WO2022111232A1 - Procédé d'optimisation de modèle de commande de système de refroidissement d'eau, dispositif électronique et support de stockage - Google Patents

Procédé d'optimisation de modèle de commande de système de refroidissement d'eau, dispositif électronique et support de stockage Download PDF

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WO2022111232A1
WO2022111232A1 PCT/CN2021/127980 CN2021127980W WO2022111232A1 WO 2022111232 A1 WO2022111232 A1 WO 2022111232A1 CN 2021127980 W CN2021127980 W CN 2021127980W WO 2022111232 A1 WO2022111232 A1 WO 2022111232A1
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control
parameters
parameter
model
evaluation value
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Chinese (zh)
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弄庆鹏
周祥生
屠要峰
李忠良
王壮
高洪
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中兴通讯股份有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
    • F24F11/46—Improving electric energy efficiency or saving
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods

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  • the embodiments of the present application relate to the technical field of data processing, and in particular, to a method for optimizing a control model of a water cooling system, an electronic device, and a storage medium.
  • the power consumption of data centers continues to increase with the expansion of the scale.
  • the water cooling system accounts for about half of the non-Internet technology (IT) energy consumption of data centers. Therefore, effectively reducing the energy consumption of the water cooling system is one of the keys to reducing non-IT energy consumption in data centers.
  • the data center will optimize the control strategy model of the water cooling system by directly interacting with the physical environment of the water cooling system or by interacting with the simulated environment to obtain the control strategy and its control effect data, so as to send control commands to the water cooling system that can reduce energy consumption.
  • the model when the model is used to directly interact with the physical environment of the water cooling system, the model is generally not optimized, and the control strategy generated by an unoptimized model is likely to affect the normal operation of the data center; on the other hand, the simulation environment is used to interact
  • the simulation environment When the control strategy and its control effect data are obtained to optimize the control strategy model of the water cooling system, the simulation environment usually deviates from the real data center environment and the real environment of the water cooling system, and the real environment parameters cannot be fitted.
  • An embodiment of the present application provides a method for optimizing a control model of a water cooling system, and the method includes the following steps: acquiring parameters of a data center; creating a state transition model and a control model; optimizing the state transition model according to the parameters of the data center; The optimized state transition model and the parameters of the data center are optimized for the control model, and the optimized control model is obtained.
  • An embodiment of the present application further provides an electronic device, the device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores data that can be processed by the at least one processor.
  • the instructions are executed by the processor, and the instructions are executed by the at least one processor, so that the at least one processor can execute the above-mentioned control model optimization method for a water cooling system.
  • An embodiment of the present application further provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the above-mentioned control model optimization method for a water cooling system is implemented.
  • FIG. 1 is a flowchart of a control model optimization method for a water cooling system provided by a first embodiment of the present application
  • FIG. 2 is a flowchart of step 103 in the control model optimization method of the water cooling system provided by the first embodiment of the present application shown in FIG. 1;
  • FIG. 3 is a flowchart of a control model optimization method for a water cooling system provided by a second embodiment of the present application.
  • FIG. 4 is a flowchart of step 305 in the control model optimization method of the water cooling system provided by the second embodiment of the present application shown in FIG. 3;
  • FIG. 5 is a flowchart of a control model optimization method for a water cooling system provided by a third embodiment of the present application.
  • FIG. 6 is a schematic structural diagram of an electronic device provided by a fourth embodiment of the present application.
  • the main purpose of the embodiments of the present application is to propose a control model optimization method, electronic device and storage medium for a water cooling system, aiming to provide an optimized control model for the water cooling system of a data center through a real interactive environment interaction process, so that It can fit the real environment and effectively reduce the energy consumption of the water cooling system without affecting the normal operation of the data center.
  • the first embodiment of the present application relates to a control model optimization method of a water cooling system, as shown in FIG. 1 , which specifically includes:
  • step 101 parameters of the data center are acquired.
  • the data center includes the data center water cooling system, the data center computer room and the data center environment.
  • the data center water cooling system can provide control parameters and output parameters in the data
  • the data center computer room can provide the data center operating parameters
  • the data center environment can provide the data center environment parameters.
