CN108921223B - Server cooling system, control method, computer program and computer - Google Patents

Server cooling system, control method, computer program and computer Download PDF

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CN108921223B
CN108921223B CN201810732051.1A CN201810732051A CN108921223B CN 108921223 B CN108921223 B CN 108921223B CN 201810732051 A CN201810732051 A CN 201810732051A CN 108921223 B CN108921223 B CN 108921223B
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CN108921223A (en
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韩琳
邵忠良
黄诚
邓桂芳
曹薇
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Guangdong Polytechnic Of Water Resources And Electric Engineering
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Abstract

The invention belongs to the technical field of server cooling, and discloses a server cooling system, a control method, a computer program and a computer, wherein the air quantity data of a server fan is detected by an air quantity detection module; the central control module dispatches the air cooling module to carry out cooling operation through blowing by a fan; cooling operation is carried out by adopting liquid through the liquid cooling module; the safety state of the server hard disk is automatically detected and maintained through an automatic maintenance module; and judging whether the temperature and the air volume are abnormal or not through the alarm module according to the detected temperature and air volume data, and timely alarming if the temperature and the air volume are abnormal. According to the invention, the air quantity detection module is convenient for the server to output air quantity information according to the self running state of the power supply module (namely the power supply of the server), so that the radiator of the data center can realize accurate monitoring on the air quantity of the server; meanwhile, the automatic maintenance module can find the abnormality of the hard disk in time, and can maintain the hard disk in time, and has the advantages of low maintenance cost and high reliability.

Description

Server cooling system, control method, computer program and computer
Technical Field
The invention belongs to the technical field of server cooling, and particularly relates to a server cooling system, a control method, a computer program and a computer.
Background
Currently, the current state of the art commonly used in the industry is such that:
servers, also known as servers, are devices that provide computing services. Since the server needs to respond to and process the service request, the server generally has the capability of assuming and securing the service. The server is constructed to include a processor, a hard disk, a memory, a system bus, etc., similar to a general-purpose computer architecture, but requires high processing power, stability, reliability, security, scalability, manageability, etc., due to the need to provide highly reliable services. However, the existing server cooling system cannot timely and accurately detect the air volume data of the server fan, and is not favorable for monitoring the heat dissipation of the server; meanwhile, if the server hard disk is abnormal, the server hard disk cannot be maintained in time, and the server is paralyzed.
In summary, the problems of the prior art are as follows:
the existing server cooling system cannot accurately detect the air volume data of the server fan in time, and is not favorable for monitoring the heat dissipation of the server; meanwhile, if the server hard disk is abnormal, the server hard disk cannot be maintained in time, and the server is paralyzed.
The deviation threat from temperature has its unique features such as ambiguity, uncertainty and timeliness, and therefore the temperature deviation threat cannot be quantitatively analyzed by conventional methods such as functional methods. Such as the engineering mathematical model evaluation method, they have great limitations, because the approximate effect of the simulation reasoning is not optimistic due to the constraint of self conditions. And lack the timeliness analysis to the deviation information of temperature, can not reflect the temperature deviation threat in the change rule of different moments. Resulting in an inability to accurately control the temperature.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a server cooling system and a control method.
