Intelligent cabinet environment adaptive temperature control method

Abstract

The invention discloses an intelligent cabinet environment self-adaptive temperature control method, which relates to the technical field of cabinet temperature control, and comprises the following steps: analyzing the temperature state of each monitoring area through edge computing equipment, judging which monitoring areas are marked as hot spot areas, obtaining the current operation condition data of an intelligent cabinet, and sending the current operation condition data to the intelligent cabinet; the current working environment of the intelligent cabinet is matched based on the database, the influence degree of the current working environment on each hot spot area is analyzed through a correlation analysis algorithm, primary temperature control adjustment is conducted on the hot spot areas according to the influence degree and a temperature condition analysis result, and after the health degree of each heat dissipation fan is evaluated, the heat dissipation efficiency of each heat dissipation fan is improved. And carrying out secondary temperature control regulation according to the health degree of the cooling fan. According to the temperature control method, by evaluating the influence degree of different environmental factors on the hot spot area, the temperature control strategy not only depends on temperature data, but also can comprehensively consider the environmental factors, the fan power adjusting mode is optimized, and adaptability is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of cabinet temperature control, and particularly relates to an intelligent cabinet environment adaptive temperature control method. Background Art

[0002] Intelligent cabinets are widely used in fields such as data centers, communication base stations, and industrial control, and are used to store servers, switches, routers, storage devices, and other electronic devices. These devices generate a large amount of heat during operation, causing the temperature inside the cabinet to rise. If the temperature control is improper, it may cause equipment overheating, performance degradation, and even damage, affecting the stability and reliability of the system. Therefore, the temperature control of intelligent cabinets has become an important link to ensure the long-term stable operation of equipment.

[0003] The existing technologies have the following defects:

[0004] 1. Traditional temperature control methods usually perform temperature control adjustment based on preset fixed rules. For example, a temperature threshold is set, and when the cabinet temperature exceeds the threshold, the fan is started or the cooling intensity is increased, but it fails to adapt to different environmental conditions. When factors such as different seasons, computer room layouts, equipment operation modes, and load states change, the traditional method cannot dynamically adjust the temperature control strategy, resulting in insufficient heat dissipation in some environments, while in other environments, it may cause excessive heat dissipation, increase energy consumption, and reduce the adaptability of intelligent cabinets in different environments;

[0005] 2. Existing temperature control methods usually only adjust the fan power based on the temperature sensor data, without evaluating the health status of each fan. Due to the long-term operation of the cabinet, some fans may have health problems, resulting in the actual heat dissipation effect still not reaching the expected value even when the fan speed is increased, and may even affect the operation of other fans, thus failing to achieve the actual temperature control purpose (i.e., the heat dissipation effect is not ideal).

[0006] Based on this, the present invention proposes an intelligent cabinet environment adaptive temperature control method. By evaluating the influence degree of different environmental factors on the hot spot area, the temperature control strategy not only depends on temperature data, but also comprehensively considers environmental factors, optimizes the fan power adjustment method, and improves adaptability. Summary of the Invention

[0007] The purpose of the present invention is to provide an intelligent cabinet environment adaptive temperature control method to solve the deficiencies in the background art.

[0008] To achieve the above purpose, the present invention provides the following technical solution: An intelligent cabinet environment adaptive temperature control method, the temperature control method includes the following steps:

[0009] S1: After the acquisition end obtains the position information of each heat dissipation fan of the intelligent cabinet, the intelligent cabinet is divided into several monitoring areas based on the heat dissipation fan position information;

[0010] S2: Collect the temperature status of the monitoring area in real time. After analyzing the temperature status of each monitoring area through the edge computing device, determine which monitoring areas are marked as hot spots;

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

[0012] S4: Use the correlation analysis algorithm to analyze the influence degree of the current working environment on each hot spot area, and perform a primary temperature control adjustment on the hot spot area according to the influence degree and the temperature status analysis result;

[0013] S5: After evaluating the health of each cooling fan, perform a secondary temperature control adjustment according to the health of the cooling fan.

