CN117170473A - High heat dispersion's respiratory server - Google Patents
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
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Abstract
A breathing server with high heat dissipation performance is provided, which is characterized in that ventilation holes corresponding to a server module are formed at the top and the bottom of a shell, a first fan and a second fan are arranged at two sides of the shell, convection is formed under the interaction of the first fan and the second fan, a breathing heat dissipation mode is realized, so that the server module can effectively discharge heat out of the shell, the running stability and the service life of the server module are improved, and meanwhile, the energy consumption and the noise are reduced.
Description
Technical Field
The application relates to the technical field of intelligent heat dissipation, in particular to a breathing server with high heat dissipation performance.
Background
In data center and server environments, heat dissipation is an important issue. High load operation of the server can result in elevated temperatures, which if excessive, can lead to equipment failure, performance degradation, and even damage. Therefore, effective heat dissipation management is critical to maintaining the stability and reliability of the server.
Currently, a cooling scheme of a server generally uses a fan and a cooling fin to improve air flow so as to achieve the purpose of cooling. However, conventional heat dissipation schemes are often designed on a highest load basis, which means that the fan will run at maximum speed even at low loads. This excessive heat dissipation causes an increase in power consumption of the fan and waste of energy, thereby increasing the running cost of the server. Furthermore, the fins in conventional heat dissipation schemes typically cover only certain areas of the server, while other areas may not be sufficiently heat-dissipated, and such uneven heat dissipation distribution may cause certain components to overheat, thereby affecting the performance and reliability of the server.
Accordingly, a high heat dissipation respiratory server is desired.
Disclosure of Invention
The present application has been made to solve the above-mentioned technical problems. The embodiment of the application provides a breathing server with high heat dissipation performance, which is characterized in that ventilation holes corresponding to a server module are arranged at the top and the bottom of a shell, a first fan and a second fan are arranged at two sides of the shell, and convection is formed under the interaction of the first fan and the second fan, so that the breathing heat dissipation mode is realized, the server module can effectively discharge heat out of the shell, the running stability and the service life of the server module are improved, and meanwhile, the energy consumption and the noise are reduced.
In a first aspect, there is provided a high heat dissipation respiratory server comprising:
the server comprises a shell, wherein a plurality of server main bodies are arranged in the shell, a plurality of ventilation holes are formed in the top and the bottom of the shell, and the number and the positions of the ventilation holes correspond to those of the server main bodies;
a temperature sensor provided to the housing;
the server comprises a server body, a server and a server, wherein the server body comprises a processor, a memory and a hard disk;
the first fan and the second fan are arranged on two sides of the shell;
and the controller can be in communication connection with the temperature sensor, the first fan and the second fan and is used for controlling the rotating speeds of the first fan and the second fan.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a block diagram of a high heat dissipation respiratory server according to an embodiment of the present application.
Fig. 2 is a block diagram of the controller in the breathing server with high heat dissipation performance according to an embodiment of the present application.
Fig. 3 is a flowchart of a control method of a high heat dissipation performance breathing server according to an embodiment of the present application.
Fig. 4 is a schematic diagram of a control method architecture of a high heat dissipation respiratory server according to an embodiment of the application.
Fig. 5 is an application scenario diagram of a high-heat-dissipation-performance breathing server according to an embodiment of the present application.
Detailed Description
The following description of the technical solutions according to the embodiments of the present application will be given with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Unless defined otherwise, all technical and scientific terms used in the embodiments of the application have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the present application is for the purpose of describing particular embodiments only and is not intended to limit the scope of the present application.
In describing embodiments of the present application, unless otherwise indicated and limited thereto, the term "connected" should be construed broadly, for example, it may be an electrical connection, or may be a communication between two elements, or may be a direct connection, or may be an indirect connection via an intermediate medium, and it will be understood by those skilled in the art that the specific meaning of the term may be interpreted according to circumstances.
It should be noted that, the term "first\second\third" related to the embodiment of the present application is merely to distinguish similar objects, and does not represent a specific order for the objects, it is to be understood that "first\second\third" may interchange a specific order or sequence where allowed. It is to be understood that the "first\second\third" distinguishing objects may be interchanged where appropriate such that embodiments of the application described herein may be practiced in sequences other than those illustrated or described herein.
Having described the basic principles of the present application, various non-limiting embodiments of the present application will now be described in detail with reference to the accompanying drawings.
The server can generate a large amount of heat in the operation process, if the server can not effectively dissipate heat in time, the temperature can be continuously increased, and the high temperature can lead to the performance reduction of server hardware and even damage key components. Therefore, through effective heat dissipation management, the temperature of the server can be controlled within a safe range, and the normal operation of the server is ensured.