The present invention is achieved as described above, and a method for controlling a server cooling system includes:
the received temperature signal s (t) is subjected to nonlinear transformation by a temperature detection module according to the following formula:
Figure BDA0001721138340000021
wherein
Figure BDA0001721138340000022
A represents the amplitude of the signal, a (m) represents the symbol sign of the signal, p (t) represents the shaping function, fcWhich is indicative of the carrier frequency of the signal,
Figure BDA0001721138340000025
representing the phase of the signal and obtained by the non-linear transformation
Figure BDA0001721138340000023
Detecting the working temperature data of the server;
the air volume detection module detects fan air volume data of the server by using the received fan air volume signal y (t), wherein the y (t) is expressed as:
y(t)＝x(t)+n(t)；
wherein, x (t) is a digital modulation signal, n (t) is pulse noise distributed according to a standard S alpha S, and the analytic form of x (t) is expressed as:
Figure BDA0001721138340000024
wherein N is the number of sampling points, anFor the transmitted information symbols, in the MASK signal, an0,1,2, …, M-1, M being the modulation order, an＝ej2πε/MWhere e is 0,1,2, …, M-1, g (T) denotes a rectangular shaping pulse, TbDenotes the symbol period, fcIndicating carrier frequency, carrier initial phase
Figure BDA0001721138340000026
Is at [0,2 π]Random numbers uniformly distributed therein;
the central control module dispatches the air cooling module to carry out cooling operation through blowing by a fan; cooling operation is carried out by adopting liquid through the liquid cooling module;
in the central control module dispatching air cooling module, quantizing the collected server temperature information and server fan air volume information according to the divided quantization levels, and establishing an observation evidence table;
establishing a conditional probability transition matrix between states by using expert knowledge or experience, and determining a state transition matrix between time slices;
establishing a discrete dynamic Bayesian network model of the temperature threat level and the factors influencing the temperature;
calculating a final temperature threat level by using a hidden Markov inference algorithm according to the established observation evidence table, the established conditional transition probability table and the established state transition probability table; sending a control instruction, and scheduling the air cooling module;
the safety state of the server hard disk is automatically detected and maintained through an automatic maintenance module;
and judging whether the temperature and the air volume are abnormal or not through the alarm module according to the detected temperature and air volume data, and timely alarming if the temperature and the air volume are abnormal.
Furthermore, the discrete dynamic Bayesian network model is a directed acyclic graph formed by observation nodes and state nodes, the server temperature and the server fan air volume jointly form discrete state nodes, and the temperature threat level is the observation nodes.
Further, the established observation evidence table, the established conditional transition probability table and the established state transition probability table are combined with the established discrete dynamic Bayesian network model to determine a final threat level, namely the probability of the maximum possible value of the observation node based on a large amount of state node data in the Bayesian inference process;
the method specifically comprises the following steps: the process of reasoning the probability P (Y | lambda) by the system parameter lambda and the observation sequence Y and the forward-backward algorithm is as follows:
forward algorithm, defining a forward variable alphat(i)＝P(y1,y2,...,yt,xt＝i|λ)
Initialization: alpha is alpha1(i)＝πibi(y1), 1≤i≤n
And (3) recursive operation:
Figure BDA0001721138340000031
as a result:
Figure BDA0001721138340000032
backward algorithm, defining backward variable betat(i)＝P(yt+1,yt+2,...,yT|xt＝i,λ)
Initialization: beta is aT(i)＝1, 1≤i≤n
And (3) recursive operation:
Figure BDA0001721138340000033
as a result:
Figure BDA0001721138340000041
combining a forward algorithm and a backward algorithm to form a forward-backward algorithm:
Figure BDA0001721138340000042
finally, according to the established observation evidence table, the condition transition probability table and the state transition probability table, combining a forward-backward algorithm to deduce the temperature threat level of the UAV;
the state set of each node in the discrete dynamic Bayesian network model is represented by S, and each factor is distinguished by subscript as follows:
STTsevere, normal.
Further, the air volume detection method comprises the following steps:
firstly, respectively based on the PWM value of the power supply fan currently acquired by the power supply fan;
then, according to a pre-established corresponding function relation formula Q ═ f (PWM) of the ventilation quantity Q of the power supply module of the cabinet server and the PWM value of the power supply fan; and calculating the current ventilation Q of the current power supply module of the server to be monitored, and then outputting the currently calculated ventilation Q.
