Environment-adaptive temperature control method for intelligent cabinet

Abstract

An environment-adaptive temperature control method for an intelligent cabinet, comprising the following steps: S1, when an acquisition end obtains position information of each cooling fan of an intelligent cabinet, dividing the intelligent cabinet into a plurality of monitoring regions on the basis of the position information of the cooling fans; S2, acquiring temperature conditions of the monitoring regions in real time, analyzing the temperature state of each monitoring region by means of an edge computing device, and then determining which monitoring regions are to be marked as hotspot regions; S3, obtaining current operating condition data of the intelligent cabinet, and matching a current working environment of the intelligent cabinet on the basis of a database; S4, analyzing, by means of a correlation analysis algorithm, a degree of influence of the current working environment on each hotspot region, and performing a first temperature control adjustment on the hotspot regions on the basis of the degrees of influence and the temperature condition analysis result; and S5: evaluating a health degree of each cooling fan, and then performing a second temperature control adjustment on the basis of the health degrees of the cooling fans. By evaluating the degrees of influence of different environmental factors on hotspot regions, the temperature control strategy not only relies on temperature data, but also comprehensively considers environmental factors, thereby optimizing the fan power adjustment mode, and improving adaptability.

Description

A Smart Cabinet Environmental Adaptive Temperature Control Method Technical Field

[0001] This invention relates to the field of cabinet temperature control technology, specifically to an intelligent cabinet environment adaptive temperature control method. Background Technology

[0002] Intelligent cabinets are widely used in data centers, communication base stations, industrial control and other fields to house servers, switches, routers, storage devices and other electronic devices. These devices generate a lot of heat during operation, which causes the temperature inside the cabinet to rise. If the temperature is not properly controlled, it may cause the equipment to overheat, degrade performance or even be damaged, affecting the stability and reliability of the system. Therefore, temperature control of intelligent cabinets has become an important part of ensuring the long-term stable operation of the equipment. Technical issues

[0003] The existing technology has the following drawbacks:

[0004] 1. Traditional temperature control methods are usually based on preset fixed rules for temperature control and adjustment, such as setting a temperature threshold. When the cabinet temperature exceeds the threshold, the fan is turned on or the cooling intensity is increased. However, they fail to adapt to different environmental conditions. When factors such as different seasons, data center layout, equipment operation mode, and load status change, traditional methods cannot dynamically adjust the temperature control strategy, resulting in insufficient heat dissipation in some environments and excessive heat dissipation and increased energy consumption in other environments, thus reducing the adaptability of intelligent cabinets in different environments.

[0005] 2. Existing temperature control methods usually adjust fan power based solely on temperature sensor data without assessing the health status of each fan. Due to long-term operation of the cabinet, some fans may experience health abnormalities, resulting in the actual heat dissipation effect failing to meet expectations even when the fan speed is increased. This may even affect the operation of other fans, thus failing to achieve the actual temperature control objective (i.e., unsatisfactory heat dissipation).

[0006] Based on this, the present invention proposes an intelligent cabinet environment adaptive temperature control method. By evaluating the impact of different environmental factors on hotspot areas, the temperature control strategy not only relies on temperature data, but also comprehensively considers environmental factors, optimizes the fan power adjustment method, and improves adaptability.

[0007] The purpose of this invention is to provide an intelligent cabinet environment adaptive temperature control method to address the shortcomings of the prior art. Technical solutions

[0008] To achieve the above objectives, the present invention provides the following technical solution: an intelligent cabinet environment adaptive temperature control method, the temperature control method comprising the following steps:

[0009] S1: After the acquisition terminal obtains the location information of each cooling fan in the smart cabinet, it divides the smart cabinet into several monitoring areas based on the cooling fan location information.

[0010] S2: Real-time collection of temperature data in the monitoring area; analysis of the temperature data in each monitoring area using edge computing devices; and determination of which monitoring areas are marked as hotspots.

[0011] S3: Obtain the current operating status data of the smart cabinet and match the current working environment of the smart cabinet based on the database;

[0012] S4: Use a correlation analysis algorithm to analyze the impact of the current working environment on each hot spot area, and adjust the temperature of the hot spot area based on the impact and temperature conditions.

[0013] S5: After assessing the health of each cooling fan, perform secondary temperature control adjustments based on the health of the cooling fans.