Excessive temperatures can negatively impact the stability and reliability of the server, and under high temperature conditions, the resistance of the electronic components can increase, signal transmission errors can occur, and even hardware failures can result. Through effective heat dissipation management, the temperature can be reduced, the occurrence of the problems is reduced, and the stability and the reliability of the server are improved.
Excessive temperatures can accelerate degradation and damage to server hardware, for example, high temperatures can lead to reduced electrolytic capacitor life, reduced fan bearing life, and the like. Through effective heat dissipation management, the temperature can be reduced, the aging speed of hardware is reduced, and the service life of the server is prolonged.
Conventional heat dissipation schemes are often designed based on the highest load, resulting in energy waste while still operating at maximum speed under low load conditions. By adopting an effective heat dissipation management strategy, the rotating speed of the fan can be intelligently controlled according to the actual load condition, so that the energy consumption is reduced, and the running cost of the server is reduced.
Effective heat dissipation management is important for maintaining stability and reliability of the server, and can control temperature, improve stability and reliability, prolong service life of the server, save energy and reduce cost. Therefore, in data center and server environments, attention to heat dissipation management is essential. Conventional heat dissipation schemes typically use fins and fans to increase air flow, but the fins tend to cover only certain areas of the server, while other areas may not be adequately cooled, and this uneven heat dissipation distribution may cause certain components to overheat, affecting the performance and reliability of the server.
Conventional heat dissipation schemes are often designed based on the highest load, which means that even at low loads, the fans operate at maximum speed, and this excessive heat dissipation approach can result in increased power consumption by the fans, thereby increasing power consumption and operating costs of the server.
In conventional heat dissipation schemes, fans typically operate at relatively high rotational speeds, producing noise. This can be a problem for sites requiring a quiet working environment, particularly in large-scale data centers, where noise can accumulate and adversely affect the comfort of the staff.
Traditional heat dissipation schemes rely primarily on a combination of fans and fins to increase the heat dissipation efficiency. However, due to the restriction of air flow and the partial coverage of the heat sink, the heat dissipation efficiency may be limited and the heat dissipation potential may not be fully utilized.
The traditional heat dissipation scheme has some disadvantages in heat dissipation balance, energy utilization, noise control and the like. To solve these problems, the respiratory server heat dissipation scheme provides a more efficient heat dissipation management manner to improve heat dissipation efficiency, reduce energy consumption and noise.
In one embodiment of the present application, fig. 1 is a block diagram of a high heat dissipation respiratory server according to an embodiment of the present application. As shown in fig. 1, a high heat dissipation respiratory server 100 according to an embodiment of the present application includes: a plurality of server main bodies 2 are arranged in the shell 1, a plurality of ventilation holes 3 are formed in the top and the bottom of the shell, and the number and the positions of the ventilation holes 3 correspond to those of the server main bodies 2; a temperature sensor 4 provided in the housing 1; a server main body 2, wherein the server main body 2 comprises a processor 21, a memory 22 and a hard disk 23; a first fan 5 and a second fan 6 disposed at both sides of the housing 1; a controller 7, said controller 7 being communicatively connected to said temperature sensor 4, said first fan 5 and said second fan 6 and being adapted to control the rotational speed 6 of said first fan 5 and said second fan.
Wherein, the shell is the outside protection architecture of respiratory server, plays the effect of protecting the inside subassembly of server to the position of ventilation hole is provided. The design of the housing should take into account the optimization of heat dissipation requirements and air flow.
The server main body is a core part of the breathing server and comprises a processor, a memory, a hard disk and other key components. The processor is responsible for executing computing tasks, the memory is used for storing data and programs, and the hard disk is used for storing large-capacity data. These components cooperate to provide computing and storage functions for the server.
The top and bottom of the housing are provided with a plurality of ventilation holes for facilitating air flow. The number and location of these vents should correspond to the number and location of the server bodies to ensure that air can flow through each server body and effectively carry heat away.
The temperature sensor is arranged in the shell and used for monitoring the temperature of the server, can measure the temperature of the main body of the server in real time and transmits the information to the controller so as to adjust the rotating speed of the fan according to the requirement.
The first fan and the second fan are disposed at both sides of the housing for generating air flow, and they take heat away and maintain the temperature of the server within a safe range by sucking external air and pushing it into the server body. The rotational speed of these fans can be adjusted by the controller based on feedback from the temperature sensor.
The controller is a central control unit of the breathing server, which is in communication with the temperature sensor, the first fan and the second fan. The controller can monitor the temperature of the server in real time according to the reading of the temperature sensor and correspondingly control the rotating speed of the fan so as to ensure the optimization of the heat dissipation effect.
The components of the breathing server cooperate together to achieve efficient heat dissipation management. The housing provides protection and ventilation, the server body performs computing and storage tasks, the temperature sensor monitors temperature, the fan generates air flow, and the controller is responsible for monitoring and adjusting the rotational speed of the fan. The cooperation of the components can provide balanced and efficient heat dissipation effect, and the stability and reliability of the server are maintained.