[0014] In a preferred embodiment, a primary temperature control adjustment is performed on the hot spot area according to the influence degree and the temperature status analysis result. The temperature control adjustment is the fan power adjustment, including the following steps:

[0015] Calculate the temperature index based on the real-time temperature value, the temperature cumulative rise index, and the temperature fluctuation index. Sum the operation load normalization value and the ambient temperature normalization value to obtain the environmental factor. Combine the environmental factor, the influence degree, and the temperature index to adjust the fan power of the hot spot area. The adjustment algorithm is: In the formula, P_fan(T i ) one is the fan power after the primary adjustment of the hot spot area T i P_fan(T i ) old is the initial fan power of the hot spot area T i gcc is the temperature index, Y(T i ) represents the influence degree of the current environment on the hot spot area T i H is the environmental factor, Z1 is the first change threshold, and Z2 is the second change threshold.

[0016] In a preferred embodiment, the acquisition logic of the temperature index is: perform normalization processing on the real-time temperature value, the temperature cumulative rise index, and the temperature fluctuation index, so that the value ranges of the real-time temperature value, the temperature cumulative rise index, and the temperature fluctuation index are mapped to [0,1]. Calculate the temperature index of the hot spot area from the normalized real-time temperature value, the temperature cumulative rise index, and the temperature fluctuation index. The calculation expression is: In the formula, gcc is the temperature index, I 实时 is the normalized real-time temperature value, I 累积 is the normalized temperature cumulative rise index value, I 波动is the normalized value of the temperature fluctuation index;

[0017] The acquisition logic of the environmental factor 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 environmental temperature, sum the normalized value of the operating load and the normalized value of the environmental temperature to obtain the environmental factor, and compare the obtained environmental factor with the first change threshold and the 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 hot spot 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 hot spot area to change in a downward trend. If the environmental factor is greater than the second change threshold, it is predicted that the environmental factor will cause the temperature in the hot spot area to change in an upward trend.

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

[0019] Vectorize the temperature data of each hot spot area. Suppose there are n hot spot areas, and construct a temperature data matrix: T = {T1, T2,..., T n}, where T i represents the temperature-related data of the i-th hot spot area;

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

[0021] E = (E1, E2,..., E p ), where E j represents the data value of the j-th environmental factor. After calculating the historical correlation between each environmental factor and the temperature of each hot spot area using the Pearson correlation coefficient, obtain the correlation coefficient, calculate the correlation coefficients of all environmental factors with each hot spot area, and construct a correlation matrix R;

[0022] According to the correlation coefficients between each environmental factor and the hot spot area, calculate the influence degree of the current environment on the hot spot area. The greater the influence degree, the greater the influence of the intelligent cabinet on the temperature data change of the hot spot area when operating under the current environmental factors.

[0023] In a preferred embodiment, after calculating the historical correlation between each environmental factor and the temperature of each hot spot area using the Pearson correlation coefficient, obtain the correlation coefficient. The expression is:

[0024] where r(T i , E j ) represents the correlation coefficient between the temperature data of the i-th hot spot area and the j-th environmental factor, are respectively T i and Ej The mean value of

[0025] Calculate the correlation coefficients of all environmental factors for each hot spot area, and construct a correlation matrix R:

[0026] Based on the correlation coefficients of each environmental factor and the hot spot area, calculate the influence degree of the current environment on the hot spot area. The expression is: In the formula, Y(T i ) represents the influence degree of the current environment on the hot spot area T i , and p is the number of environmental data.

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

[0028] Obtain real-time operating data from the sensors and management system inside the cabinet, including the load situation and environmental factors;

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

[0030] V 当前 =(E1, E2,..., E p ), in the formula, V 当前 represents the current operating state vector of the cabinet, p is the number of environmental data, and E i is the value of the th environmental data;

[0031] Extract historical operating condition data from the database to construct a set of historical environmental vectors: In the formula, M is the number of historical operating data. Each historical environmental vector contains the same data dimension as the current vector, representing the operating environment of the cabinet at different time periods;

[0032] Calculate the similarity degree between the current operating state vector and the historical environmental vectors, and select the historical environmental vector with the highest similarity degree.

[0033] In a preferred embodiment, the expression for calculating the similarity degree between the current operating state vector and the historical environmental vectors is: In the formula, is the similarity degree between the current operating state vector and the i-th historical environmental vector, is the dot product of the current operating state vector and the i-th historical environmental vector, ||V 当前 || is the norm of the current operating state vector, is the norm of the i-th historical environmental vector. Calculate the similarity degrees between the current vector and all historical vectors to obtain a similarity degree list:

[0034] Select the historical environment vector with the highest similarity: In the formula, is the matching historical environment.