  • control parameters also include the operating number of cooling towers, the operating number of cooling pumps, the operating number of refrigeration pumps, the operating number of plate replacements, the temperature of plate replacement water, the water temperature of cooling towers, the operating frequency of cooling tower fans, the operating frequency of cooling pumps, Parameters such as the operating frequency of the refrigeration pump, the setting value of the pressure difference of the refrigeration main pipe;
  • the output parameters also include parameters such as the energy consumption of the water cooling system, the air temperature at the end of the water cooling system, the return air temperature at the end of the water cooling system, and the cooling capacity of the water cooling system;
  • the operating parameters also include the IT energy parameters such as power consumption parameters and data center temperature settings; environmental parameters also include parameters such as outdoor dry bulb temperature, outdoor wet bulb temperature, and outdoor humidity.
  • parameters of the data center may also include other parameters in the actual use process, which will not be repeated here.
  • Step 102 creating a state transition model and a control model.
  • the state transition model is a model describing the state change relationship of the data center, which can obtain the current output parameters of the data center according to the input environmental parameters of the data center, the operating parameters of the data center and the control parameters of the data center, and predict The environmental parameters of the data center and the operation parameters of the data center in the next state after the output parameter is acted on are obtained.
  • the control model is the control model of the water cooling system. Inputting the environmental parameters of the data center and the operating parameters of the data center can obtain the control strategy of the data center, wherein the control strategy is expressed in the form of control parameters.
  • Step 103 optimize the state transition model according to the parameters of the data center.
  • the environmental parameters, operating parameters, control parameters, etc. of the parameters that can have an effect on the control effect in the data center are used as the input data of the model, and the output parameters that can reflect the control effect are used as the output of the model corresponding to the input data. data, and train the model to achieve model optimization.
  • step 103 includes:
  • Step 201 Input the environmental parameters, operating parameters and control parameters into the state transition model to obtain the predicted output parameters.
  • Step 202 Calculate the state transition loss according to the predicted output parameter and the output parameter.
  • the loss function may be any loss function that can reflect the deviation between the predicted output parameter and the actual output parameter.
  • Step 203 optimize the state transition model according to the state transition loss.
  • the state transition loss obtained in step 202 is used as training data to train the state transition model, so as to realize the optimization of the model.
  • Step 104 optimize the control model according to the optimized state transition model and parameters of the data center, and obtain the optimized control model.
  • the training data is directly or indirectly obtained from the parameters of the state transition model, the control model and the data center, and then the control model is trained according to the obtained training data to realize the optimization of the control model.
  • the control model optimization method for a water cooling system provides training data for the created state transition model and control model by acquiring the parameters of the data center, and then trains and optimizes the state transition model according to the parameters of the data center to provide the optimal control model.
  • the real, offline environment is used for interactive training, and then the control model is optimized according to the optimized state transition model and the parameters of the data center to realize the optimization of the control model of the water cooling system.
  • the present application provides a control model for the water cooling system of the data center, which can effectively reduce the energy consumption of the water cooling system.
  • the present application can also optimize the control model instead of using the initial control model, which is safer and more reliable. Provide an optimized control model for the water cooling system of the data center through the interaction process of the real interactive environment, so that it can fit the real environment and effectively reduce the energy consumption of the water cooling system without affecting the normal operation of the data center.
  • the second embodiment of the present application relates to a control model optimization method for a water cooling system.
  • This embodiment is roughly the same as the first embodiment, except that step 104 uses reinforcement learning to optimize the control model.
  • the specific process is shown in Figure 3 shown:
  • step 301 parameters of the data center are acquired.
  • step 301 in this embodiment is substantially the same as step 101 in the first embodiment, and details are not repeated here.
  • Step 302 creating a state transition model and a control model.
  • step 302 in this embodiment is substantially the same as step 102 in the first embodiment, and details are not repeated here.
  • Step 303 Optimize the state transition model according to the parameters of the data center.
  • step 303 in this embodiment is substantially the same as step 103 in the first embodiment, and details are not repeated here.
  • Step 304 randomly select a set of sample data from the parameters of the data center.
  • various types of parameters in a certain state of the data center are randomly selected, and only data in the same state can constitute a set of sample data.
  • Step 305 using the control model and the state transition model to process the sample data to obtain training samples.
  • step 305 includes:
  • Step 401 generating state representation data according to the environmental parameters and the operating parameters.
