Data Center Data Processing Method and System for Energy Saving Control

Abstract

The present invention discloses a data processing method and system for energy-saving control in a data center. The method includes: obtaining device sensing information of multiple computing devices in a target data center and environmental sensing information of multiple device areas; based on an energy consumption curve analysis model, analyzing the device energy consumption curve corresponding to each computing device according to the device sensing information; based on an environmental temperature analysis model, analyzing the regional temperature change curve corresponding to each device area according to the environmental sensing information; training a prediction neural network of energy consumption and temperature corresponding to the target data center according to the device energy consumption curve and the regional temperature change curve; the prediction neural network is used to predict the optimal energy consumption according to the current environmental sensing information to control the computing devices in the target data center. The present invention can realize the training of a prediction neural network of energy consumption and temperature corresponding to a target data center to achieve a dynamic and flexible energy-saving control effect.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a data center data processing method and system for energy-saving control. Background Art

[0002] With the continued rapid growth of the investment scale of the data center market and the continued expansion of the scale of data centers, the energy consumption of data centers is also rising. In addition, the development of artificial intelligence technology has increased the demand for parallel computing, and its energy consumption is even more serious. However, the resource utilization levels of various data centers are uneven. Data centers generally have problems such as extensive heat dissipation design, insufficient heat dissipation capacity, uneven heat dissipation effect, and high overall energy waste. The high energy consumption of data centers not only brings about increased power consumption and a sharp rise in operating costs, but also generates a heavy carbon emission burden. Therefore, how to use technical means and measures to reduce the energy consumption of data centers has become a top priority. Most of the existing energy-saving control methods for data centers still remain in the direction of improving hardware heat dissipation equipment, and do not fully consider the equipment energy consumption curve and environmental temperature change curve in the data center. Therefore, the hardware cost of its heat dissipation design is higher, and it is impossible to achieve dynamic and flexible energy-saving control effects at the algorithm level. It can be seen that the existing technology has defects that need to be solved urgently. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a data center data processing method and system for energy-saving control, which can realize the training of a predictive neural network for energy consumption and temperature corresponding to a target data center, so as to achieve a dynamic and flexible energy-saving control effect.

[0004] In order to solve the above technical problems, the first aspect of the present invention discloses a data center data processing method for energy-saving control, the method comprising:

[0005] Acquire device sensor information of a plurality of computing devices in a target data center and environmental sensor information of a plurality of device areas;

[0006] Based on the energy consumption curve analysis model, according to the device sensing information, analyzing the device energy consumption curve corresponding to each of the computing devices;

[0007] Based on the environmental temperature analysis model, analyzing the regional temperature change curve corresponding to each of the equipment areas according to the environmental sensing information;

[0008] Based on the equipment energy consumption curve and the regional temperature change curve, a prediction neural network for energy consumption and temperature corresponding to the target data center is trained; the prediction neural network is used to predict the optimal energy consumption based on the current environmental sensor information to control the computing equipment of the target data center.

[0009] As an alternative implementation, in the first aspect of the present invention, the device sensing information includes device temperature, device humidity, device performance, device image, and device displacement.

[0010] As an alternative implementation, in the first aspect of the present invention, the environmental sensing information includes environmental temperature, environmental humidity, environmental image, and environmental infrared detection information.

[0011] As an alternative implementation, in the first aspect of the present invention, based on the energy consumption curve analysis model, according to the device sensing information, analyzing the device energy consumption curve corresponding to each computing device includes:

[0012] For each computing device, based on the time-point sensing information corresponding to each time point in the device sensing information corresponding to the computing device, predicting the energy consumption information corresponding to the time-point sensing information based on a neural network;

[0013] According to the time points and energy consumption information corresponding to all the time-point sensing information, establishing a device energy consumption curve with variables of time and energy consumption for the computing device.

[0014] As an alternative implementation, in the first aspect of the present invention, predicting the energy consumption information corresponding to the time-point sensing information based on a neural network includes:

[0015] Inputting the data of each sensing type in the time-point sensing information into a trained multi-source information energy consumption prediction neural network model to obtain the energy consumption prediction information corresponding to the data of each sensing type; the multi-source information energy consumption prediction neural network model is trained by a training data set including training data of multiple sensing types and corresponding energy consumption annotations; the sensing type is device temperature, device humidity, device performance, device image, or device displacement;

[0016] Calculating the weighted sum average of the energy consumption prediction information corresponding to the data of all sensing types in the time-point sensing information to obtain the energy consumption information corresponding to the time-point sensing information.

