parameters and perform heat dissipation control; the above method can improve the accuracy and rationality of the heat dissipation control parameter setting, realize dual optimization of temperature control and noise control, avoid excessive heat dissipation or insufficient heat dissipation, thereby effectively improving heat dissipation efficiency, while reducing noise pollution, and ensuring efficient and stable operation of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A flow chart of a heat dissipation control method for a domestically produced switch provided by the present invention;

[0022] Figure 2 This is a structural schematic diagram of a heat dissipation control system for a domestically produced switch provided by the present invention.

[0023] In the accompanying drawings, the components represented by the reference numerals are described as follows:

[0024] State data monitoring and acquisition module 10, state change data prediction module 20, heat dissipation control parameter optimization module 30. DETAILED DESCRIPTION

[0025] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0026] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the specified features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0027] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or illustration". Any embodiment of the present invention described as "for example" is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any person skilled in the art to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed herein.

[0028] Example 1, as Figure 1 As shown, an embodiment of the present invention provides a heat dissipation control method for a domestic switch, which specifically includes the following steps:

[0029] P10: Monitors and obtains the current real-time temperature, real-time cooling control parameters, and accumulated switch noise of the switch.

[0030] Furthermore, step P10 of the present invention further includes:

[0031] P11: Monitor and obtain the real-time temperature of the switch through the temperature sensor configured on the switch; P12: Obtain the current real-time heat dissipation control parameters of the switch, where the heat dissipation control parameters include fan speed; P13: Monitor and obtain the switch noise within the past preset time interval through the noise sensor configured on the switch, and calculate the average to obtain the cumulative switch noise.

[0032] Specifically, the technical solution provided by the present invention is applicable to scenarios such as office computer rooms and factory computer rooms. In these environments, in addition to paying attention to the temperature control of the equipment, noise is also a key factor. Especially in an office environment, noise may affect the work efficiency and comfort of employees. Therefore, by monitoring temperature and noise and adjusting the heat dissipation control parameters (such as fan speed) in real time, it is possible to ensure efficient heat dissipation of the equipment while reducing noise pollution and improving the comfort of the overall working environment.

[0033] First, the switch's operating temperature is monitored in real time using a temperature sensor configured on the switch. This temperature sensor regularly collects the device's current operating temperature data and transmits it to the control system in real time. By acquiring real-time temperature data, we can accurately monitor device temperature changes and promptly detect overheating. The switch's current real-time cooling control parameters are obtained, including fan speed. Fan speed is a key factor affecting the switch's cooling performance. By monitoring fan speed, we can understand the current cooling system's operating intensity. For example, low fan speed may result in insufficient cooling.

[0034] By configuring a noise sensor on the switch, the switch noise within a preset time interval (such as 10 minutes) in the past is monitored and obtained. The preset time interval can be set according to the actual scenario. The noise sensor will continuously record the noise data around the switch and calculate the average noise within the past time period (such as 10 minutes) to obtain the cumulative switch noise.

[0035] P20: Obtain the data transmission records of the switch within a preset time range in the past, perform calculation and prediction of future data transmission, obtain predicted data transmission parameters, and perform temperature change prediction and environmental noise prediction in combination with real-time temperature and real-time heat dissipation control parameters to obtain predicted temperature and predicted environmental noise, and calculate the configuration noise coefficient in combination with the accumulated switch noise.

[0036] Furthermore, step P20 of the present invention further includes:

[0037] P21: Get the data throughput sequence of the switch within the preset time range in the past.

[0038] Specifically, the data throughput sequence of the switch within a preset time range (such as 10 minutes) in the past is obtained. For example, the data throughput record of the switch within the preset time range in the past is obtained through the network interface or monitoring system of the switch. The data throughput is usually expressed in bytes transmitted per second (bps). Collecting this data can help analyze the load of the switch in different time periods. Among them, network data transmission in offices or factories has certain regularity. For example, the data throughput may be larger during the peak working hours in the morning and evening, while it is relatively stable at noon or at night. Through historical data analysis, the changing pattern of data transmission can be identified.

[0039] P22: Pre-trained data transmission predictor for predicting future data transmission parameters.