In view of the above technical problems, the technical concept of the present application is to provide ventilation holes corresponding to a server module at the top and bottom of a housing, and provide a first fan and a second fan at both sides of the housing, and form convection under the interaction of the first fan and the second fan, so as to implement a breathing type heat dissipation manner, so that the server module can effectively discharge heat out of the housing, thereby improving the operation stability and life of the server module, and reducing energy consumption and noise.
Particularly, in the technical scheme of the application, the temperature value of the server, the rotating speed value of the first fan and the rotating speed value of the second fan are acquired through the sensor group, and a data processing and analyzing algorithm is introduced into the rear end to carry out time sequence collaborative analysis of each sensor data so as to carry out self-adaptive control of the rotating speed of the first fan and the rotating speed of the second fan based on the actual temperature.
Fig. 2 is a block diagram of the controller in the breathing server with high heat dissipation performance according to an embodiment of the present application. As shown in fig. 2, the controller 7 includes: the data acquisition module 110 is configured to acquire server temperature values, rotational speed values of the first fan, and rotational speed values of the second fan at a plurality of predetermined time points within a predetermined time period; the data interaction correlation analysis module 120 is configured to perform time sequence collaborative interaction correlation analysis on the server temperature values, the rotational speed values of the first fan and the rotational speed values of the second fan at the plurality of predetermined time points to obtain heat dissipation-heat source interaction characteristics; the fan speed control module 130 is configured to determine, based on the heat dissipation-heat source interaction characteristic, that the speed value of the first fan at the current time point should be increased or decreased, and that the speed value of the second fan at the current time point should be increased or decreased.
In the data acquisition module 110, server temperature values, a rotational speed value of the first fan, and a rotational speed value of the second fan at a plurality of predetermined time points within a predetermined period of time are acquired. In the data acquisition process, the numerical value of each parameter is ensured to be accurately acquired, and the corresponding relation between the acquired time point and the server state is ensured to be accurate.
And in the data interaction correlation analysis module, carrying out time sequence collaborative interaction correlation analysis on the server temperature values, the rotating speed value of the first fan and the rotating speed value of the second fan at a plurality of preset time points so as to obtain heat dissipation-heat source interaction characteristics. In the analysis process, the relationship between the heat dissipation and the heat source can be better understood by analyzing the obtained interaction characteristics by considering the association relationship between different parameters, such as the interaction effect between the temperature and the rotation speed of the fan.
In the fan speed control module, based on the heat dissipation-heat source interaction characteristic, it is determined that the speed value of the first fan at the current time point should be increased or decreased, and the speed value of the second fan at the current time point should be increased or decreased. When the rotating speed of the fan is controlled, the rotating speed of the fan is adjusted according to the heat dissipation requirement and the condition of the heat source. For example, when the server temperature increases, the fan speed should be increased to increase the heat dissipation efficiency, and when the temperature decreases, the fan speed should be decreased to reduce the power consumption.
In the above steps, the rotation speed of the fan can be controlled more accurately by analyzing the heat radiation-heat source interaction characteristics, so that the fan can be adjusted according to actual requirements, and the heat radiation efficiency is improved. By adjusting the rotation speed of the fan according to the heat dissipation requirement, unnecessary energy waste can be avoided, and the energy consumption of the server is reduced. The temperature of the server can be effectively controlled by monitoring the temperature of the server in real time and adjusting the rotating speed of the fan, and the stability and reliability of the system are improved. By optimizing the fan speed, noise generated by the fan can be reduced, providing a quieter operating environment. In one embodiment of the present application, the data acquisition module 110 is configured to obtain a server temperature value, a rotational speed value of the first fan, and a rotational speed value of the second fan at a plurality of predetermined time points within a predetermined period of time. In the technical scheme of the application, firstly, server temperature values, a rotating speed value of a first fan and a rotating speed value of a second fan at a plurality of preset time points in a preset time period are obtained.
The method comprises the steps of obtaining the temperature values of a server, the rotating speed values of a first fan and the rotating speed values of a second fan at a plurality of preset time points in a preset time period, wherein the rotating speed values of the first fan, the rotating speed values of the second fan and the rotating speed values of the first fan, which are determined at the current time point, are required to be increased or decreased, and the rotating speed values of the second fan, which are determined at the current time point, are required to be increased or decreased so as to realize more accurate heat dissipation control, so that the heat dissipation requirement of the server is met, and the stability of a system is improved.
Specifically, by acquiring the temperature value of the server in the predetermined time period, the controller can know the temperature change trend of the server in real time, and is helpful for judging the temperature state of the current time point, so as to adjust the rotating speed of the fan according to the requirement.