[0014] In a preferred embodiment, the hotspot area is subjected to a temperature control adjustment based on the analysis results of the degree of influence and temperature conditions. The temperature control adjustment is a fan power adjustment, including the following steps:

[0015] The temperature index is calculated based on real-time temperature values, cumulative temperature rise index, and temperature fluctuation index. An environmental factor is obtained by summing the normalized operating load value and the normalized ambient temperature value. The fan power in hotspot areas is then adjusted by combining the environmental factor, the degree of influence, and the temperature index. The adjustment algorithm is as follows: In the formula, hotspot areas Fan power after one adjustment hotspot areas Initial fan power, Temperature index Indicates the current environment's impact on hotspot areas The extent of the impact As environmental factors, The first change threshold, This is the second change threshold.

[0016] In a preferred embodiment, the logic for obtaining the temperature index is as follows: The real-time temperature value, the cumulative temperature rise index, and the temperature fluctuation index are normalized to map their values ​​to the range [0,1]. The normalized real-time temperature value, cumulative temperature rise index, and temperature fluctuation index are then used to calculate the temperature index of the hotspot area. The calculation expression is: In the formula, Temperature index This is the normalized value of real-time temperature. This is the normalized value of the cumulative temperature rise index. This is the normalized value of the temperature fluctuation index;

[0017] The logic for obtaining the environmental factors is as follows: Obtain the current environmental data of the intelligent cabinet, including the normalized value of the operating load and the normalized value of the ambient temperature; sum the normalized values ​​of the operating load and the ambient temperature to obtain the environmental factors; compare the obtained environmental factors with a first change threshold and a second change threshold; if the environmental factors are greater than or equal to the first change threshold and less than or equal to the second change threshold, it is predicted that the environmental factors have no impact on the hotspot area; if the environmental factors are less than the first change threshold, it is predicted that the environmental factors will cause the temperature of the hotspot area to show a downward trend; if the environmental factors are greater than the second change threshold, it is predicted that the environmental factors will cause the temperature of the hotspot area to show an upward trend.

[0018] In a preferred embodiment, a correlation analysis algorithm is used to analyze the impact of the current working environment on each hotspot area, including the following steps:

[0019] Vectorize the temperature data of each hotspot region. Given n hotspot regions, construct a temperature data matrix: In the formula, Temperature-related data representing the i-th hotspot region;

[0020] Vectorize all environmental factor data to construct an environmental factor data matrix:

[0021] In the formula, Let j represent the data value of the j-th environmental factor. After calculating the historical correlation between each environmental factor and the temperature of each hotspot area using the Pearson correlation coefficient, the correlation coefficient is obtained. The correlation coefficients of all environmental factors to each hotspot area are calculated, and the correlation matrix R is constructed.

[0022] Based on the correlation coefficient between each environmental factor and the hotspot area, the degree of influence of the current environment on the hotspot area is calculated. The greater the degree of influence, the greater the impact of the intelligent cabinet on the temperature data change of the hotspot area when the current environmental factors are running.

[0023] In a preferred embodiment, the correlation coefficient is obtained by calculating the historical correlation between each environmental factor and the temperature of each hotspot area using the Pearson correlation coefficient, and the expression is as follows:

[0024] In the formula, This represents the correlation coefficient between the temperature data of the i-th hotspot area and the j-th environmental factor. , They are and The mean;

[0025] Calculate the correlation coefficients of all environmental factors with each hotspot area, and construct the correlation matrix R:

[0026] Based on the correlation coefficient between each environmental factor and the hotspot area, the degree of impact of the current environment on the hotspot area is calculated, expressed as: In the formula, Indicates the current environment's impact on hotspot areas The extent of the impact This refers to the amount of environmental data.

[0027] In a preferred embodiment, after obtaining the current operating status data of the intelligent cabinet, the current working environment of the intelligent cabinet is matched based on the database, including the following steps:

[0028] Real-time operational data, including load conditions and environmental factors, is obtained from sensors and management systems inside the cabinet.

[0029] Vectorize all data to form the current running state vector:

[0030] In the formula, This represents the current operating state vector of the server rack. For the amount of environmental data, This is the i-th environmental data value;

[0031] Extract historical operational data from the database and construct a historical environment vector set: In the formula, The number of historical operational data points is represented by each historical environment vector, which contains the same data dimensions as the current vector, indicating the rack operating environment at different time periods.

[0032] Calculate the similarity between the current running state vector and the historical environment vector, and select the historical environment vector with the highest similarity.