[0040] Furthermore, step P22 of the present invention further includes:

[0041] P221: Based on the switch operation data in the historical period, a set of sample data throughput sequences is collected, and the data throughput of the switch at future moments after different sample data throughput sequences is collected to obtain a set of sample predicted data throughputs; P222: Using the sample data throughput sequence set and the sample predicted data throughput set, a data transmission predictor is trained based on machine learning, and tested until convergence.

[0042] Specifically, based on historical switch operation data (e.g., the past month), data throughput sequences are collected within different time intervals. Data throughput is typically expressed in bytes per second or bit rate. By collecting throughput data within a certain period of time (e.g., the past few hours or days), a series of sample data throughput sequence sets are formed. Next, the switch data throughput at future moments (e.g., 10 minutes later) after different sample data throughput sequences is collected and set as the sample predicted data throughput. This yields a sample predicted data throughput set, where the sample data throughput sequences and the sample predicted data throughputs correspond one-to-one. The data throughput of switches within a certain time range exhibits certain patterns. For example, the amount of data exchanged and transmitted within an office each day is relatively similar. Therefore, based on past data throughput sequences, the data throughput at future moments can be predicted as a parameter for predicting data transmission.

[0043] Then, a data transmission predictor is constructed based on the BP neural network. The data transmission predictor is used to predict the switch data throughput at a future time based on the historical data throughput sequence. By inputting the historical data sequence, the predictor can provide the data throughput value for a period of time in the future (for example, 10 minutes later). The data transmission predictor includes an input layer, multiple hidden layers, and an output layer. The input data of the input layer is the data throughput sequence, the hidden layer is used for feature conversion and pattern learning, and the output data of the output layer is the predicted data throughput. The sample data throughput sequence set and the sample predicted data throughput set are then used as training data, and the training data is divided into a training set, a validation set, and a test set according to a predetermined ratio. For example, the training set accounts for 70% and is used to train the model and adjust the network weights and biases; the validation set accounts for 20% and is used to adjust the model's hyperparameters to avoid overfitting. The test set, which accounts for 10%, is used to evaluate the model's final performance. The data transmission predictor undergoes supervised training, validation, and testing using the sample data throughput sequence as input data and the sample predicted data throughput as supervision data. During training, the neural network attempts to predict future data throughput by learning the relationship between the input data and the target output using the sample data throughput sequence and the sample predicted data throughput as supervision data. In each iteration, the data transmission predictor calculates the error between the predicted value and the actual target (i.e., the sample predicted data throughput) and uses the backpropagation algorithm to adjust the network parameters (weights and biases) to optimize the prediction effect. The mean squared error (MSE) is used as the loss function to measure the difference between the predicted value and the actual predicted data throughput. The goal is to minimize the loss function so that the model can accurately predict future data throughput. During validation, the error difference between the training and validation sets is monitored. If the model has a small error on the training set but a large error on the validation set, it may be overfitting and requires optimization through regularization or adjustment of the network structure. After training is completed and verified, the final model is evaluated using the test set. The data in the test set has not been seen before and is used to evaluate the model's generalization ability on unknown data. At this time, the model no longer adjusts parameters, but makes predictions based on the weights obtained through training. The prediction accuracy of the model is evaluated by calculating the error of the model on the test set. If the prediction accuracy meets the expected indicators, the training converges and a trained data transmission predictor is obtained.

[0044] By building a data transmission predictor based on machine learning, the data throughput at future moments can be accurately predicted, providing a scientific basis for subsequent heat dissipation control.

[0045] P23: Input the data throughput sequence into the data transmission predictor to predict the data throughput at a preset time in the future as a predicted data transmission parameter.

[0046] Specifically, the data throughput sequence is then input into a trained data transmission predictor. The predictor calculates and predicts the data throughput at a preset time in the future (e.g., 10 minutes later) and uses the predicted data throughput as a predicted data transmission parameter. The predicted data throughput is an estimate of the future switch load, which can provide strong data support for the heat dissipation control system, thereby achieving more accurate temperature control and noise control.

