CN117835645A - Intelligent linkage control method and related device for data center air conditioner - Google Patents
Intelligent linkage control method and related device for data center air conditioner Download PDFInfo
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Abstract
The invention provides an intelligent linkage control method and a related device of a data center air conditioner, wherein the method comprises the following steps: acquiring the control quantity of each air conditioner in the air conditioning system of the data center at the current moment and the running state parameter target value of the target air conditioner in the future prediction period; predicting the running state parameters of the target air conditioner in a future prediction period according to the control quantity of each air conditioner at the current moment and the running state parameter target value of the target air conditioner in the future prediction period; optimizing the control quantity of the target air conditioner in a future prediction period according to the error of the operation state parameter predicted value and the corresponding operation state parameter target value to obtain the control quantity of the future prediction period; and controlling the target air conditioner by adopting the control quantity of the future prediction period. The invention can reversely optimize the control quantity in advance based on the running state parameter predicted value, thereby adopting the optimized control quantity to control the target air conditioner so as to reduce the overshoot and improve the control stability of the target air conditioner system.
Description
Technical Field
The invention relates to the technical field of data centers, in particular to an intelligent linkage control method and a related device of an air conditioner of a data center.
Background
The rapid development of existing communication network technology has led to a rapid increase in both the size and power density of data centers. A large number of servers and other electronic equipment within a data center air conditioning system generate a large amount of heat in an operating state. Therefore, in order to ensure the safety and high-efficiency performance of the equipment, the performance and energy consumption requirements of the target air conditioner are also higher and higher.
The conventional target air conditioner generally adopts PID control, and the control method often has the problems of overshoot and control lag, so that the control stability is poor, and the energy-saving effect is poor.
Disclosure of Invention
In view of the above, the invention provides an intelligent linkage control method and a related device for a data center air conditioner, which can solve the problem of poor control stability of a target air conditioner.
In a first aspect, an embodiment of the present invention provides an intelligent coordinated control method of an air conditioner in a data center, including:
acquiring the control quantity of each air conditioner in the air conditioning system of the data center at the current moment and the running state parameter target value corresponding to the target air conditioner in the future prediction period; the target air conditioner is any air conditioner in the data center air conditioning system;
predicting the running state parameters of the target air conditioner in the future prediction period according to the control quantity of each air conditioner at the current moment and the running state parameter target value of the target air conditioner in the future prediction period to obtain the running state parameter prediction value of the future prediction period;
optimizing the control quantity of the target air conditioner in the future prediction period according to the running state parameter prediction value of the target air conditioner in the future prediction period and the error of the corresponding running state parameter target value to obtain the control quantity of the target air conditioner in the future prediction period;
and controlling the target air conditioner by adopting the control quantity of the future prediction period.
In a second aspect, an embodiment of the present invention provides an intelligent coordinated control device for an air conditioner in a data center, including:
the data acquisition module is used for acquiring the control quantity of each air conditioner in the air conditioning system of the data center at the current moment and the running state parameter target value corresponding to the target air conditioner in the future prediction period; the target air conditioner is any air conditioner in the data center air conditioning system;
the prediction module is used for predicting the running state parameters of the target air conditioner in the future prediction period according to the control quantity of each air conditioner at the current moment and the running state parameter target value of the target air conditioner in the future prediction period to obtain the running state parameter prediction value of the future prediction period;
the control quantity optimizing module is used for optimizing the control quantity of the target air conditioner in the future prediction period according to the running state parameter prediction value of the target air conditioner in the future prediction period and the error of the corresponding running state parameter target value to obtain the control quantity of the target air conditioner in the future prediction period;
and the air conditioner control module is used for controlling the target air conditioner by adopting the control quantity of the future prediction period.
In a third aspect, an embodiment of the present invention provides a monitoring host, including a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method according to any one of the possible implementations of the first aspect above when the computer program is executed.
In a fourth aspect, embodiments of the present invention provide a computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the method as described in any one of the possible implementations of the first aspect above.