By acquiring the rotation speed values of the first fan and the second fan within a preset time period and combining the temperature value of the server, the controller can perform data interaction correlation analysis to find a correlation mode between the rotation speed and the temperature of the fans. The method and the device are beneficial to determining that the rotation speeds of the first fan and the second fan at the current time point are increased or reduced so as to better control the heat dissipation effect.
Based on the analysis of the heat dissipation-heat source interaction characteristics, the controller may determine that the rotational speeds of the first fan and the second fan should be increased or decreased according to the temperature condition and the associated mode at the current time point. By dynamically adjusting the fan speed, proper heat dissipation capability can be provided to keep the server within a safe temperature range.
The controller can accurately adjust the rotation speeds of the first fan and the second fan according to the real-time temperature data and the fan rotation speed data and by combining analysis results so as to meet the heat dissipation requirement of the server. This will increase the heat dissipation efficiency, reduce the power consumption, increase the system stability, and reduce noise, thereby enhancing the performance and reliability of the server.
In one embodiment of the present application, for the data cross correlation analysis module 120, it includes: the server temperature time sequence feature extraction unit is used for extracting time sequence features of the server temperature values of the plurality of preset time points to obtain a server temperature time sequence feature vector; a fan rotation speed time sequence association coding unit, configured to perform time sequence association coding on the rotation speed values of the first fan and the rotation speed values of the second fan at the multiple preset time points to obtain rotation speed cooperative time sequence association feature vectors; and the heat radiation-heat source characteristic interaction analysis unit is used for carrying out characteristic interaction analysis on the temperature time sequence characteristic vector of the server and the rotation speed collaborative time sequence correlation characteristic vector so as to obtain the heat radiation-heat source interaction characteristic.
First, by performing time series feature extraction on server temperature values at a plurality of predetermined time points, a change pattern and trend of temperature can be captured. Such timing feature vectors may provide information about server temperature changes for subsequent analysis and control decisions.
Then, by performing time-series association encoding on the first fan rotational speed value and the second fan rotational speed value at a plurality of predetermined time points, the association relationship between rotational speeds can be encoded as a feature vector. Such associated feature vectors may provide information about interactions between fan speeds, providing a basis for subsequent feature interaction analysis.
Then, by performing feature interaction analysis on the server temperature time sequence feature vector and the rotation speed collaborative time sequence associated feature vector, interaction features between heat dissipation and a heat source can be revealed. Such analysis may help determine the relationship between heat dissipation demand and fan speed to better control fan speed and optimize heat dissipation.
And finally, extracting the time sequence characteristics of the temperature of the server to know the change mode and trend of the temperature, so that the heat dissipation requirement is better understood. Through the association relation between the rotating speeds of the encoding fans, the interaction between the fans can be analyzed and understood, and a basis is provided for subsequent control decisions. Through the feature interaction analysis, interaction features between heat dissipation and a heat source can be revealed, fan rotation speed control is helped to be optimized, and heat dissipation efficiency is improved. Thus, the accuracy and efficiency of heat dissipation control are improved, and the stability and reliability of the server are improved.
In one embodiment of the present application, the server temperature timing feature extraction unit includes: a server temperature time sequence arrangement subunit, configured to arrange the server temperature values at the plurality of predetermined time points into a server temperature time sequence input vector according to a time dimension; and the server temperature time sequence change subunit is used for enabling the server temperature time sequence input vector to pass through a server temperature time sequence feature extractor based on a one-dimensional convolution layer to obtain the server temperature time sequence feature vector.
Next, it is considered that the server temperature value has a dynamic change law of time series in the time dimension, that is, the server temperature value has cooperative correlation characteristic information of time series at each predetermined time point in time series. Therefore, in the technical scheme of the application, the server temperature values at the plurality of preset time points are arranged into the server temperature time sequence input vector according to the time dimension, so that the time sequence distribution information of the server temperature values is integrated, and then the server temperature time sequence input vector is subjected to feature mining in a server temperature time sequence feature extractor based on a one-dimensional convolution layer, so that the time sequence associated feature information of the server temperature values in the time dimension is extracted, and the server temperature time sequence feature vector is obtained.
In one embodiment of the present application, the fan rotation speed timing related encoding unit includes: a fan rotation speed time sequence arrangement subunit, configured to arrange the rotation speed values of the first fan and the rotation speed values of the second fan at the multiple predetermined time points into a first fan rotation speed time sequence input vector and a second fan rotation speed time sequence input vector according to a time dimension, respectively; a rotational speed cooperative correlation subunit, configured to calculate a rotational speed cooperative timing correlation matrix between the first fan rotational speed timing input vector and the second fan rotational speed timing input vector; and the rotating speed collaborative timing sequence correlation feature extraction subunit is used for obtaining the rotating speed collaborative timing sequence correlation feature vector by the rotating speed collaborative pattern feature extractor based on the convolutional neural network model through the rotating speed collaborative timing sequence correlation matrix.
