CN116880278A - Control method for efficient operation of precision air conditioner of data center - Google Patents
Control method for efficient operation of precision air conditioner of data center Download PDFInfo
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- H—ELECTRICITY
- H05—ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
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Abstract
The invention relates to the technical field of data center cold sources, and discloses a control method for efficient operation of a precision air conditioner of a data center, which comprises the following steps: step one, utilizing historical up-sampling data to construct an initial training data set, defining a time interval between every two adjacent sampling moments as a sampling period, and defining a prediction period on each sampling period, wherein the length of the prediction period is the prediction depth of a temperature difference prediction model. According to the control method for the efficient operation of the precise air conditioner of the data center, the Bayesian linear regression model is initially trained, the control method is used for predicting the temperature difference of the temperature sensor of the data center at the future time, newly-increased training data is obtained in real time in the control process, the Bayesian linear regression model, the controlled variable traversal and the optimal control are closely connected, the instantaneity of energy-saving control is effectively improved under the combined action, the Bayesian linear regression prediction model is updated in real time, and the temperature difference of the sensor is predicted in real time.
Description
Technical Field
The invention relates to the technical field of data center cold sources, in particular to a control method for efficient operation of a precision air conditioner of a data center.
Background
At present, the control logic of the room-level precise air conditioner of the data center is that a temperature sensor in the precise air conditioning equipment collects temperature and then transmits the temperature to a precise air conditioner controller, and an execution instruction is pushed to execution parts such as an EC fan, a water valve and the like through a control unit.
The Chinese patent publication No. CN105972766A discloses a double-power-supply multi-precision air conditioner control system of a data center, which comprises a host protection control system and a slave protection control system, wherein the cores of the host protection control system and the slave protection control system are both in a double-CPU structure, and a communication module, an LCD display module, a man-machine interface module and a control and protection module are respectively arranged under the CPU structure; the host protection control system and the slave protection control system exchange information through handshake signals, and communication modules of the host protection control system and the slave protection control system are connected to a CAN bus; the control and protection modules of the host protection control system and the slave protection control system are respectively connected with a power supply and a circuit breaker, the precise air conditioner is connected to each circuit breaker, a double CPU structure is adopted, each CPU has definite work division, and the working efficiency and the practicability of the system can be improved; meanwhile, the dual power supply from the power grid supplies power to the host protection control system and the slave protection control system respectively.
However, the invention has the following problems:
because the temperature data of control execution comes from the sensor in the precise air conditioner, the real temperature data is at a distance from the acquisition point for controlling the operation of the precise air conditioner and is single-point data, so that the temperature data has certain distortion.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the invention provides a control method for the high-efficiency operation of a precision air conditioner of a data center, which has the advantages of energy conservation, high efficiency and the like, and solves the problem that the temperature data has certain distortion due to the fact that the real temperature data is separated from an acquisition point for controlling the operation of the precision air conditioner and is single-point data.