[0033] In a preferred embodiment, the similarity between the current running state vector and the historical environment vector is calculated, expressed as: In the formula, Let be the similarity between the current running state vector and the i-th historical environment vector. The dot product of the current running state vector and the i-th historical environment vector. Let the norm of the current running state vector be . Let the norm of the i-th historical environment vector be denoted by . Calculate the similarity between the current vector and all historical vectors to obtain a similarity list:

[0034] Select the historical environment vector with the highest similarity: In the formula, To match the historical context.

[0035] In a preferred embodiment, determining which monitoring areas should be marked as hotspot areas includes the following steps:

[0036] Temperature data is collected in real time by temperature sensors and transmitted to edge computing devices for processing. Interference noise is removed by moving average filtering or Kalman filtering algorithms, and temperature data from different temperature sensors are synchronized in time.

[0037] Determine whether the monitored area needs to be marked as a hotspot based on real-time temperature values, cumulative temperature rise index, and temperature fluctuation index.

[0038] If the real-time temperature value is greater than the preset temperature threshold, it is determined that the monitoring area needs to be marked as a hotspot area.

[0039] If the real-time temperature value is less than or equal to the preset temperature threshold, the cumulative temperature rise index is greater than the preset rise threshold, and the temperature fluctuation index is less than or equal to the preset fluctuation threshold, it is determined that the monitoring area needs to be marked as a hotspot area.

[0040] In a preferred embodiment, the formula for calculating the cumulative temperature rise index is: In the formula, This is the cumulative temperature rise index. The temperature value at time t. This is the reference temperature for the server rack. For the monitoring period;

[0041] The formula for calculating the temperature fluctuation index is as follows: In the formula, The number of data points within the time window. This is the temperature fluctuation index. Let i be the temperature value at the i-th time point. This represents the average temperature value.

[0042] In a preferred embodiment, after acquiring the location information of each cooling fan in the intelligent cabinet, the data acquisition terminal divides the intelligent cabinet into several monitoring areas based on the cooling fan location information, including the following steps:

[0043] By using the distribution sensors of the cooling fans inside the cabinet or the equipment layout database, the installation position, airflow direction, and wind speed adjustment range of all fans are obtained, and the three-dimensional coordinate model of the intelligent cabinet is obtained through the database, and the fan positions are located in the three-dimensional coordinate model.

[0044] The cooling impact coverage area of ​​the fans is analyzed using fluid dynamics simulation or wind field modeling algorithms. The heat dissipation impact radius of each fan is recorded, and the cabinet is divided into several monitoring areas based on the heat dissipation impact radius. Beneficial effects

[0045] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0046] 1. This invention analyzes the temperature status of each monitoring area using edge computing devices to determine which areas are marked as hotspots. After acquiring the current operating status data of the intelligent cabinet, it matches the current working environment of the intelligent cabinet to a database and uses a correlation analysis algorithm to analyze the impact of the current working environment on each hotspot area. Based on the impact level and temperature status analysis results, it performs a first temperature control adjustment on the hotspot areas. After assessing the health of each cooling fan, it performs a second temperature control adjustment based on the fan health. This temperature control method, by evaluating the impact of different environmental factors on hotspot areas, ensures that the temperature control strategy not only relies on temperature data but also comprehensively considers environmental factors, optimizing fan power adjustment methods and improving adaptability.

[0047] 2. This invention assesses the health of each cooling fan and then performs secondary temperature control adjustments based on the fan health status. This secondary temperature control mechanism involves rebalancing the fan load across hotspot areas after the initial adjustment, ensuring that high-efficiency fans handle more cooling tasks, while reducing the load on inefficient fans or issuing maintenance alerts. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0049] Figure 1 is a flowchart of the method of the present invention.

[0050] Figure 2 is a system architecture diagram of the present invention. Embodiments of the present invention

[0051] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] Example 1: Please refer to Figure 1. This example describes an intelligent cabinet environment adaptive temperature control method, which includes the following steps:

[0053] S1: After the acquisition terminal obtains the location information of each cooling fan in the smart cabinet, it divides the smart cabinet into several monitoring areas based on the cooling fan location information.

[0054] S2: Real-time collection of temperature data in the monitoring area; analysis of the temperature data in each monitoring area using edge computing devices; and determination of which monitoring areas are marked as hotspots.

[0055] S3: Obtain the current operating status data of the smart cabinet and match the current working environment of the smart cabinet based on the database;

[0056] S4: Use a correlation analysis algorithm to analyze the impact of the current working environment on each hot spot area, and adjust the temperature of the hot spot area based on the impact and temperature conditions.