[0047] Furthermore, step P20 of the present invention further includes:

[0048] P24: Obtain the current real-time data throughput of the switch; P25: Collect a sample real-time data throughput set, a sample real-time temperature set, a sample real-time heat dissipation control parameter set, and a sample future data throughput set, and collect the switch temperature at future times under different sample real-time data throughputs, sample real-time temperatures, sample real-time heat dissipation control parameters, and sample future data throughputs to obtain a sample predicted temperature set; P26: Use the sample real-time data throughput set, sample real-time temperature set, sample real-time heat dissipation control parameter set, sample future data throughput set, and sample predicted temperature set to train a temperature change predictor; P27: Input the real-time temperature, real-time data throughput, real-time heat dissipation control parameter, and predicted throughput into the temperature change predictor, and obtain the predicted temperature through prediction output.

[0049] Specifically, a set of sample real-time data throughput, a set of sample real-time temperature, a set of sample real-time cooling control parameters, and a set of sample future data throughput are collected. The sample real-time data throughput reflects the real-time data transmission volume (throughput) of the switch at different time points. This data typically comes from the switch's monitoring system and indicates changes in the switch's load. The sample real-time temperature reflects the actual operating temperature of the switch at different time points. Temperature data is obtained from the switch's temperature sensor. The sample real-time cooling control parameters refer to the switch's real-time fan speed at different time points. These parameters are collected through the switch's cooling system. The sample future data throughput is the predicted value of data throughput at a certain time in the future (e.g., 10 minutes later). The switch temperature at a future time (e.g., 10 minutes later) is further collected under different sample real-time data throughput, sample real-time temperature, sample real-time cooling control parameters, and sample future data throughput as the sample predicted temperature, thereby obtaining a set of sample predicted temperatures.

[0050] Then, a temperature change predictor is constructed based on the BP neural network. The temperature change predictor is a BP neural network model that can be iteratively optimized in machine learning, including an input layer, multiple hidden layers and an output layer. The input data of the input layer are real-time temperature, real-time data throughput, real-time heat dissipation control parameters and predicted throughput, and the output data of the output layer is the predicted temperature. Then, the temperature change predictor is supervised and trained using sample real-time data throughput, sample real-time temperature, sample real-time heat dissipation control parameter, and sample future data throughput as input data and sample predicted temperature as supervision data. The sample real-time data throughput set, sample real-time temperature set, sample real-time heat dissipation control parameter set, sample future data throughput set, and sample predicted temperature set as training data. First, the input data is passed into the input layer, and after weights and activation functions are calculated in each hidden layer, the network output, i.e., the predicted temperature, is finally obtained. Next, the sample predicted temperature set (i.e., target data) is compared with the predicted temperature output by the network to calculate the prediction error. The commonly used error calculation method is mean square error. Then, the gradient of each weight and bias is calculated through the backpropagation algorithm, and the network parameters are updated through the gradient descent method. The goal is to minimize the loss function and minimize the network prediction error. Iterative training is repeated, and the weights and biases in the network are gradually adjusted through forward propagation and backpropagation, so that the network can predict the temperature at a future time based on the input data (real-time throughput, temperature, heat dissipation control parameter, and predicted throughput). After multiple rounds of training, training is stopped until the loss function converges, and the trained temperature change predictor is obtained.

[0051] Then, the current real-time data throughput of the switch is obtained. That is, the current data throughput of the switch is collected in real time through a monitoring system configured on the switch. The data throughput reflects the load status of the switch and is usually obtained through a network traffic statistics module. The data unit is usually bps (bits per second). The real-time data throughput directly affects the heat dissipation requirements of the switch. The greater the throughput, the heavier the load on the switch, and the more heat will be generated. Then, the real-time temperature, real-time data throughput, real-time heat dissipation control parameters and predicted throughput are input into a temperature change predictor, and the predicted output is a predicted temperature. The predicted temperature reflects the estimated temperature value of the switch at a future time under given load, heat dissipation configuration and environmental conditions.

[0052] P28: Perform environmental noise prediction based on the predicted data transmission parameters to obtain predicted environmental noise.

[0053] Furthermore, step P28 of the present invention further includes:

[0054] P281: Based on the noise monitoring data in the environment, collect a set of sample predicted data transmission parameters, and collect the average environmental noise in the environment under different sample predicted data transmission parameters to obtain a sample environmental noise set; P282: Construct a mapping relationship between the sample predicted data transmission parameter set and the sample environmental noise set to obtain an environmental noise prediction table; P283: Input the predicted data transmission parameters into the environmental noise prediction table, and obtain the predicted environmental noise through mapping and classification.