(II) technical scheme
In order to achieve the above purpose, the present invention provides the following technical solutions: a control method for efficient operation of a precision air conditioner of a data center comprises the following steps:
firstly, constructing an initial training data set by utilizing historical up-sampling data, defining a time interval between every two adjacent sampling moments as a sampling period, defining a prediction period on each sampling period, wherein the length of the prediction period is the prediction depth of a temperature difference prediction model, and the initial moments of the prediction periods are the same sampling period initial moment;
step two, taking heat source data of a cloud server and control quantity data of a water-cooling precise air conditioner at the initial moment of a prediction period as model input data, taking actually measured sensor temperature difference at the end moment of the same prediction period as model output data, constructing an initial training data set, preprocessing the initial training data set, assuming that a specified maximum temperature threshold is T, namely, the temperature of a large data center or a machine room is controlled to be less than or equal to a range of I, enabling a total of N air temperatures, assuming that the temperature report value of N air temperatures exceeds the maximum temperature threshold T at a certain moment, and if n=0 and the average temperature value T of the N air temperatures satisfy the following conditionsThen control to maintain the present state, T 0 Is the lowest temperature threshold; if n=0 and the average temperature value T of N air temperature meters<T 0 The target temperature value of the central air conditioning system is adjusted to be higher so that the average temperature value of N air temperature meters meets +.>
Thirdly, using the preprocessed initial training data set to perform initial training on the Bayesian linear regression model to obtain an initial temperature difference prediction model;
acquiring a new training data set, wherein the new training data set comprises heat source data of a cloud server at the initial time of a prediction period when the initial time of a current sampling period is ended, control quantity data of a water-cooling precision air conditioner at the initial time of the prediction period when the initial time of the current sampling period is ended, and actually measured sensor temperature difference of a machine room at the end time of the prediction period when the initial time of the current sampling period is ended;
step five, preprocessing a newly added training data set, and performing online incremental training on the Bayesian linear regression model in an incremental mode by using the preprocessed newly added training data set;
step six, based on the current Bayesian linear regression model, searching the optimal combination of the opening of the electric water valve, the frequency of the EC fan and the air conditioner by predicting the temperature difference of the sensor at the end time of the prediction period defined on the current sampling period and adopting implementation; the opening of the electric water valve, the gear of the EC fan and the gear of the air conditioning frequency are discrete gears;
step seven, judging whether the flow is ended; if yes, ending the flow; if not, the next sampling period of the current sampling period is skipped to the fourth step for continuous execution.
Preferably, the heat source data comprises performance data and temperature data of a cloud server component, the control quantity data comprises opening degree of an electric water valve and air conditioning frequency, and the temperature difference of the sensor is the difference value between the temperature of a return air position and the temperature of an outlet air position of the water-cooling precise air conditioner; the measured sensor temperature difference is the difference between the measured return air position temperature and the measured outlet air position temperature of the water-cooling precise air conditioner.
Preferably, traversing the predicted sensor temperature difference at the end time of the prediction period defined on the current sampling period respectively obtained by taking the heat source data at the initial time of the current sampling period and each element in the gear combination set as the input of the current Bayesian linear regression model, and resetting the opening degree of the electric water valve and the air conditioning frequency of the water-cooling precise air conditioner in the current sampling period according to the element corresponding to the predicted sensor temperature difference which is smaller than and closest to the target temperature difference threshold.
Preferably, the gear combination set is a complete set of elements obtained by pairwise pairing between the opening degree of the electric water valve of different gears and the air conditioning frequency of different gears.
Preferably, if N/N is greater than f1, the target temperature of the central air conditioning system is regulated down to be theta, and theta is smaller than T until the temperature report values of all the air conditioners do not exceed the highest temperature threshold T; if N/N is less than or equal to f1, starting m air conditioning single units until the temperature report values of all the air temperature meters do not exceed a highest temperature threshold T, wherein m < = N and m is less than the number of the air conditioning single units, and f1 is the current overheat/cool cooling coefficient of the temperature of the cabinet.
Preferably, in the initial training stage of Bayesian linear regression, model parameter prior distribution is selected to obey a given initial super-parameter as mu 0 ，Λ 0 ，a 0 ，b 0 After initial training, bayesian linear regression model is subjected to model parameter posterior distribution obeying super-parameter as mu n ，Λ n ，a n ，b n Wherein n represents the last sampling period in the initial training dataset; super parameter mu n ，Λ n ，a n ，b n All are mu 0 ，Λ 0 ，a 0 ，b 0 And a simple function of the initial training sample set.
Preferably, the performance data includes any one or more data of a fan rotation speed of each cloud server node of the machine room, a CPU frequency of each cloud server node of the machine room, and a memory usage rate of each cloud server node of the machine room.
Preferably, the initial moments of the two adjacent prediction periods are respectively the initial moments of the two adjacent sampling periods.
Preferably, the temperature of each monitoring point is subtracted from the average temperature of the environment to obtain a temperature difference value corresponding to each monitoring point, and if the temperature difference value corresponding to each monitoring point is larger than a preset temperature difference running value or the temperature of the monitoring point is larger than an alarm upper limit value, the monitoring point is judged to be a hot spot.