[0057] S5: After assessing the health of each cooling fan, perform secondary temperature control adjustments based on the health of the cooling fans.

[0058] This application analyzes the temperature status of each monitoring area using edge computing devices to determine which areas are marked as hotspots. After acquiring the current operating status data of the intelligent cabinet, it matches the current working environment of the intelligent cabinet against a database and uses a correlation analysis algorithm to analyze the impact of the current working environment on each hotspot area. Based on the impact level and temperature status analysis results, it performs a first temperature control adjustment on the hotspot areas. After assessing the health of each cooling fan, it performs a second temperature control adjustment based on the fan health. This temperature control method, by evaluating the impact of different environmental factors on hotspot areas, ensures that the temperature control strategy not only relies on temperature data but also comprehensively considers environmental factors, optimizing fan power adjustment methods and improving adaptability.

[0059] This application assesses the health of each cooling fan and then performs secondary temperature control adjustments based on the fan health status. This secondary temperature control mechanism involves rebalancing the fan load across hotspot areas after the initial adjustment, taking into account the fan health status. This ensures that high-efficiency fans handle more cooling tasks, while reducing the load on inefficient fans or issuing maintenance alerts.

[0060] Example 2: After acquiring the location information of each cooling fan in the intelligent cabinet, the data acquisition terminal divides the intelligent cabinet into several monitoring areas based on the cooling fan location information, including the following steps:

[0061] To achieve precise temperature control in different areas inside the smart cabinet, it is first necessary to divide the cabinet into reasonable zones based on the location information of the cooling fans.

[0062] By using the distribution sensors of the cooling fans inside the cabinet or the equipment layout database, key data such as the installation position, airflow direction, and wind speed adjustment range of all fans are obtained. The three-dimensional coordinate model of the intelligent cabinet is then obtained through the database, enabling the fan position data to be accurately located in the three-dimensional coordinate model.

[0063] Based on parameters such as fan airflow, air speed, and air outlet angle, the cooling range that each fan can influence is calculated, and the fan influence zone is established. Fluid dynamics simulation or wind field modeling algorithms (such as CFD computational fluid dynamics) are used to analyze the cooling impact of the fans, determine their coverage area, and record the heat dissipation influence radius of each fan. This ensures that hot spots can be covered by multiple fans during subsequent area division, thereby improving heat dissipation efficiency. This step is prior art and will not be elaborated upon in this application.

[0064] Based on the fan's heat dissipation radius, the server rack is divided into several monitoring areas to ensure that each area is directly affected by at least one fan.

[0065] Temperature sensors installed at each monitoring area collect real-time temperature data. Edge computing devices analyze the temperature status of each monitoring area to determine which areas are marked as hotspots (hotspots are monitoring areas requiring management). This process includes the following steps:

[0066] Temperature data is collected in real time by temperature sensors and transmitted to edge computing devices for processing. Sudden interference noise is removed using moving average filtering or Kalman filtering algorithms. Temperature data from different temperature sensors are synchronized in time to ensure consistency of analysis. After data preprocessing, the following three core indicators are calculated for the temperature data of each monitoring area to comprehensively evaluate the temperature status of the area.

[0067] Real-time temperature value: The temperature data collected directly from the temperature sensor is used to record the instantaneous temperature of the current area.

[0068] The cumulative temperature rise index (measures the sustained upward trend of temperature): It is calculated using an integral formula to determine the cumulative temperature increase; the expression is as follows: In the formula, This is the cumulative temperature rise index. The temperature value at time t. This is the reference temperature for the server rack. By calculating the integral over a given monitoring period, it can be determined whether there is a continuous warming trend in a particular monitoring area.

[0069] Temperature fluctuation index (measures the degree of drastic temperature change): Calculates the standard deviation of temperature over a period of time, expressed as: In the formula, The number of data points within the time window. This is the temperature fluctuation index. Let i be the temperature value at the i-th time point. The average temperature value is used to identify whether there are large temperature fluctuations, thereby discovering potentially unstable monitoring areas.

[0070] Determine whether the monitored area needs to be marked as a hotspot based on real-time temperature values, cumulative temperature rise index, and temperature fluctuation index.

[0071] If the real-time temperature value is greater than the preset temperature threshold, it is determined that the monitoring area needs to be marked as a hotspot area.