Then, for the rotation speed value of the first fan and the rotation speed value of the second fan at the plurality of preset time points, the rotation speed value of the first fan and the rotation speed value of the second fan have dynamic change regularity in time dimension, and time sequence cooperative association characteristic information is arranged between the rotation speed value of the first fan and the rotation speed value of the second fan, so that respiratory type heat dissipation is achieved together, and the heat dissipation effect of the server is influenced. Therefore, in the technical solution of the present application, it is necessary to further arrange the rotational speed values of the first fan and the rotational speed values of the second fan at the plurality of predetermined time points into a first fan rotational speed time sequence input vector and a second fan rotational speed time sequence input vector according to a time dimension, so as to integrate the time sequence distribution information of the rotational speed values of the first fan and the rotational speed values of the second fan, respectively.
Then, a rotational speed collaborative time sequence incidence matrix between the first fan rotational speed time sequence input vector and the second fan rotational speed time sequence input vector is calculated, so that time sequence distribution information of the rotational speed value of the first fan and the rotational speed value of the second fan in a time dimension is subjected to collaborative incidence to construct a time sequence incidence relation of the rotational speed value of the first fan and the rotational speed value of the second fan, and therefore follow-up capture and depiction of time sequence collaborative incidence change characteristics of the rotational speed value of the first fan and the rotational speed value of the second fan are facilitated, and collaborative respiration type heat dissipation effects between the first fan and the second fan at different rotational speeds can be analyzed more accurately.
Further, feature extraction is carried out on the rotational speed collaborative time sequence association matrix through a rotational speed collaborative mode feature extractor based on a convolutional neural network model, so that time sequence collaborative association feature information between the rotational speed value of the first fan and the rotational speed value of the second fan in a time dimension is extracted, and a rotational speed collaborative time sequence association feature vector is obtained.
In one embodiment of the present application, the heat dissipation-heat source characteristic interaction analysis unit is configured to: and performing feature interaction based on an attention mechanism on the rotation speed collaborative time sequence association feature vector and the server temperature time sequence feature vector by using an inter-feature attention interaction layer to obtain a heat dissipation-heat source interaction feature vector as the heat dissipation-heat source interaction feature.
And then, using an inter-feature attention interaction layer to perform feature interaction based on an attention mechanism on the rotation speed coordination time sequence association feature vector and the server temperature time sequence feature vector to obtain a heat dissipation-heat source interaction feature vector, so as to capture association and interaction between the fan rotation speed coordination time sequence association feature and the temperature time sequence change feature of the server, namely interaction influence feature of heat dissipation and heat source, so as to analyze the heat dissipation effect. It should be appreciated that since the goal of the traditional attention mechanism is to learn an attention weight matrix, a greater weight is given to important features and a lesser weight is given to secondary features, thereby selecting more critical information to the current task goal. This approach is more focused on weighting the importance of individual features, while ignoring the dependency between features. The attention interaction layer between the features can capture the correlation and the mutual influence between the fan rotating speed time sequence cooperative correlation feature and the temperature time sequence change feature of the server through the feature interaction based on an attention mechanism, learn the dependency relationship between different features, and interact and integrate the features according to the dependency relationship, so that a heat dissipation-heat source interaction feature vector is obtained.
Feature interactions based on the mechanism of attention are one way to learn the correlation and interactions between features. In heat dissipation control, feature interaction can help capture the relationship between fan rotation speed time sequence collaborative correlation features and server temperature time sequence change features, and further more accurate information and decision basis are provided.
Specifically, first, the fan rotation speed timing cooperative correlation feature and the server temperature timing variation feature are expressed as feature vectors. The correlation between the different features is then measured by calculating the attention weight. The attention weights may be assigned weights based on similarity and importance between features. And then, according to the calculated attention weight, the feature vector is interacted. Features of higher weight will have a greater impact on the interaction results, while features of lower weight will have less impact on the results. And finally, integrating the interacted characteristics to obtain a heat dissipation-heat source interaction characteristic vector. This feature vector contains correlations and interactions between fan speed and server temperature, providing more comprehensive information to guide the heat dissipation control decisions.
Through the feature interaction based on the attention mechanism, the controller can learn the dependency relationship among different features, and interact and integrate the features according to the dependency relationship. Therefore, the correlation characteristic between the rotating speed of the fan and the temperature of the server can be captured more accurately, and a more accurate heat dissipation-heat source interaction characteristic vector is provided, so that the heat dissipation control effect is improved.
In one embodiment of the present application, for the fan speed control module 130, it includes: the feature optimization unit is used for performing feature distribution optimization on the heat dissipation-heat source interaction feature vector to obtain an optimized heat dissipation-heat source interaction feature vector; the rotational speed classification control unit is used for enabling the optimized heat radiation-heat source interaction characteristic vector to pass through a classifier to obtain a classification result, wherein the classification result is used for indicating that the rotational speed value of the first fan at the current time point is increased or reduced, and the rotational speed value of the second fan at the current time point is increased or reduced.