[0055] Specifically, based on the noise monitoring data in the environment, the predicted data transmission parameters at different time points are collected to obtain a set of sample predicted data transmission parameters; then the average environmental noise in the environment under different sample predicted data transmission parameters is collected. The average environmental noise is calculated based on historical noise monitoring results, reflecting the noise level in the environment. The data unit is decibel (dB). Different data transmission parameters will lead to different degrees of environmental noise, especially in high-load scenarios such as offices or factories. The average environmental noise is set as the sample environmental noise to obtain a set of sample environmental noise.

[0056] Environmental noise refers to the noise level within an office or factory, while switch noise refers to the noise level monitored in the area near the switch. Larger data transmission parameters indicate busier production activities within the office or factory, and thus, greater environmental noise levels. Based on this, the predicted data transmission parameters are used to predict the environmental noise level at a future time.

[0057] The decision tree is a commonly used classification and regression model that classifies or predicts input data through a series of conditional judgments. A mapping relationship between sample predicted data transmission parameters and sample environmental noise is then established. Based on the decision tree principle, the sample predicted data transmission parameters are used as child nodes, the corresponding sample environmental noise is used as the leaf node of the child node, and the sample predicted data transmission parameter set and the sample environmental noise set are used as training data to construct an environmental noise prediction table. The training process is as follows: First, by calculating the feature importance of each predicted data transmission parameter, the parameter that can most effectively distinguish environmental noise is selected as the split node of the decision tree; then, based on the selected split feature, the training data set is divided, and each divided subset corresponds to a specific predicted data transmission parameter value; then, for each leaf node, a corresponding environmental noise value is assigned. These noise values come from the sample environmental noise set, and the mean or weighted average can be used to represent the environmental noise value of the leaf node.

[0058] Finally, the predicted data transmission parameters are input into the environmental noise prediction table for matching. By looking up the corresponding entries in the table, the predicted data transmission parameters are matched with the corresponding environmental noise, and the predicted environmental noise is obtained by mapping and classification.

[0059] Furthermore, step P20 of the present invention further includes:

[0060] P29: Obtain the historical average noise in the environment; P210: Calculate the configuration noise coefficient based on the accumulated switch noise, the historical average noise, and the predicted environmental noise, as shown in the following formula:

[0061] ;

[0062] in, is the noise coefficient, is the historical average noise, is the cumulative switch noise, To predict environmental noise.

[0063] Specifically, first, the historical average noise level in the environment is obtained, that is, the average environmental noise level over a period of time (e.g., the last month). Then, based on the accumulated switch noise, the historical average noise level, and the predicted environmental noise level, a configuration noise coefficient is calculated. The noise coefficient is the ratio of the historical average noise level to the sum of the accumulated switch noise level and the predicted environmental noise level. The noise coefficient is used to configure the degree of attention to noise control in subsequent optimization of heat dissipation control parameters. The greater the accumulated switch noise level and the greater the predicted environmental noise level, the greater the recent noise level experienced by office or factory personnel and the future noise level in the environment. This indicates that the personnel's future noise tolerance is greater, and the lower the noise coefficient. Consequently, when optimizing heat dissipation control parameters, less attention will be paid to noise control and more attention will be paid to temperature control. A higher noise coefficient indicates a lower noise tolerance in the environment, requiring stronger noise control during optimization. Specifically, when adjusting heat dissipation control parameters, more consideration will be given to noise control. A lower noise coefficient indicates a higher noise tolerance in the environment, and optimization can prioritize temperature control over adjustment of noise control.

[0064] Among them, when the predicted data transmission parameters are larger, the production activities are busier, and the predicted environmental noise is greater, it is more important to ensure the stable operation of the switch, pay more attention to the temperature in its heat dissipation control, and pay less attention to the noise control in the heat dissipation control, the smaller the noise coefficient.

[0065] P30: Based on the predicted temperature and noise coefficient, the real-time heat dissipation control parameters are optimized and adjusted to obtain the optimal heat dissipation control parameters and perform heat dissipation control, wherein the optimization step size is configured according to the variation range between the predicted temperature and the real-time temperature.