[0035] In a preferred embodiment, determining which monitoring areas are marked as hot spots includes the following steps:

[0036] Collect temperature data in real time through temperature sensors, and transmit the data to the edge computing device for processing. Use the moving average filtering or Kalman filtering algorithm to remove interference noise, and synchronize the temperature data of different temperature sensors in time;

[0037] Based on the real-time temperature value, temperature cumulative rise index, and temperature fluctuation index, determine whether it is necessary to mark the monitoring area as a hot spot;

[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 hot spot;

[0039] If the real-time temperature value is less than or equal to the preset temperature threshold, the temperature cumulative 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 hot spot.

[0040] In a preferred embodiment, the calculation expression of the temperature cumulative rise index is: In the formula, I 累积 is the temperature cumulative rise index, T(t) is the temperature value at time t, T 基准 is the reference temperature of the cabinet, and t0 to t1 is the monitoring time period;

[0041] The calculation expression of the temperature fluctuation index is: In the formula, N is the number of data points within the time window, I 波动 is the temperature fluctuation index, T i is the temperature value at the i-th time point, is the average temperature value.

[0042] In a preferred embodiment, after the acquisition end obtains the position information of each cooling fan of the intelligent cabinet, the intelligent cabinet is divided into several monitoring areas based on the cooling fan position information, including the following steps:

[0043] Through the cooling fan distribution sensor or device layout database inside the cabinet, obtain the installation position, air volume direction, and wind speed adjustment range data of all fans, and obtain the three-dimensional coordinate model of the intelligent cabinet through the database, and locate the fan position in the three-dimensional coordinate model;

[0044] Analyze the cooling influence coverage area of the fan using hydrodynamic simulation or wind field modeling algorithms, record the heat dissipation influence radius of each fan, and divide the cabinet into several monitoring areas according to the heat dissipation influence radius.

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

[0046] 1. After analyzing the temperature status of each monitoring area through the edge computing device, the present invention determines which monitoring areas are marked as hot spots, obtains the current operating condition data of the intelligent cabinet, matches the current working environment of the intelligent cabinet based on the database, analyzes the influence degree of the current working environment on each hot spot area using the correlation analysis algorithm, performs a primary temperature control adjustment on the hot spot area according to the influence degree and the temperature status analysis result, and after evaluating the health of each cooling fan, performs a secondary temperature control adjustment according to the health of the cooling fan. This temperature control method optimizes the fan power adjustment method and improves adaptability by evaluating the influence degree of different environmental factors on the hot spot area, making the temperature control strategy not only rely on temperature data but also comprehensively consider environmental factors.

[0047] 2. After evaluating the health of each cooling fan, the present invention performs a secondary temperature control adjustment according to the health of the cooling fan. A secondary temperature control adjustment mechanism is introduced, that is, after the primary adjustment, the fan load between the hot spot areas is rebalanced in combination with the fan health status to ensure that efficient fans undertake more heat dissipation tasks, while inefficient fans reduce the load or receive maintenance prompts. Description of the Drawings

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

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

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

[0051] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments of the present invention belong to the scope of protection of the present invention.

[0052] Example 1: Please refer to Figure 1 As shown, for the self - adaptive temperature control method for the intelligent cabinet environment in this embodiment, the temperature control method includes the following steps:

[0053] S1: After the acquisition end obtains the position information of each cooling fan in the intelligent cabinet, the intelligent cabinet is divided into several monitoring areas based on the position information of the cooling fans;

[0054] S2: The temperature conditions of the monitoring areas are collected in real - time. After analyzing the temperature status of each monitoring area through the edge - computing device, it is judged which monitoring areas are marked as hot - spot areas;

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

[0056] S4: Use the correlation analysis algorithm to analyze the influence degree of the current working environment on each hot - spot area, and perform a primary temperature control adjustment on the hot - spot areas according to the influence degree and the temperature status analysis result;

[0057] S5: After evaluating the health of each cooling fan, perform a secondary temperature control adjustment according to the health of the cooling fan.