  • the data is operated in the form of a matrix, which can effectively improve the running rate and the execution efficiency of the steps. Therefore, the data can be vectorized to further obtain the state representation vector of the data center.
  • step 402 the state representation data is input into the control model to obtain predictive control parameters.
  • the state representation vector is input into the control model, and the control model outputs a vector of control parameters predicted by the model.
  • Step 403 Input the predicted control parameters, the environmental parameters and the operating parameters into the state transition model, obtain the predicted output parameters, and update the environmental parameters and the operating parameters according to the predicted output parameters.
  • the predicted output parameters output by the model can be obtained, and the environmental parameters and operating parameters in the next state can also be obtained.
  • the environment parameters and running parameters are updated to the set environment parameters and running parameters for the next iteration cycle.
  • Step 404 Update the state representation data according to the updated environment parameters and operating parameters.
  • Step 405 Evaluate the predictive control parameters according to the predictive control parameters, the predictive output parameters and the control parameters, and obtain a control reward.
  • control action evaluation value of the control strategy first obtain the control action evaluation value of the control strategy according to the predicted control parameters and the control parameters, and then obtain the control effect evaluation value according to the predicted output parameter, wherein the control effect evaluation value includes the energy consumption evaluation value and the cooling capacity evaluation value, Finally, comprehensively analyze the control action evaluation value, energy consumption evaluation value and cooling capacity evaluation value to obtain control rewards.
  • the current predicted control parameters and the control parameters of the data center it is substituted into the preset function to obtain the control action evaluation value; according to the first predicted output parameters, the initial energy consumption parameter and the initial cooling capacity parameter are obtained, Obtain the current predicted energy consumption parameters and the current predicted cooling capacity parameters according to the current predicted output parameters, obtain the historical predicted energy consumption parameters according to the last predicted output parameters, and obtain the energy consumption according to the current predicted energy consumption parameters and the historical predicted energy consumption parameters Obtain the deviation degree of energy consumption according to the current predicted energy consumption parameters and initial energy consumption parameters, average the growth rate and deviation to obtain the energy consumption evaluation value, and analyze according to the initial cooling capacity parameters and the current predicted cooling capacity parameters, Obtain the cooling capacity evaluation value; perform the weighted average of the obtained control action evaluation value and the cooling capacity evaluation value to obtain the constraint evaluation value, and perform the weighted average of the energy consumption evaluation value and the constraint evaluation value to obtain the control reward.
  • the function used in this embodiment is not limited, and may be any function that can obtain an intuitive and accurate evaluation result according to the above data.
  • Step 406 Generate a training sample according to the state representation data before the update, the predicted control parameters, the control reward and the updated state representation data.
  • a training sample is a quadruple, and this quadruple consists of the state representation data before the update obtained in step 401 , the predicted control parameters obtained in step 402 , the control reward obtained in step 405 , and the update obtained in step 44
  • the post state characterizes the data composition.
  • Step 407 detecting whether the number of training samples reaches a second threshold.
  • step 408 if yes, go to step 408, if not, go to step 402.
  • Step 408 taking all the training samples as a group of training samples.
  • Step 306 optimize the control model according to the training samples.
  • a set of training samples obtained in step 408 is used as a training set to train the control model to complete an optimization.
  • Step 307 Detect whether the optimization times of the control model reach a first threshold.
  • step 308 if yes, go to step 308, if not, go to step 304.
  • first threshold and the second threshold are only to distinguish between the threshold of the number of optimizations and the threshold of the number of training samples. There is no connection between the first threshold and the second threshold, and they are two values set according to requirements.
  • Step 308 Obtain and save the optimized control model.
  • this embodiment can add the evaluation of cooling capacity, so as to ensure that the temperature control of the water cooling system meets the temperature control requirements of the data center, so as to ensure the normal operation of the water cooling system It can meet the temperature control requirements of the data center and reduce the energy consumption of the water cooling system.
  • the third embodiment of the present application applies the control model optimization method for the water cooling system Take the water cooling system of the data center described in the table below as an example.
  • control model optimization method of the water cooling system includes:
  • Step 501 Obtain and analyze the offline data packet of data center historical collection uploaded by the data center to obtain a data sample.
  • each data sample includes the output state parameters of the water cooling system, the environmental state parameters of the data center, the control parameters of the water cooling system, and the operating state parameters of the data center.