Compared with the prior art, the embodiment of the invention has the beneficial effects that:
the embodiment of the invention obtains the control quantity of each air conditioner in the air conditioning system of the data center at the current moment and the running state parameter target value corresponding to the target air conditioner in the future prediction period; the running state parameters of the next moment can be predicted according to the control quantity of each air conditioner at the current moment and the running state parameter target value of the next moment to obtain running state parameter predicted values; optimizing the control quantity at the next moment according to the running state parameter predicted value at the next moment and the error of the running state parameter target value to obtain the control quantity at the next moment; and finally, controlling the target air conditioner by adopting the control quantity at the next moment. According to the method and the device for predicting the running state parameters of the target air conditioner in the future prediction period based on the control quantity of all air conditioners of the data center air conditioner system, intelligent linkage control of the data center air conditioner is achieved, prediction accuracy of the running state parameters is improved, the control quantity of the next moment is reversely optimized in advance based on the running state parameter prediction value, accordingly, the optimized control quantity is adopted to control the target air conditioner in a progressive mode, the hysteresis control problem is avoided, overshoot is reduced, control stability of the target air conditioner system is improved, and energy-saving effect of the air conditioner is improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an implementation of an intelligent coordinated control method of a data center air conditioner provided by an embodiment of the invention;
FIG. 2 is a specific flow chart of an intelligent coordinated control method of a data center air conditioner provided by an embodiment of the invention;
FIG. 3 is a schematic structural diagram of an intelligent coordinated control device of a data center air conditioner according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a monitoring host according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the following description will be made by way of specific embodiments with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of an implementation of an intelligent coordinated control method of a data center air conditioner according to an embodiment of the present invention is shown, and details are as follows:
s101: acquiring the control quantity of each air conditioner in the air conditioning system of the data center at the current moment and the running state parameter target value corresponding to the target air conditioner in the future prediction period; the target air conditioner is any air conditioner in the data center air conditioning system.
The execution subject of the method provided in this embodiment may be a monitoring host of an air conditioning system of a data center.
In this embodiment, the operation state parameter of the target air conditioner is a parameter for indicating the output condition of the target air conditioner, and specifically may include an actual return air temperature value, an actual outlet air temperature value, an actual return air humidity value, a refrigerating capacity, a wind speed, and the like of the target air conditioner. The control quantity is the regulation quantity of the internal devices of the air conditioner; the method can specifically comprise compressor frequency, fan rotating speed, electric heating on-off state, return air temperature target value, outlet air temperature target value and the like.
S102: and predicting the running state parameters of the target air conditioner in the future prediction period according to the control quantity of each air conditioner at the current moment and the running state parameter target value of the target air conditioner in the future prediction period to obtain the running state parameter predicted value of the future prediction period.
In this embodiment, each air conditioner in the data center air conditioning system uploads the running state data to the monitoring host through the COM port, the monitoring host periodically outputs the control quantity of each air conditioner through the control logic, and then issues the control command back to each air conditioner, so as to complete the energy-saving linkage control.
Specifically, since all the air conditioners of the air conditioning system of the data center are located in the same space, the control quantity of other air conditioners except the target air conditioner also affects the running state parameter of the target air conditioner at the next moment, and the running state parameter of the target air conditioner at the next moment is predicted in advance according to the control quantity of each air conditioner at the current moment and the running state parameter target value of the target air conditioner at the next moment based on the principle, so that the prediction accuracy of the running state parameter can be improved.
S103: and optimizing the control quantity of the target air conditioner in the future prediction period according to the running state parameter prediction value of the target air conditioner in the future prediction period and the error of the corresponding running state parameter target value to obtain the control quantity of the target air conditioner in the future prediction period.
S104: and controlling the target air conditioner by adopting the control quantity of the future prediction period.
In this embodiment, the optimized control amount can enable the running state parameter at the next moment to reach the target value of the running state parameter in a progressive manner, so as to reduce overshoot, improve the control stability of the target air conditioning system, reduce the air conditioning energy consumption, and improve the energy saving effect.
In one possible implementation manner, fig. 2 shows a specific flow block diagram of the target air conditioner provided in this embodiment, and referring to fig. 2, the specific implementation flow of S102 includes:
and inputting the control quantity of each air conditioner at the current moment and the running state parameter target value of the target air conditioner in the future prediction period into a running parameter prediction model, and outputting the running state parameter predicted value of the target air conditioner in the future prediction period.