Further, the automatic hard disk maintenance method comprises the following steps:
firstly, detecting the state of a hard disk;
then, judging whether the hard disk is abnormal or not, if so, receiving abnormal information of the hard disk and the position information of the area where the hard disk in the abnormal state is located, positioning the hard disk in the abnormal state and identifying the hard disk in the abnormal state;
finally, controlling the robot to move to the area position of the hard disk in the abnormal state according to the abnormal information and the positioning information of the hard disk in the abnormal state; and the robot identifies the identification of the hard disk in the abnormal state so as to acquire the positioning information of the hard disk in the abnormal state, and takes out the hard disk in the abnormal state and replaces the hard disk with a normally working hard disk.
Another object of the present invention is to provide a computer program that implements the control method of the server cooling system.
Another object of the present invention is to provide an information computer that implements the control method of the server cooling system.
Another object of the present invention is a computer-readable storage medium including instructions which, when run on a computer, cause the computer to execute the control method of the server cooling system.
Another object of the present invention is to provide a server cooling system implementing the control method, the server cooling system including:
the power supply module is connected with the central control module and used for supplying power to each module of the server;
the temperature detection module is connected with the central control module and used for detecting the working temperature data of the server through a temperature sensor;
the air quantity detection module is connected with the central control module and is used for detecting the air quantity data of the server fan;
the central control module is connected with the power supply module, the temperature detection module, the air quantity detection module, the air cooling module, the liquid cooling module, the automatic maintenance module and the alarm module and is used for controlling the modules to work normally;
the air cooling module is connected with the central control module and is used for cooling operation through blowing air by a fan;
the liquid cooling module is connected with the central control module and is used for cooling operation through liquid;
the automatic maintenance module is connected with the central control module and is used for automatically detecting the safety state of the server hard disk and maintaining the server hard disk;
and the alarm module is connected with the central control module and used for judging whether the temperature and the air volume are abnormal or not according to the detected temperature and air volume data, and giving an alarm in time if the temperature and the air volume are abnormal.
Another object of the present invention is a server for industrial control, which carries the server cooling system.
The invention has the advantages and positive effects that:
according to the invention, the air quantity detection module is convenient for the server to output air quantity information according to the self running state of the power supply module (namely the power supply of the server), so that the radiator of the data center can realize accurate monitoring on the air quantity of the server; meanwhile, the automatic maintenance module can find the abnormality of the hard disk in time, and can maintain the hard disk in time, and has the advantages of low maintenance cost and high reliability.
The invention carries out nonlinear transformation on the received temperature signal s (t) through a temperature detection module according to the following formula:
Figure BDA0001721138340000061
wherein
Figure BDA0001721138340000062
A represents the amplitude of the signal, a (m) represents the symbol sign of the signal, p (t) represents the shaping function, fcWhich is indicative of the carrier frequency of the signal,
Figure BDA0001721138340000065
representing the phase of the signal and obtained by the non-linear transformation
Figure BDA0001721138340000063
Detecting the working temperature data of the server;
the air volume detection module detects fan air volume data of the server by using the received fan air volume signal y (t), wherein the y (t) is expressed as:
y(t)＝x(t)+n(t)；
wherein, x (t) is a digital modulation signal, n (t) is pulse noise distributed according to a standard S alpha S, and the analytic form of x (t) is expressed as:
Figure BDA0001721138340000064
wherein N is the number of sampling points, anFor the transmitted information symbols, in the MASK signal, an0,1,2, …, M-1, M being the modulation order, an＝ej2πε/MWhere e is 0,1,2, …, M-1, g (T) denotes a rectangular shaping pulse, TbDenotes the symbol period, fcIndicating carrier frequency, carrier initial phase
Figure BDA0001721138340000066
Is at [0,2 π]Random numbers uniformly distributed therein; accurate temperature and air volume signals can be obtained, and basis is provided for the next intelligent control.