  • a data sample includes: the output state parameters of the water cooling system include the energy consumption parameter of the water cooling system (CoolingEnergy), the current cooling capacity parameter of the water cooling system (Cooling_cal), the air temperature parameter at the end of the water cooling system (AirOutAvgTemp), the end return of the water cooling system. Air temperature parameter (AirInAvgTemp);
  • Data center environmental status parameters include outdoor dry bulb temperature (OutsideDBTemp1), outdoor relative humidity (OutsideRHumidity1), and outdoor wet bulb temperature (OutsideWetTemp1);
  • Data center operating status parameters include IT energy consumption (ITEnergy) and data center temperature setting (DCRoomTempSet);
  • the control parameters of the water cooling system include the cooling tower running number (CTNum), the freezing pump running number (CHWPNum), the cooling pump running number (CWPNum), the plate replacement running number (HENum), the plate replacement water temperature setting value (HECHWSTempSet), the cooling tower water supply Temperature setting value (CTWSTempSet), refrigeration header 1 differential pressure setting value (CH1_CWSPress), refrigeration header 2 differential pressure setting value (CH2_CWSPress), cooling water pump 1 operating frequency setting value (CWP1_Frequency), cooling water pump 2 operating frequency setting value (CWP2_Frequency) , cooling tower 1 fan 1 operating frequency setting value (CT1_FAN1_Frequency), cooling tower 1 fan 2 operating frequency setting value (CT1_FAN2_Frequency), cooling tower 2 fan 1 operating frequency setting value (CT2_FAN1_Frequency), cooling tower 2 fan 2 operating frequency setting value ( CT2_FAN2_Frequency), chilled water pump 1 operating frequency setting value (CHWP1_Frequency), chilled
  • the model before executing the next step, it is also necessary to create a data center water cooling system output state transition model M1 and a water cooling system control parameter exploration optimization model M2, and initialize the model M1 and the model M2.
  • the model After the model is created, it also includes setting the maximum training times of the model M2 and the maximum exploration times of the water-cooling system control parameters of the randomly sampled samples.
  • the maximum training times of the model M2 is equivalent to the first threshold in the second embodiment.
  • the maximum number of searches for the water-cooling system control parameters of the sampled samples corresponds to the second threshold in the second embodiment.
  • step 502 the output state transition model M1 is trained by using the parameters of the data center, and the output state transition model M3 of the water cooling system of the data center after the training is optimized is obtained.
  • the data center environmental state parameters, data center operating state parameters, and water cooling system control parameters are vectorized to generate a data center hybrid state representation through fusion operations, and the data center hybrid state representation is used as the input feature of the model M1, while the water cooling
  • the system output state parameters are vectorized as the output features of the model M1, and the model M1 is trained. After the model optimization meets the setting requirements, the training is stopped, and the model M3 is obtained and saved.
  • Step 503 Randomly sample the parsed data samples to obtain a data sample.
  • Step 504 obtain the data center state representation vector S and the water cooling system reference control parameter vector A_base according to the sample data.
  • Step 505 Input the vector S and the vector A_base into the model M3, and obtain the corresponding reference parameter param_base of the output state of the water cooling system.
  • the vector S and the vector A_base are fused and calculated to generate a data center mixed state representation vector, and then the data center mixed state representation vector is input into the model M3 to obtain the parameter param_base output by the model.
  • the parameter param_base actually outputs the state parameter param_hist of the first and previous water cooling system of each sample. Specifically, the value of the parameter param_base is assigned to the parameter param_hist.
  • Step 506 Input the vector S into the model M2 to obtain the water-cooling system control exploratory parameter vector A_explore.
  • the vector A_explore is the control parameter corresponding to the next state obtained after one exploration, including at least the number of cooling tower operations (CTNum), the number of refrigeration pump operations (CHWPNum), the number of cooling pump operations (CWPNum), and the number of plate change operations.