In this embodiment, for each air conditioner model, a single body capability test is performed on a target air conditioner of the model, and control amounts and operation state parameters of each air conditioner in the data center air conditioning system under each working condition and each cabinet load of the model are recorded. And then taking the control quantity of each air conditioner in the data center air conditioning system at a certain moment and the running state parameter target value of the air conditioner corresponding to the machine type in a preset period as inputs, and taking the actual value of the running state parameter corresponding to the air conditioner of the machine type in the preset period as output to train the mathematical model, so as to obtain the running parameter prediction model.
Wherein the future prediction period may comprise at least one time instant.
In this embodiment, the present application may train the above model using the amount of interference affecting the operation state parameter as another input, thereby improving the prediction accuracy of the operation parameter prediction model.
Exemplary amounts of interference include rack capacity of the data center, ambient temperature, ambient humidity, and the like.
In one possible implementation, referring to fig. 2, the specific implementation procedure of S103 includes:
calculating the error between the predicted value of the running state parameter of each predicted time and the target value of the running state parameter of the corresponding predicted time in the future predicted period;
inputting errors corresponding to each prediction period in the future prediction period into an optimal controller, and solving a cost function of the optimal controller to obtain the control quantity of the target air conditioner in the future prediction period;
the optimal controller is constructed based on an optimal control algorithm.
Specifically, the optimal control algorithm is used for searching for an optimal control strategy under the condition that a certain constraint condition is met, so that the performance index takes the maximum value or the minimum value. The performance index of the embodiment is the difference between the predicted value of the running state parameter and the target value of the running state parameter, and the control quantity corresponding to the minimum performance index is determined as the control quantity of the future predicted period.
In one possible embodiment, when optimizing only the control quantity at the next moment, the cost function that can be used is:
minJ＝qe 2 +ru 2 ；
where q represents a first adjustment parameter, r represents a second adjustment parameter, e represents an error between a predicted value of an operation state parameter and a target value of the operation state parameter, and u represents a control amount.
Specifically, when the control amount of the future prediction period is optimized, the cost function adopted is:
wherein E is k An error matrix representing the time k;transpose of error matrix representing time k, U k A control amount matrix representing the k time; />A transpose of the control quantity matrix at time k; q represents a first adjustment parameter matrix and R represents a second adjustment parameter matrix.
Specifically, the embodiment adopts a closed-loop optimization control strategy, when predicting the running state parameters, running state parameter predicted values of N times in the future can be predicted, then errors of the running state parameter predicted values and running state parameter target values of the N times in the future are substituted into a cost function, the accumulated errors of the N times in the future are minimum as an optimization target, the control quantity of the N times in the future is obtained, the actual running state parameter values of the N times in the future are gradually close to the running state parameter target value, then the control quantity of the first time (the next time) in the future is selected from the control quantity of the N times in the future to control the target air conditioner, and in the process of optimizing the control quantity of the next time, the control quantity after the optimization of the time is used as the control quantity of the current time to optimize the control quantity of the next time. Through the rolling optimization process, the intelligent linkage control robustness of the data center air conditioner can be improved, system fluctuation is reduced, and operation safety of a data center server and a load is further improved.
Specifically, an error matrix E k The control quantity matrix U comprises errors of each operation state parameter predicted value corresponding to k time and operation state parameter target value k Including the respective control amounts corresponding to the k time. The first adjusting parameter matrix comprises adjusting parameters corresponding to each error, and the second adjusting parameter matrix comprises adjusting parameters corresponding to each control quantity.
For example, when the number of the operation state parameters is two and the number of the control amounts is two, the cost function may specifically be:
wherein,respectively representing errors of two predicted values of the running state parameters and target values of the running state parameters at the moment k, and q1 and q2 respectively representing adjusting parameters corresponding to different errors, < >>Respectively representing two control amounts at the time of k, and r1 and r2 respectively represent adjustment parameters corresponding to different control amounts.