The method realizes the organic combination of the continuous observed value and the discrete dynamic Bayesian network, integrates all important factors related to the temperature deviation threat degree to judge and reason, establishes a discrete dynamic Bayesian network model (shown in figure 3) suitable for the temperature deviation threat degree reasoning, and deduces the probability distribution of the threat degree by combining; the effectiveness, the practicability and the accuracy of the temperature deviation evaluation are greatly improved; compared with a static Bayesian network, the discrete dynamic Bayesian network utilizes node information of an adjacent time period, so that the accuracy of the inference result is higher, and the discrete dynamic Bayesian network can still infer a more correct temperature deviation threat level under the condition that data is abnormal or uncertain.
Drawings
FIG. 1 is a flow chart of a method for controlling a server cooling system in accordance with an embodiment of the present invention.
FIG. 2 is a block diagram of a server cooling system provided by the practice of the present invention.
In the figure: 1. a power supply module; 2. a temperature detection module; 3. an air quantity detection module; 4. a central control module; 5. an air-cooled module; 6. a liquid cooling module; 7. an automatic maintenance module; 8. and an alarm module.
FIG. 3 is a diagram of a discrete dynamic Bayesian network model suitable for threat degree inference on temperature deviation provided by the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The application of the principles of the present invention will be further described with reference to the accompanying drawings and specific embodiments.
As shown in fig. 1, the server cooling system and the control method provided by the present invention include the following steps:
s101, supplying power to each module of the server through a power supply module;
s102, detecting the working temperature data of the server through a temperature detection module; detecting fan air volume data of the server through an air volume detection module;
s103, the central control module dispatches the air cooling module to carry out cooling operation through fan blowing; cooling operation is carried out by adopting liquid through the liquid cooling module;
s104, automatically detecting the safety state of the server hard disk through an automatic maintenance module and maintaining;
and S105, judging whether the air flow is abnormal or not through the alarm module according to the detected temperature and air volume data, and timely alarming if the air flow is abnormal.
As shown in fig. 2, the server cooling system provided by the embodiment of the present invention includes: the device comprises a power module 1, a temperature detection module 2, an air quantity detection module 3, a central control module 4, an air cooling module 5, a liquid cooling module 6, an automatic maintenance module 7 and an alarm module 8.
The power module 1 is connected with the central control module 4 and used for supplying power to each module of the server;
the temperature detection module 2 is connected with the central control module 4 and used for detecting the working temperature data of the server through a temperature sensor;
the air volume detection module 3 is connected with the central control module 4 and is used for detecting the fan air volume data of the server;
the central control module 4 is connected with the power supply module 1, the temperature detection module 2, the air quantity detection module 3, the air cooling module 5, the liquid cooling module 6, the automatic maintenance module 7 and the alarm module 8 and is used for controlling the normal work of each module;
the air cooling module 5 is connected with the central control module 4 and used for cooling operation through fan blowing;
the liquid cooling module 6 is connected with the central control module 4 and is used for carrying out cooling operation through liquid;
the automatic maintenance module 7 is connected with the central control module 4 and is used for automatically detecting the safety state of the server hard disk and maintaining the server hard disk;
and the alarm module 8 is connected with the central control module 4 and used for judging whether the temperature and the air volume are abnormal or not according to the detected temperature and air volume data, and giving an alarm in time if the temperature and the air volume are abnormal.
The detection method of the air volume detection module 3 provided by the embodiment of the invention is as follows:
firstly, respectively based on the PWM value of the power supply fan currently acquired by the power supply fan;
then, according to a pre-established corresponding function relation formula Q ═ f (PWM) of the ventilation quantity Q of the power supply module of the cabinet server and the PWM value of the power supply fan; and calculating the current ventilation Q of the current power supply module of the server to be monitored, and then outputting the currently calculated ventilation Q.