[0058] In this application, after analyzing the temperature status of each monitoring area through the edge - computing device, it is judged which monitoring areas are marked as hot - spot areas. After obtaining the current operation status data of the intelligent cabinet, the current working environment of the intelligent cabinet is matched based on the database. The correlation analysis algorithm is used to analyze the influence degree of the current working environment on each hot - spot area, and a primary temperature control adjustment is performed on the hot - spot areas according to the influence degree and the temperature status analysis result. After evaluating the health of each cooling fan, a secondary temperature control adjustment is performed according to the health of the cooling fan. This temperature control method optimizes the fan power adjustment method and improves adaptability by evaluating the influence degree of different environmental factors on the hot - spot areas, making the temperature control strategy not only rely on temperature data but also comprehensively consider environmental factors.

[0059] In this application, after evaluating the health of each cooling fan, a secondary temperature control adjustment is performed according to the health of the cooling fan. A secondary temperature control adjustment mechanism is introduced, that is, after the primary adjustment, the fan load between the hot - spot areas is re - balanced in combination with the fan health status, ensuring that efficient fans undertake more heat - dissipation tasks, while inefficient fans reduce the load or receive maintenance prompts.

[0060] Example 2: After the acquisition end obtains the position information of each cooling fan in the intelligent cabinet, the intelligent cabinet is divided into several monitoring areas based on the position information of the cooling fans, including the following steps:

[0061] To achieve precise temperature control in different areas inside the intelligent cabinet, it is first necessary to reasonably divide the cabinet based on the position information of the cooling fans.

[0062] Through the distribution sensors of the cooling fans inside the cabinet or the device layout database, key data such as the installation positions, air volume directions, and wind speed adjustment ranges of all fans are obtained, and the three-dimensional coordinate model of the intelligent cabinet is obtained through the database, enabling the fan position data to be accurately located in this three-dimensional coordinate model.

[0063] According to parameters such as the air volume, wind speed, and air outlet angle of the fans, calculate the cooling range that each fan can affect, establish the fan influence interval, and use fluid mechanics simulation or wind field modeling algorithms (such as CFD computational fluid dynamics) to analyze the cooling influence of the fans, determine their coverage areas, and record the heat dissipation influence radius of each fan to ensure that hot spots can be covered by multiple fans during subsequent area division, improving the heat dissipation efficiency. This step belongs to the prior art and will not be elaborated in this application.

[0064] Divide the cabinet into several monitoring areas according to the heat dissipation influence radius of the fans to ensure that each area is directly affected by at least one fan.

[0065] The temperature sensors set at each monitoring area collect the temperature conditions of the monitoring area in real time. After analyzing the temperature status of each monitoring area through the edge computing device, determine which monitoring areas are marked as hot spots (hot spots are the monitoring areas that need to be managed), including the following steps:

[0066] Collect temperature data in real time through the temperature sensors, transmit the data to the edge computing device for processing, use the moving average filtering or Kalman filtering algorithm to remove sudden interference noise, synchronize the temperature data of different temperature sensors in time to ensure the consistency of analysis. After the data preprocessing is completed, calculate the following three core indicators for the temperature data of each monitoring area to comprehensively evaluate the regional temperature status.

[0067] Real-time temperature value: Directly use the temperature data collected by the temperature sensor to record the instantaneous temperature of the current area.

[0068] Temperature cumulative rise index (measuring the continuous rising trend of temperature): Calculate the temperature cumulative growth through the integral formula, and the expression is: In the formula, I 累积 is the temperature cumulative rise index, T(t) is the temperature value at time t, T 基准 is the reference temperature of the cabinet, and t0~t1 is the monitoring time period. Through integral calculation, it can be judged whether there is a continuous rising trend in a certain monitoring area.

[0069] Temperature fluctuation index (measuring the drastic change of temperature): Calculate the standard deviation of temperature over a period of time, and the expression is: In the formula, N is the number of data points within the time window, and I 波动 is the temperature fluctuation index, and T i is the temperature value at the i-th time point, is the average temperature value. The temperature fluctuation index is used to identify whether there are large fluctuations in temperature, so as to discover potential unstable monitoring areas.

[0070] Based on the real-time temperature value, the temperature cumulative rise index, and the temperature fluctuation index, determine whether it is necessary to mark the monitoring area as a hot spot area;

[0071] If the real-time temperature value is greater than the preset temperature threshold, it is determined that it is necessary to mark the monitoring area as a hot spot area;

[0072] If the real-time temperature value is less than or equal to the preset temperature threshold, the temperature cumulative 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, due to temperature fluctuations, it indicates that the overall temperature is in a stable rising state), it is determined that it is necessary to mark the monitoring area as a hot spot area.