[0072] If the real-time temperature value is less than or equal to the preset temperature threshold, the cumulative temperature rise index is greater than the preset rise threshold, and the temperature fluctuation index is less than or equal to the preset fluctuation threshold (although the current temperature value of the monitoring area is low, the temperature fluctuation indicates that the overall temperature is in a stable rising state), it is determined that the monitoring area needs to be marked as a hot spot area.

[0073] After obtaining the current operating status data of the intelligent cabinet, the current working environment of the intelligent cabinet is matched based on the database, including the following steps:

[0074] In the process of intelligent cabinet temperature control management, in order to accurately match the current cabinet's working environment, it is necessary to find the most similar working environment in the historical database based on the current operating status data. A cosine similarity algorithm is used to calculate the similarity between the current operating status and historical environmental data, thereby determining the optimal matching environment and providing a basis for subsequent temperature control strategies.

[0075] Real-time operational data is obtained from sensors and the management system inside the rack, mainly including:

[0076] Load conditions: CPU, GPU, and memory usage and power consumption of each device (such as servers and storage devices) inside the rack.

[0077] Environmental factors: Information such as humidity, airflow rate, and external temperature in the computer room.

[0078] Vectorize all data to form the current running state vector:

[0079] In the formula, This represents the current operating state vector of the server rack. For the amount of environmental data, This is the i-th environmental data value.

[0080] Extract historical operational data from the database and construct a historical environment vector set: In the formula, The number of historical operational data points is represented by each historical environment vector, which contains the same data dimensions as the current vector, representing the rack operating environment at different time periods.

[0081] The similarity between the current running state vector and the historical environment vector is calculated using the following formula: In the formula, Let be the similarity between the current running state vector and the i-th historical environment vector. The dot product of the current running state vector and the i-th historical environment vector. Let the norm of the current running state vector be . Let the norm of the i-th historical environment vector be denoted by . Calculate the similarity between the current vector and all historical vectors to obtain a similarity list:

[0082] Select the historical environment vector with the highest similarity: In the formula, To match the historical context.

[0083] The impact of the current work environment on each hotspot area is analyzed using correlation analysis algorithms, including the following steps:

[0084] In the temperature control management of intelligent cabinets, different working environment factors will have varying degrees of impact on the temperature of each hotspot area. In order to accurately assess these impacts, a correlation analysis algorithm (Pearson correlation coefficient) is needed to quantify the degree of influence of the current working environment on each hotspot area and provide a basis for subsequent temperature control adjustments.

[0085] For the marked hotspot areas (i.e., high-temperature areas that need to be managed), temperature data for each area is obtained from temperature sensors, mainly including:

[0086] Real-time temperature values ​​(current temperature of each hotspot area);

[0087] The cumulative temperature rise index (represents the rate at which temperature continues to rise, calculated based on integration).

[0088] Temperature fluctuation index (indicates temperature fluctuation, calculated based on standard deviation);

[0089] Vectorize the temperature data of each hotspot region. Assuming there are n hotspot regions, construct a temperature data matrix: In the formula, This represents the temperature-related data for the i-th hotspot region.

[0090] The current working environment factors collected from the rack management system and sensors mainly include:

[0091] Load conditions: CPU, GPU, and memory usage and power consumption of each device (such as servers and storage devices) inside the rack.

[0092] Environmental factors: Information such as humidity, airflow rate, and external temperature in the computer room.

[0093] Vectorize all environmental factor data to construct an environmental factor data matrix:

[0094] In the formula, This represents the data value of the j-th environmental factor. The historical correlation between each environmental factor and the temperature of each hotspot region is calculated using the Pearson correlation coefficient, expressed as: In the formula, This represents the correlation coefficient between the temperature data of the i-th hotspot area and the j-th environmental factor. , They are and The mean;

[0095] Calculate the correlation coefficients of all environmental factors with each hotspot area, and construct the correlation matrix R:

[0096] In the correlation matrix R, each element Representative environmental factors hotspot areas The extent of the impact.

[0097] Based on the correlation coefficient between each environmental factor and the hotspot area, the degree of influence of the current environment on the hotspot area is calculated, as expressed by: In the formula, Indicates the current environment's impact on hotspot areas The extent of the impact For the amount of environmental data, The correlation coefficient represents the relationship between the temperature data of the i-th hotspot area and the j-th environmental factor. The greater the influence, the greater the impact of the smart cabinet on the temperature data changes of the hotspot area when the current environmental factors are in operation.