[0066] Furthermore, step P30 of the present invention further includes:

[0067] P31: Calculate the temperature deviation between the predicted temperature and the real-time temperature as the optimization step size; P32: Use the optimization step size to adjust the real-time heat dissipation control parameter to obtain a first heat dissipation control parameter; P33: Combine the first heat dissipation control parameter with the real-time temperature, real-time data throughput, and predicted throughput and input it into the temperature change predictor, and predict the output to obtain a first control temperature; P34: Obtain the first switch noise under the first heat dissipation control parameter, and calculate the heat dissipation control fitness based on the first control temperature and noise coefficient, as shown in the following formula:

[0068] ;

[0069] in, To control the adaptability of heat dissipation, is the first control temperature, is the standard temperature, is the noise coefficient, is a small real number, avoiding the denominator being 0, For the first switch noise, is standard noise; P35: continue to use the optimization step size to optimize and adjust the first heat dissipation control parameter until convergence, output the optimal heat dissipation control parameter with the maximum heat dissipation control fitness, and perform heat dissipation control.

[0070] Specifically, first, the temperature deviation amplitude between the predicted temperature and the real-time temperature is calculated. The temperature deviation amplitude is the ratio of the temperature difference between the predicted temperature and the real-time temperature to the real-time temperature. The temperature deviation amplitude is used as the optimization step size. If the temperature deviation is small, it indicates that the current temperature is close to the target value, and the heat dissipation control can be fine-tuned to ensure that the switch operates at the optimal temperature. If the temperature deviation is large, it indicates that the current temperature is far from the target temperature and requires a larger adjustment, such as increasing the fan speed to quickly reduce the temperature. The real-time heat dissipation control parameter is then adjusted using the optimization step size, such as by multiplying the real-time heat dissipation control parameter by 1 plus the optimization step size to obtain a first heat dissipation control parameter. The first heat dissipation control parameter is then combined with the real-time temperature, real-time data throughput, and predicted throughput and input into the temperature change predictor for temperature prediction, outputting a first control temperature.

[0071] Further, the noise of the first switch under the first heat dissipation control parameters is obtained, that is, the noise generated by the switch under the use of the first heat dissipation control parameters can be obtained through historical data query; then, a heat dissipation control fitness evaluation function is constructed, and the heat dissipation control fitness evaluation function is used to calculate the first heat dissipation control fitness based on the first switch noise, the first control temperature and the noise coefficient. The heat dissipation control fitness is an indicator used to measure the current heat dissipation control effect, comprehensively considering the two factors of temperature control and noise control. The larger the value, the better the adaptability.

[0072] Then, the optimization step size is continued to be used to optimize and adjust the first heat dissipation control parameter to obtain the second heat dissipation control parameter, and the second heat dissipation fitness of the second heat dissipation control parameter is calculated; the iterative adjustment of the heat dissipation control parameter and the control fitness calculation are continued until a predetermined number of iterations is reached, and the optimization converges, and a plurality of heat dissipation control parameters and corresponding plurality of heat dissipation control fitnesses are obtained, and the heat dissipation control parameter corresponding to the maximum heat dissipation control fitness is selected as the optimal heat dissipation control parameter; finally, heat dissipation control is performed according to the optimal heat dissipation control parameter.

[0073] By calculating and optimizing the heat dissipation control adaptability, the heat dissipation control parameters can be dynamically adjusted while taking both temperature and noise into account, ultimately achieving the optimal heat dissipation control settings. Recent switch noise and future environmental noise are also incorporated to calculate the noise coefficient, optimizing heat dissipation control based on user sensitivity to noise. This method not only effectively improves the heat dissipation efficiency of the switch, but also reduces noise pollution, ensuring efficient and stable operation of the equipment while ensuring that environmental noise does not impact personnel or equipment.

[0074] The heat dissipation control method for a domestically produced switch provided by an embodiment of the present invention has at least the following technical effects:

[0075] The current real-time temperature, real-time heat dissipation control parameters and accumulated switch noise of the switch are obtained through monitoring; then the data transmission records of the switch within a preset time range in the past are obtained, and future data transmission calculation predictions are performed to obtain predicted data transmission parameters; then, temperature change predictions and environmental noise predictions are performed based on the predicted data transmission parameters, real-time temperature and real-time heat dissipation control parameters to obtain predicted temperature and predicted environmental noise, and the configuration noise coefficient is calculated in combination with the accumulated switch noise; finally, the real-time heat dissipation control parameters are optimized and adjusted based on the predicted temperature and noise coefficient to obtain optimal heat dissipation control parameters and perform heat dissipation control; the above method can improve the accuracy and rationality of the heat dissipation control parameter setting, achieve dual optimization of temperature control and noise control, avoid excessive or insufficient heat dissipation, thereby effectively improving heat dissipation efficiency, while reducing noise pollution, and ensuring efficient and stable operation of the equipment.