In one embodiment of the present application, the feature optimization unit includes: the space self-adaptive point learning optimization subunit is used for carrying out non-homogeneous Hilbert-face space self-adaptive point learning on the rotation speed collaborative time sequence associated feature vector and the server temperature time sequence feature vector so as to obtain a fusion feature vector; and the fusion optimization subunit is used for fusing the fusion feature vector and the heat dissipation-heat source interaction feature vector to obtain the optimized heat dissipation-heat source interaction feature vector.
And then, the heat radiation-heat source interaction characteristic vector is passed through a classifier to obtain a classification result, wherein the classification result is used for indicating that the rotating speed value of the first fan at the current time point is increased or reduced, and the rotating speed value of the second fan at the current time point is increased or reduced. That is, the method uses the interactive characteristic information between the temperature time sequence change characteristic of the server and the rotation speed time sequence cooperative correlation characteristic of the first fan and the second fan to perform classification processing, so as to perform adaptive control on the rotation speed of the first fan and the rotation speed of the second fan in real time based on the actual temperature.
Particularly, in the technical scheme of the application, when the inter-feature attention layer is used for carrying out feature interaction based on an attention mechanism on the rotational speed collaborative time sequence association feature vector and the server temperature time sequence feature vector to obtain the heat dissipation-heat source interaction feature vector, the inter-feature attention layer focuses on the extraction of the dependency relation feature between the rotational speed collaborative time sequence association feature vector and the server temperature time sequence feature vector, so that if the expression of the rotational speed collaborative time sequence semantic feature of the rotational speed collaborative time sequence association feature vector and the server temperature time sequence semantic feature of the server temperature time sequence feature vector by the heat dissipation-heat source interaction feature vector can be further enhanced, the expression effect of the heat dissipation-heat source interaction feature vector can be improved.
Here, the applicant of the present application considers the non-homogeneous point-by-point correspondence between the rotation speed cooperative timing correlation feature vector obtained by performing feature filtering on the basis of a two-dimensional convolution kernel scale on the full-time-sequential spatial correlation of the rotation speed timing sequence of the first fan and the rotation speed timing sequence of the second fan and the server temperature timing feature vector obtained by performing feature filtering on the basis of a one-dimensional convolution kernel on the server temperature timing sequence, and thus,for example, asAnd the server temperature timing feature vector, e.g., denoted +.>Spatially adaptive point learning on non-homogeneous Hilbert-face is performed to obtain a fused feature vector, e.g., denoted +.>The method is specifically expressed as follows: carrying out non-homogeneous Hilbert-face space self-adaptive point learning on the rotation speed collaborative time sequence association feature vector and the server temperature time sequence feature vector by using the following optimization formula to obtain the fusion feature vector; wherein, the optimization formula is:
wherein (1)>Is the rotation speed cooperative time sequence associated characteristic vector, < >>Is the server temperature time sequence feature vector, < >>Is a transpose of the server temperature timing feature vector,>，/>and->Representing a non-homogeneous minpoint distance based on Gilbert space, and +.>And->Is the parameter of the ultrasonic wave to be used as the ultrasonic wave,and->Feature vector +.>And->Is defined as the global feature mean value of (2), and feature vector +.>And->Are all row vectors, +.>Representing multiplication by location +.>Representing addition by position +.>Is covariance matrix>Is the fusion feature vector.
Thus, the rotation speed is matched with the time sequence related characteristic vector by using non-homogeneous Gilbert space measurementAnd the server temperature time sequence feature vector +.>The vector point correlation between the two can be subjected to one-dimensional convolution, and the rotation speed can be used for correlating the characteristic vector +.>And the server temperature time sequence feature vector +.>Feature manifold of the high-dimensional feature representation of Hilbert space-based manifold convergence hyperplane, and correlating feature vectors with each other in a rotation speed-oriented collaborative timing by adaptively learning points toward the hyperplane in a face space of the Hilbert space-based manifold convergence hyperplane>And the server temperature time sequence feature vector +.>The air measurement (aerial measurement) of each distribution convergence direction is corrected, and the rotation speed collaborative time sequence associated characteristic vector is improved>And the server temperature time sequence feature vector +.>Non-homogeneous point-by-point fusibility between them, thereby promoting the fusion feature vector +.>Then, the fusion feature vector is added +.>Further fusing with the heat dissipation-heat source interaction feature vector, the expression effect of the heat dissipation-heat source interaction feature vector can be improved. In this way, the adaptive control of the first fan rotation speed and the second fan rotation speed can be performed based on the change condition of the actual temperature, so that the performance and the reliability of the server are improved, and the energy consumption and the operation and maintenance cost are reduced.