  • HENum plate replacement water temperature setting value (HECHWSTempSet), cooling tower water supply temperature setting value (CTWSTempSet), refrigeration header 1 differential pressure setting value (CH1_CWSPress), refrigeration header 2 differential pressure setting value (CH2_CWSPress), cooling water pump 1 running Frequency setting value (CWP1_Frequency), cooling water pump 2 operating frequency setting value (CWP2_Frequency), cooling tower 1 fan 1 operating frequency setting value (CT1_FAN1_Frequency), cooling tower 1 fan 2 operating frequency setting value (CT1_FAN2_Frequency), cooling tower 2 fan 1 operating Frequency setting value (CT2_FAN1_Frequency), cooling tower 2 fan 2 operating frequency setting value (CT2_FAN2_Frequency), chilled water pump 1 operating frequency setting value (CHWP1_Frequency), chilled water pump 2 operating frequency setting value (CHWP2_Frequency).
  • Step 507 Obtain the current water cooling system output state parameter param_explore according to the vector S and the vector A_explore, further obtain the energy consumption evaluation value and the cooling capacity evaluation value, and obtain the control action evaluation value according to the parameter A_base and the parameter A_explore.
  • the vector S and vector A_explore are input into the water cooling system output state transition model M3 to obtain the current water cooling system output state parameter param_explore; according to the parameter param_base, parameter param_explore and parameter param_hist, respectively obtain the energy consumption evaluation value and cooling capacity evaluation value.
  • the vector S and A_explore are combined and calculated to generate the data center mixed state representation vector S_mix, and then the vector S_mix is input into the model M3 to obtain the parameter param_explore output by the model, and then obtained according to the parameter param_base, parameter param_explore and parameter param_hist
  • the energy consumption evaluation method is as follows:
  • reward_ce is the energy consumption evaluation value
  • the two 0.5 are the weights of the deviation degree of energy consumption and the growth rate of energy consumption.
  • the arithmetic average method is used for comprehensive calculation here, and the weight is set to (0.5, 0.5), but the weight can also be set to other values according to actual needs. In other examples, other statistical analysis methods other than average may also be used for calculation, which will not be repeated here.
  • the cooling capacity evaluation value is obtained as follows:
  • the cooling capacity parameter Cooling_cal is obtained from the water cooling system output state reference parameter param_base as the reference cooling capacity cool_cal_ ⁇
  • the cooling capacity parameter Cooling_cal is obtained from the current water cooling system output state parameter param_explore as the cooling capacity parameter corresponding to the current water cooling system control exploratory parameter cool_cal_t, and calculate the cooling capacity constraint evaluation value g(cool_cal_t, cool_cal_ ⁇ ) through the water cooling system control parameter cooling capacity constraint evaluation function g(x).
  • the constraint evaluation function g(x) can be designed according to business requirements, and the specific form of the constraint evaluation function g(x) is not limited.
  • the current water-cooling system control exploratory parameter constraint evaluation value f(A_base, A_explore) is calculated by the water-cooling system control parameter constraint evaluation function f(x).
  • the water cooling system control parameter constraint evaluation function f(x) can be designed according to business requirements, and the specific form of the water cooling system control parameter constraint evaluation function f(x) is not limited.
  • Step 508 Obtain a control reward according to the action evaluation value, the cooling capacity evaluation value and the energy consumption evaluation value.
  • the action constraint evaluation value is first obtained according to the action evaluation value and the cooling capacity evaluation value.
  • safe_eval_action 0.5 ⁇ f(A_base,A_explore)+0.5 ⁇ g(cool_cal_t,cool_cal_ ⁇ )
  • safe_eval_action is the action constraint evaluation value
  • f(A_base, A_explore) and g(cool_cal_t, cool_cal_ ⁇ ) are the action evaluation value and the coolness evaluation value, respectively
  • the two 0.5 are the weights of the action evaluation value and the coolness evaluation value respectively.
  • the arithmetic average method is used for comprehensive calculation here, and the weight is set to (0.5, 0.5), but the weight can also be set to other values according to actual needs. In other examples, other statistical analysis methods other than average may also be used for calculation, which will not be repeated here.
  • control reward is calculated through the energy consumption evaluation value and the water cooling system control parameter action constraint evaluation value.
  • R is the control reward
  • reward_ce is the energy consumption evaluation value
  • safe_eval_action is the action constraint evaluation value
  • the two 0.5 are the weights of the energy consumption evaluation value and the action constraint evaluation value respectively.
  • the arithmetic average method is used for comprehensive calculation here, and the weight is set to (0.5, 0.5), but the weight can also be set to other values according to actual needs. In other examples, other statistical analysis methods other than average may also be used for calculation, which will not be repeated here.