(III) beneficial effects
Compared with the prior art, the invention provides a control method for efficiently running a precision air conditioner of a data center, which has the following beneficial effects:
according to the control method for the efficient operation of the precise air conditioner of the data center, the Bayesian linear regression model is initially trained, the temperature difference of the temperature sensor of the data center at the future time is predicted, newly-increased training data is obtained in real time in the control process, the Bayesian linear regression model, the control quantity traversal and the optimal control are closely connected, the instantaneity of energy-saving control is effectively improved under the combined action, the Bayesian linear regression prediction model is updated in real time, the temperature difference of the sensor is predicted in real time, the optimal control quantity of the combination of discrete compressor frequency and water valve opening is obtained in real time by utilizing the grid search algorithm, the heat source data at the initial moment of a sampling period and the Bayesian linear regression model are matched, the problem that the distance exists between the heat source data and the acquisition point of the operation of the precise air conditioner is improved can be solved, the operation energy consumption of the precise air conditioner is greatly reduced, the stable transmission of the temperature data is guaranteed, and the effect of energy saving and emission reduction of the data center is achieved.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below in connection with the embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
A control method for efficient operation of a precision air conditioner of a data center comprises the following steps:
firstly, constructing an initial training data set by utilizing historical up-sampling data, defining a time interval between every two adjacent sampling moments as a sampling period, defining a prediction period on each sampling period, wherein the length of the prediction period is the prediction depth of a temperature difference prediction model, and the initial moments of the prediction periods are the same sampling period initial moment;
step two, taking heat source data of a cloud server and control quantity data of the water-cooling precise air conditioner at the initial moment of a prediction period as model input data, taking actual measured sensor temperature difference at the end moment of the same prediction period as model output data,an initial training data set is constructed, the initial training data set is preprocessed, a specified maximum temperature threshold is assumed to be T, namely, the temperature of a large data center or a machine room is controlled to be less than or equal to a range of I, N air temperatures are provided in total, the temperature report values of N air temperatures at a certain moment are assumed to exceed the maximum temperature threshold T, and if n=0 and the average temperature value T of the N air temperatures meets the following conditionsThen control to maintain the present state, T 0 Is the lowest temperature threshold; if n=0 and the average temperature value T of N air temperature meters<T 0 The target temperature value of the central air conditioning system is adjusted to be higher so that the average temperature value of N air temperature meters meets +.>
Thirdly, using the preprocessed initial training data set to perform initial training on the Bayesian linear regression model to obtain an initial temperature difference prediction model;
acquiring a new training data set, wherein the new training data set comprises heat source data of a cloud server at the initial time of a prediction period when the initial time of a current sampling period is ended, control quantity data of a water-cooling precision air conditioner at the initial time of the prediction period when the initial time of the current sampling period is ended, and actually measured sensor temperature difference of a machine room at the end time of the prediction period when the initial time of the current sampling period is ended;
step five, preprocessing a newly added training data set, and performing online incremental training on the Bayesian linear regression model in an incremental mode by using the preprocessed newly added training data set;
step six, based on the current Bayesian linear regression model, searching the optimal combination of the opening of the electric water valve, the frequency of the EC fan and the air conditioner by predicting the temperature difference of the sensor at the end time of the prediction period defined on the current sampling period and adopting implementation; the opening of the electric water valve, the gear of the EC fan and the gear of the air conditioning frequency are discrete gears;
step seven, judging whether the flow is ended; if yes, ending the flow; if not, the next sampling period of the current sampling period is skipped to the fourth step for continuous execution.