The maintenance method of the automatic maintenance module 7 provided by the invention comprises the following steps:
firstly, detecting the state of a hard disk;
then, judging whether the hard disk is abnormal or not, if so, receiving abnormal information of the hard disk and the position information of the area where the hard disk in the abnormal state is located, positioning the hard disk in the abnormal state and identifying the hard disk in the abnormal state;
finally, controlling the robot to move to the area position of the hard disk in the abnormal state according to the abnormal information and the positioning information of the hard disk in the abnormal state; and the robot identifies the identification of the hard disk in the abnormal state so as to acquire the positioning information of the hard disk in the abnormal state, and takes out the hard disk in the abnormal state and replaces the hard disk with a normally working hard disk.
The invention is further described below with reference to specific assays.
The control method of the server cooling system provided by the embodiment of the invention comprises the following steps:
the received temperature signal s (t) is subjected to nonlinear transformation by a temperature detection module according to the following formula:
Figure BDA0001721138340000091
wherein
Figure BDA0001721138340000092
A represents the amplitude of the signal, a (m) represents the symbol sign of the signal, p (t) represents the shaping function, fcWhich is indicative of the carrier frequency of the signal,
Figure BDA0001721138340000095
representing the phase of the signal and obtained by the non-linear transformation
Figure BDA0001721138340000093
Detecting the working temperature data of the server;
the air volume detection module detects fan air volume data of the server by using the received fan air volume signal y (t), wherein the y (t) is expressed as:
y(t)＝x(t)+n(t)；
wherein, x (t) is a digital modulation signal, n (t) is pulse noise distributed according to a standard S alpha S, and the analytic form of x (t) is expressed as:
Figure BDA0001721138340000094
wherein N is the number of sampling points, anFor the transmitted information symbols, in the MASK signal, an0,1,2, …, M-1, M being the modulation order, an＝ej2πε/MWhere e is 0,1,2, …, M-1, g (T) denotes a rectangular shaping pulse, TbDenotes the symbol period, fcIndicating carrier frequency, carrier initial phase
Figure BDA0001721138340000096
Is at [0,2 π]Random numbers uniformly distributed therein;
the central control module dispatches the air cooling module to carry out cooling operation through blowing by a fan; cooling operation is carried out by adopting liquid through the liquid cooling module;
in the central control module dispatching air cooling module, quantizing the collected server temperature information and server fan air volume information according to the divided quantization levels, and establishing an observation evidence table;
establishing a conditional probability transition matrix between states by using expert knowledge or experience, and determining a state transition matrix between time slices;
establishing a discrete dynamic Bayesian network model of the temperature threat level and the factors influencing the temperature;
calculating a final temperature threat level by using a hidden Markov inference algorithm according to the established observation evidence table, the established conditional transition probability table and the established state transition probability table; sending a control instruction, and scheduling the air cooling module;
the safety state of the server hard disk is automatically detected and maintained through an automatic maintenance module;
and judging whether the temperature and the air volume are abnormal or not through the alarm module according to the detected temperature and air volume data, and timely alarming if the temperature and the air volume are abnormal.
The discrete dynamic Bayesian network model is a directed acyclic graph formed by observation nodes and state nodes, the server temperature and the server fan air volume jointly form discrete state nodes, and the temperature threat level is the observation nodes.
The established observation evidence table, the established conditional transition probability table and the established state transition probability table are combined with the established discrete dynamic Bayesian network model to determine a final threat level, namely a probability that the maximum possible value of the observation node is inferred according to a large amount of state node data in the Bayesian inference process;
the method specifically comprises the following steps: the process of reasoning the probability P (Y | lambda) by the system parameter lambda and the observation sequence Y and the forward-backward algorithm is as follows:
forward algorithm, defining a forward variable alphat(i)＝P(y1,y2,...,yt,xt＝i|λ)
Initialization: alpha is alpha1(i)＝πibi(y1), 1≤i≤n
And (3) recursive operation:
Figure BDA0001721138340000101
as a result:
Figure BDA0001721138340000102
backward algorithm, defining backward variable betat(i)＝P(yt+1,yt+2,...,yT|xt＝i,λ)
Initialization: beta is aT(i)＝1, 1≤i≤n
And (3) recursive operation:
Figure BDA0001721138340000103
as a result:
Figure BDA0001721138340000111
combining a forward algorithm and a backward algorithm to form a forward-backward algorithm:
Figure BDA0001721138340000112
finally, according to the established observation evidence table, the condition transition probability table and the state transition probability table, combining a forward-backward algorithm to deduce the temperature threat level of the UAV;
the state set of each node in the discrete dynamic Bayesian network model is represented by S, and each factor is distinguished by subscript as follows:
STTsevere, normal.