[0098] Based on the analysis results of the impact level and temperature conditions, a temperature control adjustment is performed on the hotspot area. The temperature control adjustment is achieved by adjusting the fan power, including the following steps:

[0099] To ensure the temperature inside the intelligent cabinet remains within a reasonable range, a temperature control adjustment is required based on the degree of environmental influence on the hotspot area (determined by the Pearson correlation coefficient) and the current temperature status of the hotspot area (real-time temperature). In this application, temperature control adjustment is mainly achieved through fan power adjustment, that is, dynamically adjusting the fan speed to adapt to current environmental changes.

[0100] The real-time temperature value, cumulative temperature rise index, and temperature fluctuation index are normalized to their maximum and minimum values ​​(existing technology, not described in detail here), mapping the range of these values ​​to [0,1]. The normalized real-time temperature value, cumulative temperature rise index, and temperature fluctuation index are then used to calculate the temperature index of the hotspot area. The calculation expression is as follows: In the formula, Temperature index This is the normalized value of real-time temperature. This is the normalized value of the cumulative temperature rise index. This is the normalized value of the temperature fluctuation index. The larger the temperature index, the more severe the overall temperature situation in the hot spot area, and the more the fan power should be increased.

[0101] The current environmental data of the intelligent cabinet is acquired, including operating load and ambient temperature. The operating load and ambient temperature are also normalized using a maximum-minimum method to obtain normalized values. These normalized values ​​are then summed to obtain an environmental factor. This environmental factor is compared with a first change threshold and a second change threshold. If the environmental factor is greater than or equal to the first change threshold and less than or equal to the second change threshold, it is predicted that the environmental factor has no impact on the hotspot area. If the environmental factor is less than the first change threshold, it is predicted that the environmental factor will cause the temperature in the hotspot area to decrease. If the environmental factor is greater than the second change threshold, it is predicted that the environmental factor will cause the temperature in the hotspot area to increase. The fan power in the hotspot area is adjusted based on the environmental factor, the degree of impact, and the temperature index. The adjustment algorithm is as follows:

[0102] In the formula, hotspot areas The corresponding fan power after one adjustment of the cooling fan, hotspot areas Initial fan power, hotspot areas Temperature index, Indicates the current environment's impact on hotspot areas The extent of the impact As environmental factors, The first change threshold, This is the second change threshold.

[0103] After assessing the health of each cooling fan, secondary temperature control adjustments are made based on the fan health status, including the following steps:

[0104] After completing the first temperature control adjustment (fan power adjustment based on environmental influences and temperature conditions), further optimization of the intelligent cabinet's temperature control strategy is still needed to ensure that the overall heat dissipation performance meets requirements. The second temperature control adjustment mainly considers:

[0105] Fan health: Assess the operating status of each fan and avoid applying excessive loads to fans with degraded performance to prevent further damage.

[0106] Distance between hot spots: When the performance of the cooling fan in a certain hot spot area decreases, auxiliary cooling can be provided by the fans in adjacent hot spot areas.

[0107] Global heat dissipation effect assessment: If the overall cabinet still cannot meet the heat dissipation requirements after adjustments, it will trigger a shutdown and send an alarm signal.

[0108] The abnormality coefficient of the cooling fan is calculated based on the current fluctuation amplitude (calculated using the standard deviation formula, which will not be elaborated here), vibration amplitude, and noise decibels. The larger the abnormality coefficient, the worse the health of the cooling fan. Cooling fans with an abnormality coefficient greater than the abnormal threshold are shut down (i.e., not supported).

[0109] The amplitude of current fluctuations, vibration amplitudes, and noise levels are normalized (the maximum-minimum normalization mentioned earlier). The anomaly coefficient is obtained by summing the normalized amplitude of current fluctuations, vibration amplitudes, and noise levels.

[0110] The power of the supporting cooling fans is adjusted a second time based on the anomaly coefficient, as expressed by:

[0111] In the formula, hotspot areas The corresponding fan power after the secondary adjustment of the cooling fan, hotspot areas The corresponding fan power after one adjustment of the cooling fan, This is the anomaly coefficient.

[0112] After adjusting the fan health and providing auxiliary heat dissipation to adjacent hot spots, it is necessary to evaluate the overall heat dissipation effect of the smart cabinet to determine whether it meets the temperature control requirements.

[0113] Calculate the average rate of temperature change for all hotspot regions. : In the formula, n represents the number of hotspot areas. To determine the rate of temperature change in hotspot areas after temperature control adjustments, the temperature difference is obtained by subtracting the previous temperature from the current temperature. This temperature difference is then divided by the monitoring duration to determine the rate of temperature change. If the temperature in all hotspot areas continues to rise, then... If so, the heat dissipation is deemed to have failed.