[0076] Example 2, as Figure 2 As shown, based on the same inventive concept as the heat dissipation control method for a domestic switch provided in Example 1, an embodiment of the present invention further provides a heat dissipation control system for a domestic switch, including: a state data monitoring and acquisition module 10, used to monitor and obtain the current real-time temperature, real-time heat dissipation control parameters and accumulated switch noise of the switch; a state change data prediction module 20, used to obtain the data transmission records of the switch within a preset time range in the past, perform calculation and prediction of future data transmission, obtain predicted data transmission parameters, and perform temperature change prediction and environmental noise prediction in combination with the real-time temperature and real-time heat dissipation control parameters to obtain predicted temperature and predicted environmental noise, and calculate the configured noise coefficient in combination with the accumulated switch noise; a heat dissipation control parameter optimization module 30, used to optimize and adjust the real-time heat dissipation control parameters according to the predicted temperature and noise coefficient, obtain optimal heat dissipation control parameters, and perform heat dissipation control, wherein the optimization step size is configured according to the change amplitude of the predicted temperature and the real-time temperature.

[0077] Furthermore, the heat dissipation control system for a domestically produced switch is also used to: monitor and obtain the real-time temperature of the switch through a temperature sensor configured on the switch; obtain the current real-time heat dissipation control parameters of the switch, wherein the heat dissipation control parameters include the fan speed; monitor and obtain the switch noise within a preset time interval in the past through a noise sensor configured on the switch, and calculate the average to obtain the cumulative switch noise.

[0078] Furthermore, the heat dissipation control system for a domestically produced switch is also used to: obtain a data throughput sequence of the switch within a preset time range in the past; pre-train a data transmission predictor for predicting future data transmission parameters; input the data throughput sequence into the data transmission predictor, and predict the data throughput at a preset time in the future as a predicted data transmission parameter.

[0079] Furthermore, the heat dissipation control system for a domestically produced switch is also used to: collect a set of sample data throughput sequences based on the switch operation data in a historical period, and collect the data throughput of the switch at future moments after different sample data throughput sequences to obtain a set of sample predicted data throughputs; use the sample data throughput sequence set and the sample predicted data throughput set to train a data transmission predictor based on machine learning, and conduct testing until convergence.

[0080] Furthermore, the heat dissipation control system for a domestically produced switch is also used to: obtain the current real-time data throughput of the switch; collect a sample real-time data throughput set, a sample real-time temperature set, a sample real-time heat dissipation control parameter set, and a sample future data throughput set, and collect the switch temperature at future times under different sample real-time data throughputs, sample real-time temperatures, sample real-time heat dissipation control parameters, and sample future data throughputs to obtain a sample predicted temperature set; use the sample real-time data throughput set, sample real-time temperature set, sample real-time heat dissipation control parameter set, sample future data throughput set, and sample predicted temperature set to train a temperature change predictor; input the real-time temperature, real-time data throughput, real-time heat dissipation control parameter, and predicted throughput into the temperature change predictor, and output a prediction to obtain a predicted temperature; perform environmental noise prediction based on the predicted data transmission parameters to obtain predicted environmental noise.

[0081] Furthermore, the heat dissipation control system for domestically produced switches is also used to: collect a set of sample predicted data transmission parameters based on noise monitoring data in the environment, and collect the average environmental noise in the environment under different sample predicted data transmission parameters to obtain a sample environmental noise set; construct a mapping relationship between the sample predicted data transmission parameter set and the sample environmental noise set to obtain an environmental noise prediction table; input the predicted data transmission parameters into the environmental noise prediction table, and obtain the predicted environmental noise by mapping and classification.

[0082] Furthermore, the heat dissipation control system for a domestically produced switch is further configured to: obtain a historical average noise level in an environment; and calculate a configuration noise coefficient based on the accumulated switch noise, the historical average noise level, and the predicted environmental noise level, as shown in the following formula:

[0083] ;

[0084] in, is the noise coefficient, is the historical average noise, is the cumulative switch noise, To predict environmental noise.