Specifically, the cost function may also be:
on the basis of taking the minimum error accumulated in multiple steps as an optimization target, the cost function optimizes the target with the minimum error in the last step as a key point, so that the actual value of the running state parameter of the target air conditioner gradually approaches to the target value of the running state parameter, the overshoot is reduced, and the control robustness is improved.
In one possible implementation, referring to fig. 2, after S103, the method provided in this embodiment further includes:
judging whether the control quantity of the target air conditioner at the next moment is in a first threshold range or not; the first threshold range includes a maximum threshold value and a minimum threshold value;
if the control quantity of the target air conditioner at the next moment is not in the first threshold range, a first critical value is adopted as the control quantity of the target air conditioner at the next moment to control the target air conditioner;
the first critical value is the critical value closest to the control quantity of the target air conditioner at the next moment.
Specifically, if the control amount of the target air conditioner at the next moment is greater than the maximum critical value, the maximum critical value is used as the control amount at the next moment to control the target air conditioner; and if the control quantity of the target air conditioner at the next moment is smaller than the minimum critical value, controlling the target air conditioner by taking the minimum critical value as the control quantity at the next moment. And if the control quantity of the target air conditioner at the next moment is in the first threshold range, controlling the target air conditioner by adopting the control quantity of the target air conditioner at the next moment.
For example, when the control amount is the outlet air temperature, the first threshold value may be in the range of 18 to 27 ℃. And if the control quantity of the target air conditioner at the next moment is 17 ℃, controlling the target air conditioner by adopting 18 ℃ as the control quantity at the next moment. And if the control quantity of the target air conditioner at the next moment is 29 ℃, controlling the target air conditioner by adopting 28 ℃ as the control quantity at the next moment. So as to avoid the problem of poor temperature control effect caused by too low or too high control amount.
In one possible embodiment, the control amount includes at least one; after S103, the method provided in this embodiment further includes:
after optimizing the control quantity of the target air conditioner in the future prediction period to obtain the control quantity of the future prediction period, the method further comprises:
judging whether the control quantity of the target air conditioner at the next moment is in a corresponding first preset range or not;
if the control quantity of the target air conditioner at the next moment is not located in the corresponding first preset range, the control quantity of the current moment is adopted to control the air conditioner, the control quantity of the current moment is returned to the operation state parameter target value according to the control quantity of each air conditioner at the current moment and the operation state parameter target value of the target air conditioner at the future prediction period, the operation state parameter of the target air conditioner at the future prediction period is predicted, and the operation state parameter predicted value of the future prediction period is obtained to be continuously executed;
and if the control quantity at the next moment meets the corresponding constraint condition, controlling the target air conditioner by adopting the control quantity at the next moment.
In this embodiment, each control amount corresponds to a constraint condition, for example, a compressor frequency corresponds to a frequency adjustment range, a fan rotation speed corresponds to a rotation speed adjustment range, and the like. If the control quantity of the next moment output by the optimal controller meets the corresponding constraint condition, the target air conditioner is controlled by adopting the control quantity, if the control quantity does not meet the constraint condition, the control quantity is taken as the control quantity of the current moment, and the control quantity of the next moment is optimized by adopting the algorithm again until the control quantity of the next moment meets the corresponding constraint condition.
In one possible embodiment, the operating state parameters include outlet air temperature, and the control amounts include outlet air temperature target value, fan speed, and compressor operating frequency, and the disturbance amounts include cabinet load amount and ambient temperature, respectively.
In one possible embodiment, the operating state parameters include the outlet air humidity, and accordingly, the control amount may include the compressor operating frequency and the electric heating on-off state, and the disturbance amount may include the ambient humidity and the cabinet load amount.
According to the method, the control quantity of the next moment can be reversely optimized through the predicted running state parameter of the next moment, so that the optimized control quantity is adopted to control the target air conditioner, the overshoot of the system is reduced, the running energy consumption of the compressor is reduced, the control hysteresis problem is solved, the stability of the target air conditioner system is improved, and the energy-saving effect is improved.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.
The following are device embodiments of the invention, for details not described in detail therein, reference may be made to the corresponding method embodiments described above.