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

[0074] During the temperature control management of the intelligent cabinet, in order to accurately match the current working environment of the cabinet, it is necessary to find the most similar working environment in the historical database based on the current operating condition data. The cosine similarity algorithm is used to calculate the similarity between the current operating condition and the historical environment data, so as to determine the best matching environment and provide a basis for subsequent temperature control strategies.

[0075] Obtain real-time operation data from the sensors and management system inside the cabinet, mainly including:

[0076] Load condition: The CPU, GPU, memory occupancy rate, and power consumption of each device (such as servers, storage devices, etc.) inside the cabinet.

[0077] Environmental factors: Information such as the humidity, air flow rate, and external temperature of the computer room.

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

[0079] V 当前 =(E1, E2,..., E p ), in the formula, V 当前 represents the current operating state vector of the cabinet, p is the number of environmental data, and E i is the value of the th environmental data.

[0080] Extract historical operation status data from the database and construct a set of historical environment vectors: In the formula, M is the number of historical operation data. Each historical environment vector contains the same data dimensions as the current vector, representing the cabinet operation environment at different time periods.

[0081] Calculate the similarity between the current operation status vector and the historical environment vectors. The formula is as follows: In the formula, is the similarity between the current operation status vector and the i-th historical environment vector, is the dot product of the current operation status vector and the i-th historical environment vector, ||V 当前 || is the norm of the current operation status vector, is the norm of the i-th historical environment vector. 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, is the matching historical environment.

[0083] Use the correlation analysis algorithm to analyze the influence degree of the current working environment on each hot spot area, including the following steps:

[0084] In the temperature control management process of the intelligent cabinet, different working environment factors will have different degrees of influence on the temperature of each hot spot area. In order to accurately evaluate these influences, it is necessary to use the correlation analysis algorithm (Pearson correlation coefficient) to quantify the influence degree of the current working environment on each hot spot area and provide a basis for subsequent temperature control adjustment.

[0085] For the marked hot spot areas (i.e., the high-temperature areas that need to be managed), obtain the temperature data of each area from the temperature sensors, mainly including:

[0086] Real-time temperature value (the current temperature of each hot spot area);

[0087] Temperature cumulative rise index (indicating the rate of continuous temperature rise, calculated based on integration);

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

[0089] Vectorize the temperature data of each hot spot area. Assuming there are n hot spot areas, then construct a temperature data matrix: T = {T1, T2,..., T n}, in the formula, T i represents the temperature-related data of the i-th hot spot area.

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

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

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

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

[0094] E = (E1, E2,..., E p ), where E j represents the data value of the jth environmental factor. Calculate the historical correlation between each environmental factor and the temperature of each hot spot area using the Pearson correlation coefficient. The expression is: where r(T i , E j ) represents the correlation coefficient between the temperature data of the ith hot spot area and the jth environmental factor. are the means of T i and E j respectively;

[0095] Calculate the correlation coefficients of all environmental factors on each hot spot area and construct a correlation matrix R:

[0096] In the correlation matrix R, each element r(T i , E j ) represents the influence degree of the environmental factor E j on the hot spot area T i .

[0097] According to the correlation coefficient between each environmental factor and the hot spot area, calculate the influence degree of the current environment on the hot spot area. The expression is: where Y(T i ) represents the influence degree of the current environment on the hot spot area T i . p is the number of environmental data, and r(T i , E j ) represents the correlation coefficient between the temperature data of the ith hot spot area and the jth environmental factor. The greater the influence degree, the greater the influence on the change of the temperature data of the hot spot area when the intelligent cabinet operates under the current environmental factors.