[0114] If the cabinet still cannot meet the heat dissipation requirements after secondary temperature control adjustment, the following measures shall be taken:

[0115] Trigger emergency frequency reduction: If the heat dissipation pressure is too high, reduce the operating load of the equipment inside the cabinet to reduce heat generation.

[0116] Shut down equipment at risk of high temperatures: Limit the current or suspend the operation of equipment that is overheating to protect the equipment safety.

[0117] Send an alert signal: Record the abnormal situation and upload it to the operation and maintenance management system. Trigger alarm lights and buzzers to remind operation and maintenance personnel to take emergency measures. If the temperature still cannot be reduced, automatically shut down the intelligent cabinet to prevent equipment overheating and damage.

[0118] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0119] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0120] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for adaptive temperature control of an intelligent cabinet environment, characterized in that: The temperature control method includes the following steps: S1: After the acquisition terminal obtains the location information of each cooling fan in the smart cabinet, it divides the smart cabinet into several monitoring areas based on the cooling fan location information. S2: Real-time collection of temperature data in the monitoring area; analysis of the temperature data in each monitoring area using edge computing devices; and determination of which monitoring areas are marked as hotspots. S3: Obtain the current operating status data of the smart cabinet and match the current working environment of the smart cabinet based on the database; S4: Use a correlation analysis algorithm to analyze the impact of the current working environment on each hot spot area, and adjust the temperature of the hot spot area based on the impact and temperature conditions. S5: After assessing the health of each cooling fan, perform secondary temperature control adjustments based on the health of the cooling fans.

2. The intelligent cabinet environmental adaptive temperature control method according to claim 1, characterized in that: Based on the analysis results of the impact level and temperature conditions, a temperature control adjustment is performed on the hotspot area. The temperature control adjustment is achieved by adjusting the fan power, including the following steps: The temperature index is calculated based on real-time temperature values, cumulative temperature rise index, and temperature fluctuation index. An environmental factor is obtained by summing the normalized operating load value and the normalized ambient temperature value. The fan power in hotspot areas is then adjusted by combining the environmental factor, the degree of influence, and the temperature index. The adjustment algorithm is as follows: In the formula, hotspot areas Fan power after one adjustment hotspot areas Initial fan power, Temperature index Indicates the current environment's impact on hotspot areas The extent of the impact As environmental factors, The first change threshold, This is the second change threshold.

3. The intelligent cabinet environment adaptive temperature control method according to claim 2, characterized in that: The logic for obtaining the temperature index is as follows: The real-time temperature value, cumulative temperature rise index, and temperature fluctuation index are normalized to map their values ​​to the range [0,1]. The normalized real-time temperature value, cumulative temperature rise index, and temperature fluctuation index are then used to calculate the temperature index of the hotspot area. The calculation expression is: In the formula, Temperature index This is the normalized value of real-time temperature. This is the normalized value of the cumulative temperature rise index. This is the normalized value of the temperature fluctuation index; The logic for obtaining the environmental factors is as follows: Obtain the current environmental data of the intelligent cabinet, including the normalized value of the operating load and the normalized value of the ambient temperature; sum the normalized values ​​of the operating load and the ambient temperature to obtain the environmental factors; compare the obtained environmental factors with a first change threshold and a second change threshold; if the environmental factors are greater than or equal to the first change threshold and less than or equal to the second change threshold, it is predicted that the environmental factors have no impact on the hotspot area; if the environmental factors are less than the first change threshold, it is predicted that the environmental factors will cause the temperature of the hotspot area to show a downward trend; if the environmental factors are greater than the second change threshold, it is predicted that the environmental factors will cause the temperature of the hotspot area to show an upward trend.

4. The intelligent cabinet environmental adaptive temperature control method according to claim 3, characterized in that: The impact of the current work environment on each hotspot area is analyzed using correlation analysis algorithms, including the following steps: Vectorize the temperature data of each hotspot region. Given n hotspot regions, construct a temperature data matrix: In the formula, Temperature-related data representing the i-th hotspot region; Vectorize all environmental factor data to construct an environmental factor data matrix: In the formula, Let j represent the data value of the j-th environmental factor. After calculating the historical correlation between each environmental factor and the temperature of each hotspot area using the Pearson correlation coefficient, the correlation coefficient is obtained. The correlation coefficients of all environmental factors to each hotspot area are calculated, and the correlation matrix R is constructed. Based on the correlation coefficient between each environmental factor and the hotspot area, the degree of influence of the current environment on the hotspot area is calculated. The greater the degree of influence, the greater the impact of the intelligent cabinet on the temperature data change of the hotspot area when the current environmental factors are running.