In summary, the respiratory server 100 with high heat dissipation performance according to the embodiment of the present application is illustrated, which enables the server module to effectively discharge heat out of the housing, thereby improving the operation stability and lifetime of the server module, and reducing energy consumption and noise.
In one embodiment of the present application, fig. 3 is a flowchart of a control method of a breathing server with high heat dissipation performance according to an embodiment of the present application. Fig. 4 is a schematic diagram of a control method architecture of a high heat dissipation respiratory server according to an embodiment of the application. As shown in fig. 3 and 4, the method for controlling the breathing server with high heat dissipation performance includes: 210, acquiring server temperature values, a rotating speed value of a first fan and a rotating speed value of a second fan at a plurality of preset time points in a preset time period; 220, performing time sequence collaborative interaction correlation analysis on the server temperature values, the rotating speed value of the first fan and the rotating speed value of the second fan at a plurality of preset time points to obtain heat dissipation-heat source interaction characteristics; 230, determining that the rotational speed value of the first fan at the current point in time should be increased or decreased and the rotational speed value of the second fan at the current point in time should be increased or decreased based on the heat sink-heat source interaction characteristic.
It will be appreciated by those skilled in the art that the specific operation of each step in the above-described method of controlling a high heat dissipation performance breathing server has been described in detail in the above description of the high heat dissipation performance breathing server system with reference to fig. 1 to 2, and thus, repetitive description thereof will be omitted.
Fig. 5 is an application scenario diagram of a high-heat-dissipation-performance breathing server according to an embodiment of the present application. As shown in fig. 5, in the application scenario, first, server temperature values (e.g., C1 as illustrated in fig. 5), rotational speed values of a first fan (e.g., C2 as illustrated in fig. 5), and rotational speed values of a second fan (e.g., C3 as illustrated in fig. 5) at a plurality of predetermined time points within a predetermined period of time are acquired; then, the acquired server temperature value, the rotational speed value of the first fan, and the rotational speed value of the second fan are input to a server (e.g., S as illustrated in fig. 1) in which a high heat dissipation performance breathing server algorithm is deployed, wherein the server is capable of processing the server temperature value, the rotational speed value of the first fan, and the rotational speed value of the second fan based on the high heat dissipation performance breathing server algorithm to determine whether the rotational speed value of the first fan should be increased or decreased at the current point in time, and the rotational speed value of the second fan should be increased or decreased at the current point in time.
The basic principles of the present application have been described above in connection with specific embodiments, however, it should be noted that the advantages, benefits, effects, etc. mentioned in the present application are merely examples and not intended to be limiting, and these advantages, benefits, effects, etc. are not to be considered as essential to the various embodiments of the present application. Furthermore, the specific details disclosed herein are for purposes of illustration and understanding only, and are not intended to be limiting, as the application is not necessarily limited to practice with the above described specific details.
The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the application. Thus, the present application is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or terminal device comprising the element.
The foregoing description has been presented for purposes of illustration and description. Furthermore, this description is not intended to limit embodiments of the application to the form disclosed herein. Although a number of example aspects and embodiments have been discussed above, a person of ordinary skill in the art will recognize certain variations, modifications, alterations, additions, and subcombinations thereof.
Claims (9)
1. A high heat dissipation respiratory server, comprising:
the server comprises a shell, wherein a plurality of server main bodies are arranged in the shell, a plurality of ventilation holes are formed in the top and the bottom of the shell, and the number and the positions of the ventilation holes correspond to those of the server main bodies;
a temperature sensor provided to the housing;
the server comprises a server body, a server and a server, wherein the server body comprises a processor, a memory and a hard disk;
the first fan and the second fan are arranged on two sides of the shell;
and the controller can be in communication connection with the temperature sensor, the first fan and the second fan and is used for controlling the rotating speeds of the first fan and the second fan.
2. The high heat dissipation respiratory server of claim 1, wherein the controller comprises:
the data acquisition module is used for acquiring server temperature values, rotating speed values of the first fan and rotating speed values of the second fan at a plurality of preset time points in a preset time period;
the data interaction correlation analysis module is used for carrying out time sequence collaborative interaction correlation analysis on the server temperature values, the rotating speed values of the first fan and the rotating speed values of the second fan at a plurality of preset time points so as to obtain heat dissipation-heat source interaction characteristics;
the fan speed control module is used for determining that the speed value of the first fan at the current time point is increased or reduced and the speed value of the second fan at the current time point is increased or reduced based on the heat radiation-heat source interaction characteristics.