[0098] Based on the analysis results of the influence degree and temperature conditions, perform a primary temperature control adjustment on the hot spot area. The temperature control adjustment is the fan power adjustment, including the following steps:

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

[0100] Perform maximum-minimum normalization processing on the real-time temperature value, temperature cumulative rise index, and temperature fluctuation index (prior art, not elaborated further), so that the value ranges of the real-time temperature value, temperature cumulative rise index, and temperature fluctuation index are mapped to between [0, 1]. Calculate the temperature index of the hot spot area using the normalized real-time temperature value, temperature cumulative rise index, and temperature fluctuation index. The calculation expression is: In the formula, gcc is the temperature index, I 实时 is the normalized real-time temperature value, I 累积 is the normalized temperature cumulative rise index value, I 波动 is the normalized temperature fluctuation index value. The larger the temperature index, the more severe the overall temperature status of the hot spot area, and the more the fan power should be increased.

[0101] Obtain the current environmental data of the intelligent cabinet, including the operating load and environmental temperature. Similarly, perform maximum-minimum normalization processing on the operating load and environmental temperature to obtain the normalized operating load value and environmental temperature value. Sum the normalized operating load value and environmental temperature value to obtain the environmental factor. Compare the obtained environmental factor with the first change threshold and the 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 hot spot area. If the environmental factor is less than the first change threshold, it is predicted that the environmental factor will cause the temperature of the hot spot area to change in a downward trend. If the environmental factor is greater than the second change threshold, it is predicted that the environmental factor will cause the temperature of the hot spot area to change in an upward trend. Adjust the fan power of the hot spot area by combining the environmental factor, the degree of influence, and the temperature index. The adjustment algorithm is:

[0102] In the formula, P_fan(T i ) one is the fan power after one adjustment of the cooling fan corresponding to the hot spot area T i , P_fan(T i ) old is the initial fan power of the hot spot area T i , gcc(T i ) is the temperature index of the hot spot area T i , Y(T i ) represents the current environment's influence on the hot spot area T iThe degree of influence, H is the environmental factor, Z1 is the first change threshold, and Z2 is the second change threshold.

[0103] After evaluating the health of each cooling fan, perform secondary temperature control adjustment based on the health of the cooling fan, including the following steps:

[0104] After completing the primary temperature control adjustment (fan power adjustment based on environmental impact and temperature conditions), it is still necessary to further optimize the temperature control strategy of the intelligent cabinet to ensure that the overall heat dissipation effect meets the requirements. In the secondary temperature control adjustment, the main considerations are:

[0105] Fan health: Evaluate the working state of each fan and avoid applying too high a load to the fan with performance degradation to prevent further damage.

[0106] Hot spot area distance: When the performance of the cooling fan in a certain hot spot area degrades, the fan in the adjacent hot spot area can be used to provide auxiliary heat dissipation.

[0107] Global heat dissipation effect evaluation: If the overall cabinet still cannot meet the heat dissipation requirements after adjustment, trigger a stop operation and send a warning signal.

[0108] Calculate the abnormality coefficient of the cooling fan based on the amplitude of the current fluctuation (calculated by the standard deviation formula, not elaborated here), the vibration amplitude, and the noise decibel. The larger the abnormality coefficient, the worse the health of the cooling fan. Turn off the cooling fan with an abnormality coefficient greater than the abnormality threshold (i.e., do not support operation);

[0109] Perform normalization processing on the amplitude of the current fluctuation, the vibration amplitude, and the noise decibel (the maximum-minimum normalization processing in the previous text), and sum the normalized amplitude of the current fluctuation, the vibration amplitude, and the noise decibel to obtain the abnormality coefficient

[0110] Adjust the fan power of the supported cooling fans according to the abnormality coefficient for the second time. The expression is:

[0111] P_fan(T i ) two =P_fan(T i ) one (1 - FC), where P_fan(T i ) two is the fan power after the second adjustment of the cooling fan corresponding to the hot spot area T i , P_fan(T i ) one is the fan power after the first adjustment of the cooling fan corresponding to the hot spot area T i , and FC is the abnormality coefficient.

[0112] After completing the adjustment of the fan health and assisting in heat dissipation in adjacent hot spot areas, it is necessary to evaluate the overall heat dissipation effect of the intelligent cabinet to determine whether the temperature control requirements are met.

[0113] Calculate the average temperature change rate of all hot spot areas In the formula, n is the number of hot spot areas, and ΔT i is the temperature change rate of the hot spot area after temperature control adjustment. The temperature difference is obtained by subtracting the temperature at the previous moment from the temperature at the current moment, and the temperature change rate is obtained by dividing the temperature difference by the monitoring duration. If the temperatures of all hot spot areas are still on the rise, that is it is determined that the heat dissipation fails.