5. The intelligent cabinet environment adaptive temperature control method according to claim 4, characterized in that: The correlation coefficient is obtained by calculating the historical correlation between each environmental factor and the temperature of each hotspot area using the Pearson correlation coefficient, and the expression is as follows: In the formula, This represents the correlation coefficient between the temperature data of the i-th hotspot area and the j-th environmental factor. 、 They are and The mean; Calculate the correlation coefficients of all environmental factors with each hotspot area, and construct the correlation matrix R: Based on the correlation coefficient between each environmental factor and the hotspot area, the degree of impact of the current environment on the hotspot area is calculated, expressed as: In the formula, Indicates the current environment's impact on hotspot areas The extent of the impact This refers to the amount of environmental data.

6. The intelligent cabinet environmental adaptive temperature control method according to claim 1, characterized in that: After obtaining the current operating status data of the intelligent cabinet, the current working environment of the intelligent cabinet is matched based on the database, including the following steps: Real-time operational data, including load conditions and environmental factors, is obtained from sensors and management systems inside the cabinet. Vectorize all data to form the current running state vector: In the formula, This represents the current operating state vector of the server rack. For the amount of environmental data, This is the i-th environmental data value; Extract historical operational data from the database and construct a historical environment vector set: In the formula, The number of historical operational data points is represented by each historical environment vector, which contains the same data dimensions as the current vector, indicating the rack operating environment at different time periods. Calculate the similarity between the current running state vector and the historical environment vector, and select the historical environment vector with the highest similarity.

7. The intelligent cabinet environmental adaptive temperature control method according to claim 6, characterized in that: The similarity between the current running state vector and the historical environment vector is calculated using the following expression: In the formula, Let be the similarity between the current running state vector and the i-th historical environment vector. The dot product of the current running state vector and the i-th historical environment vector. Let the norm of the current running state vector be . Let the norm of the i-th historical environment vector be denoted by . Calculate the similarity between the current vector and all historical vectors to obtain a similarity list: Select the historical environment vector with the highest similarity: In the formula, To match the historical context.

8. The intelligent cabinet environmental adaptive temperature control method according to claim 7, characterized in that: Determining which monitoring areas to mark as hotspots involves the following steps: Temperature data is collected in real time by temperature sensors and transmitted to edge computing devices for processing. Interference noise is removed by moving average filtering or Kalman filtering algorithms, and temperature data from different temperature sensors are synchronized in time. Determine whether the monitored area needs to be marked as a hotspot based on real-time temperature values, cumulative temperature rise index, and temperature fluctuation index. If the real-time temperature value is greater than the preset temperature threshold, it is determined that the monitoring area needs to be marked as a hotspot area. If the real-time temperature value is less than or equal to the preset temperature threshold, the cumulative temperature rise index is greater than the preset rise threshold, and the temperature fluctuation index is less than or equal to the preset fluctuation threshold, it is determined that the monitoring area needs to be marked as a hotspot area.

9. The intelligent cabinet environmental adaptive temperature control method according to claim 8, characterized in that: The formula for calculating the cumulative temperature rise index is as follows: In the formula, This is the cumulative temperature rise index. The temperature value at time t. This is the reference temperature for the server rack. For the monitoring period; The formula for calculating the temperature fluctuation index is as follows: In the formula, The number of data points within the time window. This is the temperature fluctuation index. Let i be the temperature value at the i-th time point. This represents the average temperature value.

10. The intelligent cabinet environment adaptive temperature control method according to claim 9, characterized in that: After acquiring the location information of each cooling fan in the intelligent cabinet, the data acquisition terminal divides the intelligent cabinet into several monitoring areas based on the cooling fan location information, including the following steps: By using the distribution sensors of the cooling fans inside the cabinet or the equipment layout database, the installation position, airflow direction, and wind speed adjustment range of all fans are obtained, and the three-dimensional coordinate model of the intelligent cabinet is obtained through the database, and the fan positions are located in the three-dimensional coordinate model. The cooling impact coverage area of ​​the fans is analyzed using fluid dynamics simulation or wind field modeling algorithms. The heat dissipation impact radius of each fan is recorded, and the cabinet is divided into several monitoring areas based on the heat dissipation impact radius.

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