Fig. 3 is a schematic structural diagram of an intelligent coordinated control device of a data center air conditioner according to an embodiment of the present invention, and for convenience of explanation, only a portion relevant to the embodiment of the present invention is shown, which is described in detail below:
as shown in fig. 3, the intelligent coordinated control device 100 of the data center air conditioner includes:
a data acquisition module 110, configured to acquire a control amount of each air conditioner in the data center air conditioning system at a current moment and an operation state parameter target value corresponding to a future prediction period of a target air conditioner; the target air conditioner is any air conditioner in the data center air conditioning system;
the prediction module 120 is configured to predict an operation state parameter of the target air conditioner in the future prediction period according to a control amount of each air conditioner at a current time and an operation state parameter target value of the target air conditioner in the future prediction period, so as to obtain an operation state parameter predicted value of the future prediction period;
the control amount optimizing module 130 is configured to optimize the control amount of the target air conditioner in the future prediction period according to the running state parameter prediction value of the target air conditioner in the future prediction period and the error of the corresponding running state parameter target value, so as to obtain the control amount of the target air conditioner in the future prediction period;
and a target air conditioner control module 140 for controlling the target air conditioner by using the control amount of the future prediction period.
In one possible implementation, the prediction module 120 includes:
and inputting the control quantity of each air conditioner at the current moment and the running state parameter target value of the target air conditioner in the future prediction period into a running parameter prediction model, and outputting the running state parameter predicted value of the target air conditioner in the future prediction period.
In one possible implementation, the prediction module 120 further includes:
and inputting the control quantity of each air conditioner at the current moment, the running state parameter target value of the target air conditioner in the future prediction period and the interference quantity influencing the running state parameter control into a running parameter prediction model, and outputting the running state parameter predicted value of the target air conditioner in the future prediction period.
In one possible embodiment, the control amount tuning module 130 includes:
calculating the error between the predicted value of the running state parameter of each predicted time and the target value of the running state parameter of the corresponding predicted time in the future predicted period;
inputting errors corresponding to each prediction period in the future prediction period into an optimal controller, and solving a cost function of the optimal controller to obtain the control quantity of the target air conditioner in the future prediction period;
the optimal controller is constructed based on an optimal control algorithm.
In one possible implementation, the cost function is:
wherein E is k An error matrix representing the time k;transpose of error matrix representing time k, U k A control amount matrix representing the k time; />A transpose of the control quantity matrix at time k; q represents a first adjustment parameter matrix and R represents a second adjustment parameter matrix.
In one possible embodiment, the intelligent coordinated control apparatus 100 of a data center air conditioner further includes a model training module for:
constructing a machine learning model;
acquiring control quantity of each air conditioner in a data center air conditioning system, and an operation state parameter target value and an operation state parameter actual value of each air conditioner in a period of time;
taking the control quantity of each air conditioner in the data center air conditioning system at a first moment and the running state parameter target value of a single air conditioner in a preset period as inputs, and taking the corresponding running state parameter actual value of the single air conditioner in the preset period as output to generate a training sample; the first time is any time in the running of the air conditioner, and the preset period is a period after the first time;
and training the machine learning model by adopting a plurality of training samples to obtain the operation parameter prediction model.
In one possible embodiment, the future prediction period comprises a next time instant; the intelligent coordinated control device 100 of the data center air conditioner further includes a control amount constraint module for:
judging whether the control quantity of the target air conditioner at the next moment is in a first threshold range or not; the first threshold range includes a maximum threshold value and a minimum threshold value;
if the control quantity of the target air conditioner at the next moment is not in the first threshold range, a first critical value is adopted as the control quantity of the target air conditioner at the next moment to control the target air conditioner;
the first critical value is the critical value closest to the control quantity of the target air conditioner at the next moment.
In one possible embodiment, the operating state parameters include an actual outlet air temperature value, and the control amounts include an outlet air temperature target value, a fan speed, and a compressor operating frequency, and the disturbance amounts include a cabinet load amount and an ambient temperature, respectively.
The device can predict the running state parameters of the target air conditioner in the future prediction period based on the control quantity of all air conditioners of the data center air conditioning system, realize intelligent linkage control of the data center air conditioner, improve the prediction accuracy of the running state parameters, and reversely optimize the control quantity at the next moment in advance based on the running state parameter prediction value, thereby controlling the target air conditioner in a progressive mode by adopting the optimized control quantity, avoiding the hysteresis control problem, reducing the overshoot, improving the control stability of the target air conditioning system and improving the energy saving effect of the air conditioner. .