The heat source data comprise performance data and temperature data of cloud server components, the control quantity data comprise opening degree of an electric water valve and air conditioning frequency, and the temperature difference of the sensor is the difference value between the temperature of a return air position and the temperature of an outlet air position of the water-cooling precise air conditioner; the measured sensor temperature difference is the difference value between the measured return air position temperature and the measured outlet air position temperature of the water-cooling precise air conditioner, the predicted sensor temperature difference at the end time of a prediction period defined on the current sampling period and obtained respectively according to the heat source data at the initial time of the current sampling period and each element in a gear combination set serving as the input of the current Bayesian linear regression model is reset according to the element corresponding to the predicted sensor temperature difference which is smaller than and closest to the target temperature difference threshold value, the electric water valve opening and the air conditioning frequency of the water-cooling precise air conditioner in the current sampling period are reset, the gear combination set is a complete set of elements obtained by pairwise pairing the electric water valve opening of different gears and the air conditioning frequency of different gears, if N/N is larger than f1, the target temperature of the central air conditioning system is reduced to be theta, theta is smaller than T, and until the temperature report values of all air conditioners do not exceed the maximum temperature threshold value T; if N/N is less than or equal to f1, starting m air conditioning single units until the temperature report values of all the air conditioners do not exceed the highest temperature threshold T, m<N and m are smaller than the number of air conditioning units, wherein f1 is the cooling coefficient of the current overheat/cool of the cabinet temperature, and in the initial training stage of Bayesian linear regression, the prior distribution of model parameters is selected to obey a given initial super parameter to be mu 0 ，Λ 0 ，a 0 ，b 0 After initial training, bayesian linear regression model is subjected to model parameter posterior distribution obeying super-parameter as mu n ，Λ n ，a n ，b n Wherein n represents the last sampling period in the initial training dataset; super parameter mu n ，Λ n ，a n ，b n All are mu 0 ，Λ 0 ，a 0 ，b 0 And the performance data comprise any one or more data of the utilization rate of the internal memory of each cloud server node of the machine room, the initial moments of two continuous adjacent prediction periods are respectively the initial moments of two continuous adjacent sampling periods, the temperature of each monitoring point is subtracted from the average temperature of the environment to obtain the temperature difference value corresponding to each monitoring point, and if the temperature difference value corresponding to each monitoring point is larger than a preset temperature difference running value or the temperature of the monitoring point is larger than an alarm upper limit value, the monitoring point is judged to be a hot spot.
The beneficial effects of the invention are as follows: according to the control method for the efficient operation of the precise air conditioner of the data center, the Bayesian linear regression model is initially trained, the temperature difference of the temperature sensor of the data center at the future time is predicted, newly-increased training data is obtained in real time in the control process, the Bayesian linear regression model, the control quantity traversal and the optimal control are closely connected, the instantaneity of energy-saving control is effectively improved under the combined action, the Bayesian linear regression prediction model is updated in real time, the temperature difference of the sensor is predicted in real time, the optimal control quantity of the combination of discrete compressor frequency and water valve opening is obtained in real time by utilizing the grid search algorithm, the heat source data at the initial moment of a sampling period and the Bayesian linear regression model are matched, the problem that the distance exists between the heat source data and the acquisition point of the operation of the precise air conditioner is improved can be solved, the operation energy consumption of the precise air conditioner is greatly reduced, the stable transmission of the temperature data is guaranteed, and the effect of energy saving and emission reduction of the data center is achieved.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (9)
1. The control method for the efficient operation of the precision air conditioner of the data center is characterized by comprising the following steps of:
firstly, constructing an initial training data set by utilizing historical up-sampling data, defining a time interval between every two adjacent sampling moments as a sampling period, defining a prediction period on each sampling period, wherein the length of the prediction period is the prediction depth of a temperature difference prediction model, and the initial moments of the prediction periods are the same sampling period initial moment;
step two, taking heat source data of a cloud server and control quantity data of a water-cooling precise air conditioner at the initial moment of a prediction period as model input data, taking actually measured sensor temperature difference at the end moment of the same prediction period as model output data, constructing an initial training data set, preprocessing the initial training data set, assuming that a specified maximum temperature threshold is T, namely, the temperature of a large data center or a machine room is controlled to be less than or equal to a range of I, enabling a total of N air temperatures, assuming that the temperature report value of N air temperatures exceeds the maximum temperature threshold T at a certain moment, and if n=0 and the average temperature value T of the N air temperatures satisfy the following conditionsThen control to maintain the present state, T 0 Is the lowest temperature threshold; if n=0 and the average temperature value T of N air temperature meters is less than T 0 The target temperature value of the central air conditioning system is adjusted to be higher so that the average temperature value of N air temperature meters meets +.>
Thirdly, using the preprocessed initial training data set to perform initial training on the Bayesian linear regression model to obtain an initial temperature difference prediction model;
acquiring a new training data set, wherein the new training data set comprises heat source data of a cloud server at the initial time of a prediction period when the initial time of a current sampling period is ended, control quantity data of a water-cooling precision air conditioner at the initial time of the prediction period when the initial time of the current sampling period is ended, and actually measured sensor temperature difference of a machine room at the end time of the prediction period when the initial time of the current sampling period is ended;
step five, preprocessing a newly added training data set, and performing online incremental training on the Bayesian linear regression model in an incremental mode by using the preprocessed newly added training data set;
step six, based on the current Bayesian linear regression model, searching the optimal combination of the opening of the electric water valve, the frequency of the EC fan and the air conditioner by predicting the temperature difference of the sensor at the end time of the prediction period defined on the current sampling period and adopting implementation; the opening of the electric water valve, the gear of the EC fan and the gear of the air conditioning frequency are discrete gears;
step seven, judging whether the flow is ended; if yes, ending the flow; if not, the next sampling period of the current sampling period is skipped to the fourth step for continuous execution.