[0085] Furthermore, the heat dissipation control system for a domestically produced switch is further configured to: calculate a temperature deviation between the predicted temperature and the real-time temperature as an optimization step; use the optimization step to adjust the real-time heat dissipation control parameter to obtain a first heat dissipation control parameter; input the first heat dissipation control parameter into the temperature change predictor in combination with the real-time temperature, real-time data throughput, and predicted throughput, and obtain a first control temperature as a prediction output; obtain the first switch noise under the first heat dissipation control parameter, and calculate the heat dissipation control adaptability based on the first control temperature and noise coefficient, as shown in the following formula:

[0086] ;

[0087] in, To control the adaptability of heat dissipation, is the first control temperature, is the standard temperature, is the noise coefficient, is a small real number, For the first switch noise, is standard noise; continue to use the optimization step size to optimize and adjust the first heat dissipation control parameter until convergence, output the optimal heat dissipation control parameter with the maximum heat dissipation control fitness, and perform heat dissipation control.

[0088] In a third embodiment, based on the same inventive concept as the heat dissipation control method for a domestic switch provided in the first embodiment, an embodiment of the present invention further provides a switch, which can be heat-dissipated and regulated by any one of the heat dissipation control methods for a domestic switch described in the first embodiment when in operation.

[0089] Although preferred embodiments of the present invention have been described, additional changes and modifications to these embodiments may occur to those skilled in the art once the basic inventive concepts become known.

[0090] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the present invention and its equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A heat dissipation control method for a domestic switch, characterized in that the method include: Monitor and obtain the current real-time temperature, real-time heat dissipation control parameters, and accumulated switch noise of the switch, wherein the accumulated switch noise includes an average value of the switch noise within a preset time interval in the past; Obtain data transmission records of the switch within a preset time range in the past, perform calculation and prediction of future data transmission, obtain predicted data transmission parameters, and perform temperature change prediction and environmental noise prediction in combination with real-time temperature and real-time heat dissipation control parameters to obtain predicted temperature and predicted environmental noise. Combined with the accumulated switch noise, calculate the configuration noise coefficient, where the noise coefficient represents the degree of attention paid to noise control, including: Obtaining a historical average noise level in the environment, wherein the historical average noise level includes an average environmental noise level in the past; The configuration noise factor is calculated based on the accumulated switch noise, historical average noise, and predicted environmental noise, as shown in the following formula: ； in, is the noise coefficient, is the historical average noise, is the cumulative switch noise, To predict environmental noise; According to the predicted temperature and noise coefficient, the real-time heat dissipation control parameters are optimized and adjusted to obtain optimal heat dissipation control parameters, and heat dissipation control is performed, wherein the optimization step size is configured according to the variation range between the predicted temperature and the real-time temperature, wherein the ratio of the temperature difference between the predicted temperature and the real-time temperature to the real-time temperature is calculated to obtain the temperature deviation range as the optimization step size, including: Calculating a temperature deviation between the predicted temperature and the real-time temperature as an optimization step size; Using the optimization step size, adjusting the real-time heat dissipation control parameter to obtain a first heat dissipation control parameter; The first heat dissipation control parameter is combined with the real-time temperature, the real-time data throughput, and the predicted throughput and inputted into the temperature change predictor, and the prediction output is used to obtain a first control temperature; The noise of the first switch under the first heat dissipation control parameter is obtained, and the heat dissipation control adaptability is calculated according to the first control temperature and the noise coefficient, as shown in the following formula: ； in, The heat dissipation control fitness is used to measure the heat dissipation control effect. The heat dissipation control fitness is positively correlated with the heat dissipation control effect. is the first control temperature, is the standard operating temperature of the switch, X is a small real number, is the noise coefficient, For the first switch noise, Standard noise for switch operation; Continue to use the optimization step size to optimize and adjust the first heat dissipation control parameter until convergence, output the optimal heat dissipation control parameter with the maximum heat dissipation control fitness, and perform heat dissipation control.