3. The high heat dissipation respiratory server of claim 2, wherein the data cross-correlation analysis module comprises:
the server temperature time sequence feature extraction unit is used for extracting time sequence features of the server temperature values of the plurality of preset time points to obtain a server temperature time sequence feature vector;
a fan rotation speed time sequence association coding unit, configured to perform time sequence association coding on the rotation speed values of the first fan and the rotation speed values of the second fan at the multiple preset time points to obtain rotation speed cooperative time sequence association feature vectors;
and the heat radiation-heat source characteristic interaction analysis unit is used for carrying out characteristic interaction analysis on the temperature time sequence characteristic vector of the server and the rotation speed collaborative time sequence correlation characteristic vector so as to obtain the heat radiation-heat source interaction characteristic.
4. The high-heat-dissipation respiratory server of claim 3, wherein the server temperature timing feature extraction unit comprises:
a server temperature time sequence arrangement subunit, configured to arrange the server temperature values at the plurality of predetermined time points into a server temperature time sequence input vector according to a time dimension;
and the server temperature time sequence change subunit is used for enabling the server temperature time sequence input vector to pass through a server temperature time sequence feature extractor based on a one-dimensional convolution layer to obtain the server temperature time sequence feature vector.
5. The high heat dissipation respiratory server of claim 4, wherein the fan speed timing correlation encoding unit comprises:
a fan rotation speed time sequence arrangement subunit, configured to arrange the rotation speed values of the first fan and the rotation speed values of the second fan at the multiple predetermined time points into a first fan rotation speed time sequence input vector and a second fan rotation speed time sequence input vector according to a time dimension, respectively;
a rotational speed cooperative correlation subunit, configured to calculate a rotational speed cooperative timing correlation matrix between the first fan rotational speed timing input vector and the second fan rotational speed timing input vector;
and the rotating speed collaborative timing sequence correlation feature extraction subunit is used for obtaining the rotating speed collaborative timing sequence correlation feature vector by the rotating speed collaborative pattern feature extractor based on the convolutional neural network model through the rotating speed collaborative timing sequence correlation matrix.
6. The high heat dissipation respiratory server of claim 5, wherein the heat dissipation-heat source feature interaction analysis unit is configured to: and performing feature interaction based on an attention mechanism on the rotation speed collaborative time sequence association feature vector and the server temperature time sequence feature vector by using an inter-feature attention interaction layer to obtain a heat dissipation-heat source interaction feature vector as the heat dissipation-heat source interaction feature.
7. The high heat dissipation respiratory server of claim 6, wherein the fan speed control module comprises:
the feature optimization unit is used for performing feature distribution optimization on the heat dissipation-heat source interaction feature vector to obtain an optimized heat dissipation-heat source interaction feature vector;
the rotational speed classification control unit is used for enabling the optimized heat radiation-heat source interaction characteristic vector to pass through a classifier to obtain a classification result, wherein the classification result is used for indicating that the rotational speed value of the first fan at the current time point is increased or reduced, and the rotational speed value of the second fan at the current time point is increased or reduced.
8. The high heat dissipation respiratory server of claim 7, wherein the feature optimization unit comprises:
the space self-adaptive point learning optimization subunit is used for carrying out non-homogeneous Hilbert-face space self-adaptive point learning on the rotation speed collaborative time sequence associated feature vector and the server temperature time sequence feature vector so as to obtain a fusion feature vector;
and the fusion optimization subunit is used for fusing the fusion feature vector and the heat dissipation-heat source interaction feature vector to obtain the optimized heat dissipation-heat source interaction feature vector.
9. The high-heat-dissipation breathing server of claim 8 wherein the spatially adaptive point learning optimization subunit is configured to: carrying out non-homogeneous Hilbert-face space self-adaptive point learning on the rotation speed collaborative time sequence association feature vector and the server temperature time sequence feature vector by using the following optimization formula to obtain the fusion feature vector;
wherein, the optimization formula is:wherein (1)>Is the rotation speed cooperative time sequence associated characteristic vector, < >>Is the server temperature time sequence feature vector, < >>Is a transpose of the server temperature timing feature vector,>，/>and->Representing a non-homogeneous minpoint distance based on Gilbert space, and +.>Andis super-parameter (herba Cinchi Oleracei)>And->Feature vector +.>And->Is defined as the global feature mean value of (2), and feature vector +.>And->Are all row vectors, +.>Representing multiplication by location +.>Representing addition by position +.>Is covariance matrix>Is the fusion feature vector.
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| CN118224112A (en) * | 2024-02-28 | 2024-06-21 | 宁波瑞能智慧科技股份有限公司 | Intelligent electric fan control system and method based on Internet of things technology |
| CN118265275A (en) * | 2024-05-30 | 2024-06-28 | 山西新泰富安新材有限公司 | Air cooling line control cooling optimization control method |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN118224112A (en) * | 2024-02-28 | 2024-06-21 | 宁波瑞能智慧科技股份有限公司 | Intelligent electric fan control system and method based on Internet of things technology |
| CN118265275A (en) * | 2024-05-30 | 2024-06-28 | 山西新泰富安新材有限公司 | Air cooling line control cooling optimization control method |
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