FIG. 3 is a diagram of a discrete dynamic Bayesian network model suitable for threat degree inference on temperature deviation provided by the present invention.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When used in whole or in part, can be implemented in a computer program product that includes one or more computer instructions. When loaded or executed on a computer, cause the flow or functions according to embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center via wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL), or wireless (e.g., infrared, wireless, microwave, etc.)). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that includes one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.

Claims (9)

1. A control method of a server cooling system, characterized by comprising:
the received temperature signal s (t) is subjected to nonlinear transformation by a temperature detection module according to the following formula:
Figure FDA0003256946850000011
wherein
Figure FDA0003256946850000012
A represents the amplitude of the signal, a (m) represents the symbol sign of the signal, p (t) represents the shaping function, fcWhich is indicative of the carrier frequency of the signal,
Figure FDA0003256946850000013
representing the phase of the signal and obtained by the non-linear transformation
Figure FDA0003256946850000014
Detecting the working temperature data of the server;
the air volume detection module detects fan air volume data of the server by using the received fan air volume signal y (t), wherein the y (t) is expressed as:
y(t)＝x(t)+n(t)；
wherein, x (t) is a digital modulation signal, n (t) is pulse noise distributed according to a standard S alpha S, and the analytic form of x (t) is expressed as:
Figure FDA0003256946850000015
wherein N is the number of sampling points, anFor the transmitted information symbols, in the MASK signal,n is 0,1,2, …, M-1, M is the modulation order, an＝ej2πε/MWhere e is 0,1,2, …, M-1, g (T) denotes a rectangular shaping pulse, TbDenotes the symbol period, fcIndicating carrier frequency, carrier initial phase
Figure FDA0003256946850000016
Is at [0,2 π]Random numbers uniformly distributed therein;
the central control module dispatches the air cooling module to carry out cooling operation through blowing by a fan; cooling operation is carried out by adopting liquid through the liquid cooling module;
in the central control module dispatching air cooling module, quantizing the collected server temperature information and server fan air volume information according to the divided quantization levels, and establishing an observation evidence table;
establishing a conditional probability transition matrix between states by using expert knowledge or experience, and determining a state transition matrix between time slices;
establishing a discrete dynamic Bayesian network model of the temperature threat level and the factors influencing the temperature;
calculating a final temperature threat level by using a hidden Markov inference algorithm according to the established observation evidence table, the established conditional transition probability table and the established state transition probability table; sending a control instruction, and scheduling the air cooling module;
the safety state of the server hard disk is automatically detected and maintained through an automatic maintenance module;
and judging whether the temperature and the air volume are abnormal or not through the alarm module according to the detected temperature and air volume data, and timely alarming if the temperature and the air volume are abnormal.
2. The control method of a server cooling system according to claim 1,
the discrete dynamic Bayesian network model is a directed acyclic graph formed by observation nodes and state nodes, the server temperature and the server fan air volume jointly form discrete state nodes, and the temperature threat level is the observation nodes.