2. The heat dissipation control method for a domestically produced switch according to claim 1, characterized in that: Monitor and obtain the current real-time temperature, real-time cooling control parameters, and accumulated switch noise of the switch, including: Monitor and obtain the real-time temperature of the switch through the temperature sensor configured on the switch; Obtain the current real-time heat dissipation control parameters of the switch, where the heat dissipation control parameters include fan speed; The noise sensor configured on the switch is used to monitor and obtain the switch noise within a preset time interval in the past, and the average is calculated to obtain the cumulative switch noise.

3. The heat dissipation control method for a domestically produced switch according to claim 1, characterized in that: Obtaining data transmission records of the switch within a preset time range in the past and making future data transmission predictions, including: Obtain the data throughput sequence of the switch within a preset time range in the past; Pre-training a data transmission predictor for predicting future data transmission parameters; The data throughput sequence is input into the data transmission predictor to predict the data throughput at a preset time in the future as a predicted data transmission parameter.

4. The heat dissipation control method for a domestically produced switch according to claim 3, characterized in that: Pre-training a data transmission predictor for predicting future data transmission parameters, including: Based on the switch operation data in the historical time, a set of sample data throughput sequences is collected, and the data throughput of the switch at future moments after different sample data throughput sequences is collected to obtain a set of sample predicted data throughputs; The sample data throughput sequence set and the sample predicted data throughput set are used to train a data transmission predictor based on machine learning, and the training is performed until convergence.

5. The heat dissipation control method for a domestically produced switch according to claim 1, characterized in that: Perform temperature change prediction and environmental noise prediction to obtain predicted temperature and predicted environmental noise, including: Get the current real-time data throughput of the switch; Collecting a sample real-time data throughput set, a sample real-time temperature set, a sample real-time heat dissipation control parameter set, and a sample future data throughput set, and collecting the switch temperature at future moments under different sample real-time data throughputs, sample real-time temperatures, sample real-time heat dissipation control parameters, and sample future data throughputs to obtain a sample predicted temperature set, wherein the sample real-time data throughput set includes the real-time data transmission volume at different time points, and the sample future data throughput set includes the data throughput prediction values at future moments after different time points; Training a temperature change predictor using the sample real-time data throughput set, the sample real-time temperature set, the sample real-time heat dissipation control parameter set, the sample future data throughput set, and the sample predicted temperature set; Inputting the real-time temperature, real-time data throughput, real-time heat dissipation control parameter and predicted throughput into a temperature change predictor, and outputting a prediction to obtain a predicted temperature; An environmental noise prediction is performed according to the predicted data transmission parameters to obtain the predicted environmental noise.

6. The heat dissipation control method for a domestically produced switch according to claim 5, characterized in that: Performing environmental noise prediction according to the predicted data transmission parameters to obtain predicted environmental noise includes: According to the noise monitoring data in the environment, a sample prediction data transmission parameter set is collected, and the average environmental noise in the environment under different sample prediction data transmission parameters is collected to obtain a sample environmental noise set, wherein the larger the sample data transmission parameter, the greater the sample environmental noise; Constructing a mapping relationship between the sample prediction data transmission parameter set and the sample environmental noise set to obtain an environmental noise prediction table; The predicted data transmission parameters are input into the environmental noise prediction table, and the predicted environmental noise is obtained by mapping and classification.

7. A heat dissipation control system for a domestically produced switch, characterized in that: The steps for implementing the heat dissipation control method for a domestically produced switch according to any one of claims 1 to 6 include: Status data monitoring and acquisition module, used to monitor and obtain the current real-time temperature, real-time heat dissipation control parameters and accumulated switch noise of the switch; A state change data prediction module is used to obtain the data transmission records of the switch within a preset time range in the past, perform calculation predictions on future data transmission, obtain predicted data transmission parameters, and perform temperature change prediction and environmental noise prediction in combination with real-time temperature and real-time heat dissipation control parameters. The predicted temperature and predicted environmental noise are obtained, and the configuration noise coefficient is calculated in combination with the accumulated switch noise; The heat dissipation control parameter optimization module is used to optimize and adjust the real-time heat dissipation control parameters according to the predicted temperature and noise coefficient, obtain the optimal heat dissipation control parameters, and perform heat dissipation control, wherein the optimization step size is configured according to the change range between the predicted temperature and the real-time temperature.

8. A switch, characterized in that: When the switch is in operation, heat dissipation can be regulated by the heat dissipation control method for a domestically produced switch according to any one of claims 1 to 6.

Images