[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 high-temperature risk equipment: Limit the current or suspend the operation of equipment with too high temperature to protect the safety of the equipment.

[0117] Send a warning signal: Record the abnormal situation and upload it to the operation and maintenance management system. Trigger the alarm light and buzzer to remind the operation and maintenance personnel to carry out emergency handling. If the temperature still cannot drop, the intelligent cabinet will be automatically shut down to avoid damage to the equipment due to overheating.

[0118] The above formulas are all calculated by taking the numerical values without dimensions. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. 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, the descriptions referring to terms such as "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0120] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only the specific embodiments. Obviously, according to the content of this specification, many modifications and changes can be made. These embodiments are selected and specifically described in this specification in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can well understand and utilize the present invention. The present invention is only limited 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 comprises the following steps: S1: After the collection end obtains the location information of each cooling fan of the smart cabinet, the smart cabinet is divided into several monitoring areas based on the location information of the cooling fans; S2: Collect the temperature status of the monitoring area in real time, analyze the temperature status of each monitoring area through edge computing devices, and determine which monitoring areas should be marked as hot spots; 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 the correlation analysis algorithm to analyze the impact of the current working environment on each hot spot area, and perform temperature control adjustment on the hot spot area based on the impact and temperature condition analysis results; S5: After evaluating the health of each cooling fan, perform secondary temperature control adjustment based on the health of the cooling fan.

2. The method for adaptive temperature control of an intelligent cabinet environment according to claim 1, characterized in that: According to the impact degree and temperature condition analysis results, the hot spot area is temperature controlled and adjusted. The temperature control adjustment is fan power adjustment, which includes the following steps: The temperature index is calculated based on the real-time temperature value, the temperature cumulative rise index, and the temperature fluctuation index. The normalized value of the operating load and the normalized value of the ambient temperature are summed to obtain the environmental factor. The fan power of the hot spot area is adjusted in combination with the environmental factor, the impact degree, and the temperature index. The adjustment algorithm is as follows: Where, P_fan(T i ) one is the hotspot area T i The fan power after one adjustment, P_fan(T i ) old is the hotspot area T i The initial fan power, gcc is the temperature index, Y(T i ) indicates the current environment for the hot spot area T i , H is the environmental factor, Z1 is the first change threshold, and Z2 is the second change threshold.

3. The method for adaptive temperature control of an intelligent cabinet environment according to claim 2, characterized in that: The acquisition logic of the temperature index is: normalize the real-time temperature value, the temperature cumulative rise index and the temperature fluctuation index, so that the value range of the real-time temperature value, the temperature cumulative rise index and the temperature fluctuation index is mapped to [0,1], and the normalized real-time temperature value, the temperature cumulative rise index and the temperature fluctuation index are used to calculate the temperature index of the hot spot area. The calculation expression is: Where gcc is the temperature index, I 实时 is the normalized value of real-time temperature, I 累积 is the normalized value of the cumulative temperature rise index, I 波动 is the normalized value of temperature fluctuation index; The logic for acquiring the environmental factor is: acquiring the current environmental data of the smart cabinet, including a normalized value of the operating load and a normalized value of the ambient temperature, summing the normalized value of the operating load and the normalized value of the ambient temperature to obtain the environmental factor, comparing the obtained environmental factor 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 the environmental factor is less than or equal to the second change threshold, it is predicted that the environmental factor has no effect on the hot spot area, if the environmental factor is less than the first change threshold, it is predicted that the environmental factor will cause the temperature of the hot spot area to change with a downward trend, and if the environmental factor is greater than the second change threshold, it is predicted that the environmental factor will cause the temperature of the hot spot area to change with an upward trend.

4. The method for adaptive temperature control of an intelligent cabinet environment according to claim 3, characterized in that: The correlation analysis algorithm is used to analyze the impact of the current working environment on each hot spot area, including the following steps: The temperature data of each hot spot area is vectorized. There are n hot spots, and the temperature data matrix is ​​constructed: T = {T1, T2, ..., T n }, where T i Represents the temperature-related data of the i-th hot spot area; Vectorize all environmental factor data and construct the environmental factor data matrix: E=(E1,E2,...,E p ), where E j Represents the data value of the jth environmental factor. Use the Pearson correlation coefficient to calculate the historical correlation between each environmental factor and the temperature of each hotspot area to obtain the correlation coefficient. Calculate the correlation coefficient of all environmental factors to each hotspot area and construct the correlation matrix R. According to the correlation coefficient between each environmental factor and the hot spot area, the influence of the current environment on the hot spot area is calculated. The greater the influence, the greater the influence of the smart cabinet on the temperature data change of the hot spot area when the current environmental factor is running.