3. The method for controlling a server cooling system according to claim 1, wherein the established observation evidence table, the condition transition probability table, and the state transition probability table are combined with the established discrete dynamic bayesian network model to determine a final threat level, which is a probability that the bayesian inference process infers a maximum possible value of an observation node based on a large amount of state node data;
the method specifically comprises the following steps: the process of reasoning the probability P (Y | lambda) by the system parameter lambda and the observation sequence Y and the forward-backward algorithm is as follows:
forward algorithm, defining a forward variable alphat(i)＝P(y1,y2,...,yt,xt＝i|λ)
Initialization: alpha is alpha1(i)＝πibi(y1),1≤i≤n
And (3) recursive operation:
Figure FDA0003256946850000021
as a result:
Figure FDA0003256946850000022
backward algorithm, defining backward variable betat(i)＝P(yt+1,yt+2,...,yT|xt＝i,λ)
Initialization: beta is aT(i)＝1,1≤i≤n
And (3) recursive operation:
Figure FDA0003256946850000023
as a result:
Figure FDA0003256946850000031
combining a forward algorithm and a backward algorithm to form a forward-backward algorithm:
Figure FDA0003256946850000032
finally, according to the established observation evidence table, the condition transition probability table and the state transition probability table, combining a forward-backward algorithm to deduce the temperature threat level of the UAV;
the state set of each node in the discrete dynamic Bayesian network model is represented by S, and each factor is distinguished by subscript as follows:
STTsevere, normal.
4. The control method of the server cooling system according to claim 1, wherein the air volume detection method includes:
firstly, respectively based on the PWM value of the power supply fan currently acquired by the power supply fan;
then, according to a pre-established corresponding function relation formula Q ═ f (PWM) of the ventilation quantity Q of the power supply module of the cabinet server and the PWM value of the power supply fan; and calculating the current ventilation Q of the current power supply module of the server to be monitored, and then outputting the currently calculated ventilation Q.
5. The control method of the server cooling system according to claim 1, wherein the hard disk automatic maintenance method comprises:
firstly, detecting the state of a hard disk;
then, judging whether the hard disk is abnormal or not, if so, receiving abnormal information of the hard disk and the position information of the area where the hard disk in the abnormal state is located, positioning the hard disk in the abnormal state and identifying the hard disk in the abnormal state;
finally, controlling the robot to move to the area position of the hard disk in the abnormal state according to the abnormal information and the positioning information of the hard disk in the abnormal state; and the robot identifies the identification of the hard disk in the abnormal state so as to acquire the positioning information of the hard disk in the abnormal state, and takes out the hard disk in the abnormal state and replaces the hard disk with a normally working hard disk.
6. An information computer for implementing the method for controlling a server cooling system according to any one of claims 1 to 5.
7. A computer-readable storage medium comprising instructions that, when executed on a computer, cause the computer to perform the method of controlling a server cooling system according to any one of claims 1 to 5.
8. A server cooling system that implements the control method according to claim 1, the server cooling system comprising:
the power supply module is connected with the central control module and used for supplying power to each module of the server;
the temperature detection module is connected with the central control module and used for detecting the working temperature data of the server through a temperature sensor;
the air quantity detection module is connected with the central control module and is used for detecting the air quantity data of the server fan;
the central control module is connected with the power supply module, the temperature detection module, the air quantity detection module, the air cooling module, the liquid cooling module, the automatic maintenance module and the alarm module and is used for controlling the modules to work normally;
the air cooling module is connected with the central control module and is used for cooling operation through blowing air by a fan;
the liquid cooling module is connected with the central control module and is used for cooling operation through liquid;
the automatic maintenance module is connected with the central control module and is used for automatically detecting the safety state of the server hard disk and maintaining the server hard disk;
and the alarm module is connected with the central control module and used for judging whether the temperature and the air volume are abnormal or not according to the detected temperature and air volume data, and giving an alarm in time if the temperature and the air volume are abnormal.
9. A server for industrial control carrying the server cooling system of claim 8.
CN201810732051.1A 2018-07-05 2018-07-05 Server cooling system, control method, computer program and computer Expired - Fee Related CN108921223B (en)

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