  • R 0.5 ⁇ reward_ce+0.25 ⁇ f(A_base,A_explore)+0.25 ⁇ g(cool_cal_t,cool_cal_ ⁇ )
  • R is the control reward
  • reward_ce is the energy consumption evaluation value
  • f(A_base, A_explore) is the action evaluation value
  • g(cool_cal_t, cool_cal_ ⁇ ) is the cooling capacity evaluation value
  • 0.5, 0.25, and 0.25 are the weights.
  • Step 509 Store the tuple (S, A_explore, R, S) in the experience sample pool and update the parameter param_hist to the parameter param_explore.
  • Step 510 detecting whether the current number of explorations reaches the maximum number.
  • step 506 if yes, go to step 506, if not, go to step 511.
  • Step 511 using the data in the experience sample pool to optimize the model M2.
  • one sample can obtain the data required for one optimization of the model M2, and the optimization times are incremented by 1 after one time.
  • Step 512 detecting whether the current optimization times reaches the maximum value.
  • step 505 if yes, go to step 505, if not, go to step 513.
  • step 513 the model M2 is saved, and an on-line deployment field test is performed on the model M2.
  • the control model optimization method for a water cooling system provides training data for the created state transition model and control model by acquiring the parameters of the data center, and then trains and optimizes the state transition model according to the parameters of the data center to provide the optimal control model.
  • the real, offline environment is used for interactive training, and then the control model is optimized according to the optimized state transition model and the parameters of the data center to realize the optimization of the control model of the water cooling system.
  • the present application provides a control model for the water cooling system of the data center, which can effectively reduce the energy consumption of the water cooling system.
  • the present application can also optimize the control model instead of using the initial control model, which is safer and more reliable. Provide an optimized control model for the water cooling system of the data center through the interaction process of the real interactive environment, so that it can fit the real environment and effectively reduce the energy consumption of the water cooling system without affecting the normal operation of the data center.
  • the fourth embodiment of the present application relates to an electronic device, as shown in FIG. 6 , comprising: at least one processor 601 ; and a memory 602 communicatively connected to the at least one processor 601 ; wherein the memory 602 stores data that can be accessed by at least one processor 601 .
  • Instructions executed by one processor 601, the instructions are executed by at least one processor 601, so that at least one processor 601 can execute the control model optimization method for a water cooling system described in any of the above method embodiments.
  • the memory 602 and the processor 601 are connected by a bus, and the bus may include any number of interconnected buses and bridges, and the bus connects one or more processors 601 and various circuits of the memory 602 together.
  • the bus may also connect together various other circuits, such as peripherals, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein.
  • the bus interface provides the interface between the bus and the transceiver.
  • a transceiver may be a single element or multiple elements, such as multiple receivers and transmitters, providing a means for communicating with various other devices over a transmission medium.
  • the data processed by the processor 601 is transmitted on the wireless medium through the antenna, and further, the antenna also receives the data and transmits the data to the processor 601 .
  • Processor 601 is responsible for managing the bus and general processing, and may also provide various functions including timing, peripheral interface, voltage regulation, power management, and other control functions.
  • the memory 602 may be used to store data used by the processor 601 when performing operations.
  • the fifth embodiment of the present application relates to a computer-readable storage medium storing a computer program.
  • the above method embodiments are implemented when the computer program is executed by the processor.
  • the aforementioned storage medium includes: U disk, mobile hard disk, Read-Only Memory (ROM, Read-Only Memory), Random Access Memory (RAM, Random Access Memory), magnetic disk or optical disk and other media that can store program codes .

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Abstract

Selon des modes de réalisation, la présente invention se rapporte au domaine technique du traitement de données, et concerne un procédé d'optimisation d'un modèle de commande d'un système de refroidissement d'eau, un dispositif électronique et un support de stockage. Le procédé d'optimisation d'un modèle de commande d'un système de refroidissement d'eau comprend les étapes suivantes : obtenir des paramètres d'un centre de données ; créer un modèle de transition d'état et un modèle de commande ; optimiser le modèle de transition d'état selon les paramètres du centre de données ; et optimiser le modèle de commande selon le modèle de transition d'état optimisé et les paramètres du centre de données, et obtenir le modèle de commande optimisé.