The intelligent linkage control device of the data center air conditioner provided by the embodiment can be used for executing the intelligent linkage control method embodiment of the data center air conditioner, and the implementation principle and the technical effect are similar, and the embodiment is not repeated here.
Fig. 4 is a schematic diagram of a monitoring host according to an embodiment of the invention. As shown in fig. 4, the monitoring host 4 of this embodiment includes: a processor 40, a memory 41 and a computer program 42 stored in the memory 41 and executable on the processor 40. The steps of the intelligent coordinated control method embodiment of each data center air conditioner described above, such as steps 101 to 104 shown in fig. 1, are implemented when the processor 40 executes the computer program 42. Alternatively, the processor 40, when executing the computer program 42, performs the functions of the modules/units of the apparatus embodiments described above, such as the functions of the modules 110-140 shown in fig. 3.
Illustratively, the computer program 42 may be partitioned into one or more modules/units that are stored in the memory 41 and executed by the processor 40 to complete the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing a specific function describing the execution of the computer program 42 in the monitoring host 4.
The monitoring host 4 may be a computing device such as a desktop computer, a notebook computer, a palm computer, a cloud server, etc. The monitoring host 4 may include, but is not limited to, a processor 40, a memory 41. It will be appreciated by those skilled in the art that fig. 4 is merely an example of a monitoring host 4 and is not meant to be limiting as the monitoring host 4 may include more or fewer components than shown, or may combine certain components, or different components, e.g., the monitoring host may further include input-output devices, network access devices, buses, etc.
The processor 40 may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field-programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 41 may be an internal storage unit of the monitoring host 4, for example, a hard disk or a memory of the monitoring host 4. The memory 41 may be an external storage device of the monitoring host 4, for example, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the monitoring host 4. Further, the memory 41 may also include both an internal storage unit and an external storage device of the monitoring host 4. The memory 41 is used for storing the computer program and other programs and data required by the monitoring host. The memory 41 may also be used for temporarily storing data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/monitoring host and method may be implemented in other manners. For example, the above-described apparatus/monitoring host embodiments are merely illustrative, e.g., the division of the modules or units is merely a logical functional division, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present invention may implement all or part of the procedures in the methods of the foregoing embodiments, or may be implemented by instructing related hardware by a computer program, where the computer program may be stored in a computer readable storage medium, and the computer program may implement the steps of the embodiments of the intelligent coordinated control method of each data center air conditioner when executed by a processor. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium may include content that is subject to appropriate increases and decreases as required by jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is not included as electrical carrier signals and telecommunication signals.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention.
Claims (10)
1. An intelligent linkage control method of a data center air conditioner is characterized by comprising the following steps:
acquiring the control quantity of each air conditioner in the air conditioning system of the data center at the current moment and the running state parameter target value corresponding to the target air conditioner in the future prediction period; the target air conditioner is any air conditioner in the data center air conditioning system;
predicting the running state parameters of the target air conditioner in the future prediction period according to the control quantity of each air conditioner at the current moment and the running state parameter target value of the target air conditioner in the future prediction period to obtain the running state parameter prediction value of the future prediction period;
optimizing the control quantity of the target air conditioner in the future prediction period according to the running state parameter prediction value of the target air conditioner in the future prediction period and the error of the corresponding running state parameter target value to obtain the control quantity of the target air conditioner in the future prediction period;
and controlling the target air conditioner by adopting the control quantity of the future prediction period.
2. The intelligent coordinated control method of a data center air conditioner according to claim 1, wherein the predicting the operation state parameter of the target air conditioner in the future prediction period according to the control amount of each air conditioner at the current time and the operation state parameter target value of the target air conditioner in the future prediction period to obtain the operation state parameter prediction value of the future prediction period comprises:
and inputting the control quantity of each air conditioner at the current moment and the running state parameter target value of the target air conditioner in the future prediction period into a running parameter prediction model, and outputting the running state parameter predicted value of the target air conditioner in the future prediction period.