5. The method for adaptive temperature control of an intelligent cabinet environment according to claim 4, characterized in that: The Pearson correlation coefficient is used to calculate the historical correlation between each environmental factor and the temperature of each hot spot area to obtain the correlation coefficient, which is expressed as: In the formula, r(T i ,E j ) represents the correlation coefficient between the temperature data of the i-th hot spot area and the j-th environmental factor, T i and E j The mean of Calculate the correlation coefficients of all environmental factors to each hotspot area and construct the correlation matrix R: According to the correlation coefficient between each environmental factor and the hotspot area, the impact of the current environment on the hotspot area is calculated. The expression is: In the formula, Y(T i ) indicates the current environment for the hot spot area T i The impact degree of , p is the number of environmental data.

6. The method for adaptive temperature control of an intelligent cabinet environment according to claim 1, characterized in that: After obtaining the current operating status data of the smart cabinet, the current working environment of the smart cabinet is matched based on the database, including the following steps: Obtain real-time operating data from internal cabinet sensors and management systems, including load conditions and environmental factors; Vectorize all data to form the current running state vector: V 当前 =(E1,E2,...,E p ), where V 当前 represents the current operating state vector of the cabinet, p is the number of environmental data, E i is the th environmental data value; Extract historical operating status data from the database and construct a collection of historical environment vectors: Where M is the number of historical operation data. Each historical environment vector contains the same data dimension as the current vector, indicating the cabinet operation environment in 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 method for adaptive temperature control of an intelligent cabinet environment according to claim 6, characterized in that: Calculate the similarity between the current running state vector and the historical environment vector. The expression is: In the formula, is the similarity between the current running state vector and the i-th historical environment vector, is the dot product of the current running state vector and the i-th historical environment vector, ||V 当前 || is the norm of the current running state vector, is the norm of the i-th historical environment vector, calculates the similarity between the current vector and all historical vectors, and obtains a similarity list: Select the historical environment vector with the highest similarity: In the formula, To match the historical environment.

8. The method for adaptive temperature control of an intelligent cabinet environment according to claim 7, characterized in that: Determining which monitoring areas are marked as hot spots includes the following steps: The temperature data is collected in real time through the temperature sensor and transmitted to the edge computing device for processing. The sliding average filter or Kalman filter algorithm is used to remove interference noise and synchronize the temperature data of different temperature sensors. Determine whether to mark the monitoring area as a hot spot based on the real-time temperature value, temperature cumulative 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 hot spot; If the real-time temperature value is less than or equal to the preset temperature threshold, the temperature cumulative 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 hot spot.

9. The method for adaptive temperature control of an intelligent cabinet environment according to claim 8, characterized in that: The calculation expression of the temperature cumulative rise index is: In the formula, I 累积 is the temperature cumulative rise index, T(t) is the temperature value at time t, T 基准 is the reference temperature of the cabinet, and t0~t1 is the monitoring time period; The calculation expression of the temperature fluctuation index is: Where N is the number of data points in the time window, I 波动 is the temperature fluctuation index, T i is the temperature value at the i-th time point, is the average temperature value.

10. The method for adaptive temperature control of an intelligent cabinet environment according to claim 9, characterized in that: After the collection end obtains the location information of each cooling fan of the smart cabinet, the smart cabinet is divided into several monitoring areas based on the location information of the cooling fans, including the following steps: The installation position, air volume direction, and wind speed adjustment range data of all fans are obtained through the cooling fan distribution sensor inside the cabinet or the equipment layout database, and the three-dimensional coordinate model of the smart cabinet is obtained through the database to locate the fan position in the three-dimensional coordinate model; Fluid mechanics simulation or wind field modeling algorithm is used to analyze the cooling impact coverage area of ​​the fan, the heat dissipation impact radius of each fan is recorded, and the cabinet is divided into several monitoring areas according to the heat dissipation impact radius.

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