2. The control method for efficient operation of a precision air conditioner of a data center according to claim 1, wherein the heat source data comprises performance data and temperature data of a cloud server component, the control amount data comprises opening degree of an electric water valve and air conditioning frequency, and the sensor temperature difference is a difference value between a return air position temperature and an outlet air position temperature of the water-cooling precision air conditioner; the measured sensor temperature difference is the difference between the measured return air position temperature and the measured outlet air position temperature of the water-cooling precise air conditioner.
3. The control method for efficient operation of a precision air conditioner of a data center according to claim 1, wherein the predicted sensor temperature difference at the end time of a prediction period defined on a current sampling period obtained by traversing each element in a heat source data and gear combination set according to the initial time of the current sampling period as the input of a current bayesian linear regression model is reset according to the element corresponding to the predicted sensor temperature difference which is smaller than and closest to a target temperature difference threshold value.
4. The control method for efficient operation of a precision air conditioner of a data center according to claim 1, wherein the gear combination set is a complete set of elements obtained by pairwise pairing between electric water valve openings of different gears and air conditioning frequencies of different gears.
5. The control method for efficient operation of a precision air conditioner of a data center according to claim 1, wherein if N/N is greater than f1, the target temperature of the central air conditioning system is lowered to θ, θ being smaller than T, until the temperature report values of all the air conditioners do not exceed a maximum temperature threshold T; if N/N is less than or equal to f1, starting m air conditioning single units until the temperature report values of all the air temperature meters do not exceed a highest temperature threshold T, wherein m < = N and m is less than the number of the air conditioning single units, and f1 is the current overheat/cool cooling coefficient of the temperature of the cabinet.
6. The control method for efficient operation of a precision air conditioner of a data center according to claim 1, wherein, in a bayesian linear regression initial training phase, a model parameter prior distribution is selected to obey a given initial super parameter as μ 0 ，Λ 0 ，a 0 ，b 0 After initial training, bayesian linear regression model is subjected to model parameter posterior distribution obeying super-parameter as mu n ，Λ n ，a n ，b n Wherein n represents the last sampling period in the initial training dataset; super parameter mu n ，Λ n ，a n ，b n All are mu 0 ，A 0 ，a 0 ，b 0 And a simple function of the initial training sample set.
7. The method for controlling efficient operation of a precision air conditioner in a data center according to claim 1, wherein the performance data includes any one or more of a fan rotation speed of each cloud server node in a machine room, a CPU frequency of each cloud server node in the machine room, and a memory usage rate of each cloud server node in the machine room.
8. The control method for efficient operation of a precision air conditioner of a data center according to claim 1, wherein the initial moments of the two consecutive adjacent prediction periods are initial moments of the two consecutive adjacent sampling periods, respectively.
9. The control method for efficient operation of a precision air conditioner of a data center according to claim 1, wherein the temperature of each monitoring point is subtracted from the average temperature of the environment to obtain a temperature difference value corresponding to each monitoring point, and if the temperature difference value corresponding to each monitoring point is greater than a preset temperature difference running value or the temperature of the monitoring point is greater than an alarm upper limit value, the monitoring point is judged to be a hot spot.
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