3. The intelligent coordinated control method of a data center air conditioner according to claim 1, wherein the predicting the operation state parameter of the target air conditioner in the future prediction period according to the control amount of each air conditioner at the current time and the operation state parameter target value of the target air conditioner in the future prediction period to obtain the operation state parameter prediction value of the future prediction period comprises:
and inputting the control quantity of each air conditioner at the current moment, the running state parameter target value of the target air conditioner in the future prediction period and the interference quantity influencing the running state parameter control into a running parameter prediction model, and outputting the running state parameter predicted value of the target air conditioner in the future prediction period.
4. The intelligent coordinated control method of a data center air conditioner according to claim 1, wherein optimizing the control amount of the target air conditioner in the future prediction period according to the error of the operation state parameter prediction value of the target air conditioner in the future prediction period and the corresponding operation state parameter target value to obtain the control amount of the target air conditioner in the future prediction period comprises:
calculating the error between the predicted value of the running state parameter of each predicted time and the target value of the running state parameter of the corresponding predicted time in the future predicted period;
inputting errors corresponding to each prediction period in the future prediction period into an optimal controller, and solving a cost function of the optimal controller to obtain the control quantity of the target air conditioner in the future prediction period;
the optimal controller is constructed based on an optimal control algorithm.
5. The intelligent coordinated control method of a data center air conditioner according to claim 2, further comprising:
constructing a machine learning model;
acquiring control quantity of each air conditioner in a data center air conditioning system, and an operation state parameter target value and an operation state parameter actual value of each air conditioner in a period of time;
taking the control quantity of each air conditioner in the data center air conditioning system at a first moment and the running state parameter target value of a single air conditioner in a preset period as inputs, and taking the corresponding running state parameter actual value of the single air conditioner in the preset period as output to generate a training sample; the first time is any time in the running of the air conditioner, and the preset period is a period after the first time;
and training the machine learning model by adopting a plurality of training samples to obtain the operation parameter prediction model.
6. The intelligent coordinated control method of a data center air conditioner according to claim 1, wherein the future prediction period includes a next time; after optimizing the control quantity of the target air conditioner in the future prediction period to obtain the control quantity of the future prediction period, the method further comprises:
judging whether the control quantity of the target air conditioner at the next moment is in a first threshold range or not; the first threshold range includes a maximum threshold value and a minimum threshold value;
if the control quantity of the target air conditioner at the next moment is not in the first threshold range, a first critical value is adopted as the control quantity of the target air conditioner at the next moment to control the target air conditioner;
the first critical value is the critical value closest to the control quantity of the target air conditioner at the next moment.
7. The intelligent coordinated control method of a data center air conditioner according to claim 3, wherein the operation state parameters include an outlet air temperature actual value, and correspondingly, the control quantity includes an outlet air temperature target value, a fan rotation speed and a compressor operation frequency, and the disturbance quantity includes a cabinet load quantity and an ambient temperature.
8. An intelligent coordinated control device of a data center air conditioner, which is characterized by comprising:
the data acquisition module is used for acquiring the control quantity of each air conditioner in the air conditioning system of the data center at the current moment and the running state parameter target value corresponding to the target air conditioner in the future prediction period; the target air conditioner is any air conditioner in the data center air conditioning system;
the prediction module is used for predicting the running state parameters of the target air conditioner in the future prediction period according to the control quantity of each air conditioner at the current moment and the running state parameter target value of the target air conditioner in the future prediction period to obtain the running state parameter prediction value of the future prediction period;
the control quantity optimizing module is used for optimizing the control quantity of the target air conditioner in the future prediction period according to the running state parameter prediction value of the target air conditioner in the future prediction period and the error of the corresponding running state parameter target value to obtain the control quantity of the target air conditioner in the future prediction period;
and the air conditioner control module is used for controlling the target air conditioner by adopting the control quantity of the future prediction period.
9. A monitoring host for communication connection with respective air conditioners of a data center air conditioning system, performing the method of any one of claims 1 to 7.
10. A data center air conditioning system comprising at least one air conditioner and the monitoring host of claim 9.
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