PCT/CN2021/127980 2020-11-30 2021-11-01 Procédé d'optimisation de modèle de commande de système de refroidissement d'eau, dispositif électronique et support de stockage WO2022111232A1 (fr)

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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116307946A (zh) * 2023-05-19 2023-06-23 中科航迈数控软件(深圳)有限公司 基于加工环境的自适应能耗评估方法、装置、设备及介质
WO2023246717A1 (fr) * 2022-06-23 2023-12-28 华为技术有限公司 Procédé et appareil de commande de système de refroidissement par liquide, dispositif et système
CN117408170A (zh) * 2023-12-15 2024-01-16 南京群顶科技股份有限公司 一种适用于数据中心水冷系统的节能预测控制方法
CN117529070A (zh) * 2024-01-08 2024-02-06 四川建诚智造科技有限公司 一种用于光伏逆变器稳定运行的散热控制方法及系统

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114727570B (zh) * 2022-06-09 2022-09-02 南京群顶科技有限公司 一种基于温度变化关系的制冷机组水温调节方法
CN116466672B (zh) * 2023-06-13 2023-11-10 深圳市宝腾互联科技有限公司 基于人工智能的数据中心机房参数调控方法及相关装置

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104698843A (zh) * 2015-02-06 2015-06-10 同济大学 一种基于模型预测控制的数据中心节能控制方法
CN109882996A (zh) * 2019-01-25 2019-06-14 珠海格力电器股份有限公司 一种控制的方法及设备
CN111505944A (zh) * 2019-01-30 2020-08-07 珠海格力电器股份有限公司 节能控制策略学习方法、实现空调节能控制的方法及装置
CN111609534A (zh) * 2020-05-25 2020-09-01 珠海拓芯科技有限公司 温度控制方法、装置及中央温度控制系统
WO2020199648A1 (fr) * 2019-04-01 2020-10-08 珠海格力电器股份有限公司 Procédé et dispositif de commande pour climatiseur

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104698843A (zh) * 2015-02-06 2015-06-10 同济大学 一种基于模型预测控制的数据中心节能控制方法
CN109882996A (zh) * 2019-01-25 2019-06-14 珠海格力电器股份有限公司 一种控制的方法及设备
CN111505944A (zh) * 2019-01-30 2020-08-07 珠海格力电器股份有限公司 节能控制策略学习方法、实现空调节能控制的方法及装置
WO2020199648A1 (fr) * 2019-04-01 2020-10-08 珠海格力电器股份有限公司 Procédé et dispositif de commande pour climatiseur
CN111609534A (zh) * 2020-05-25 2020-09-01 珠海拓芯科技有限公司 温度控制方法、装置及中央温度控制系统

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
JIAMIN DU, SHUHONG LI, XINMEI LI: "Energy-saving optimization of cooling system based on ＲesNet-GWO", JOURNAL OF SOUTHEAST UNIVERSITY (NATURAL SCIENCE EDITION), vol. 50, no. 5, 1 September 2020 (2020-09-01), pages 866 - 874, XP055932774 *

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2023246717A1 (fr) * 2022-06-23 2023-12-28 华为技术有限公司 Procédé et appareil de commande de système de refroidissement par liquide, dispositif et système
CN116307946A (zh) * 2023-05-19 2023-06-23 中科航迈数控软件(深圳)有限公司 基于加工环境的自适应能耗评估方法、装置、设备及介质
CN116307946B (zh) * 2023-05-19 2023-08-18 中科航迈数控软件(深圳)有限公司 基于加工环境的自适应能耗评估方法、装置、设备及介质
CN117408170A (zh) * 2023-12-15 2024-01-16 南京群顶科技股份有限公司 一种适用于数据中心水冷系统的节能预测控制方法
CN117408170B (zh) * 2023-12-15 2024-03-08 南京群顶科技股份有限公司 一种适用于数据中心水冷系统的节能预测控制方法
CN117529070A (zh) * 2024-01-08 2024-02-06 四川建诚智造科技有限公司 一种用于光伏逆变器稳定运行的散热控制方法及系统
CN117529070B (zh) * 2024-01-08 2024-03-26 四川建诚智造科技有限公司 一种用于光伏逆变器稳定运行的散热控制方法及系统

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