[0017] As an alternative implementation, in the first aspect of the present invention, based on the environmental temperature analysis model, according to the environmental sensing information, analyzing the regional temperature change curve corresponding to each device area includes:

[0018] For each device area, based on the time-point environmental sensing information corresponding to each time point in the environmental sensing information corresponding to the device area, predicting the temperature information corresponding to the time-point environmental sensing information based on a neural network;

[0019] Based on the time points and temperature information corresponding to all the environmental sensing information at the time points, establish a regional temperature change curve with variables of time and temperature corresponding to the device area.

[0020] As an optional implementation manner, in the first aspect of the present invention, the predicting the temperature information corresponding to the environmental sensing information at the time point based on a neural network includes:

[0021] Input each non-temperature sensing information in the environmental sensing information at the time point into a trained multi-source information temperature prediction neural network model to obtain temperature prediction information corresponding to each non-temperature sensing information; the multi-source information energy consumption prediction neural network model is trained by a training data set including training non-temperature sensing information of various sensing types and corresponding energy consumption labels; the non-temperature sensing information is environmental humidity, environmental image or environmental infrared detection information;

[0022] Calculate the weighted sum average of the temperature prediction information corresponding to all non-temperature sensing information in the environmental sensing information at the time point to obtain the corrected temperature information corresponding to the environmental sensing information at the time point;

[0023] Calculate the average value between the corrected temperature information and the environmental temperature information in the environmental sensing information at the time point to obtain the temperature information corresponding to the environmental sensing information at the time point.

[0024] As an optional implementation manner, in the first aspect of the present invention, the training the prediction neural network of energy consumption and temperature corresponding to the target data center according to the device energy consumption curve and the regional temperature change curve includes:

[0025] Calculate the overall curve positive correlation degree between the device energy consumption curve of any one of the computing devices and the regional temperature change curve of any one of the device areas;

[0026] Determine the computing device and the device area with the overall curve positive correlation degree greater than the first threshold as a device area corresponding group;

[0027] For any one of the device area corresponding groups, calculate the curve positive correlation degree in any time period of the device energy consumption curve and the regional temperature change curve corresponding to the device area corresponding group;

[0028] Determine the curve part with the curve positive correlation degree greater than the second threshold in the device energy consumption curve and the regional temperature change curve as the training curve part;

[0029] Determine the energy consumption information, the temperature information and the environmental sensing information corresponding to the temperature information at any same time point in any one of the training curve parts to obtain a plurality of training data pairs;

[0030] Input each of the training data pairs into a prediction neural network for training until convergence, obtaining a prediction neural network capable of predicting energy consumption information based on environmental sensing information.

[0031] A second aspect of the embodiments of the present invention discloses a data center data processing system for energy-saving control, the system comprising:

[0032] An acquisition module, configured to acquire device sensing information of a plurality of computing devices and environmental sensing information of a plurality of device areas of a target data center;

[0033] A first analysis module, configured to analyze a device energy consumption curve corresponding to each computing device based on an energy consumption curve analysis model according to the device sensing information;

[0034] A second analysis module, configured to analyze a regional temperature change curve corresponding to each device area based on an environmental temperature analysis model according to the environmental sensing information;

[0035] A training module, configured to train a prediction neural network of energy consumption and temperature corresponding to the target data center according to the device energy consumption curve and the regional temperature change curve; the prediction neural network is used to predict the optimal energy consumption according to current environmental sensing information to control the computing devices of the target data center.

[0036] As an optional implementation manner, in the second aspect of the present invention, the device sensing information includes device temperature, device humidity, device performance, device image, and device displacement.

[0037] As an optional implementation manner, in the second aspect of the present invention, the environmental sensing information includes environmental temperature, environmental humidity, environmental image, and environmental infrared detection information.

[0038] As an optional implementation manner, in the second aspect of the present invention, the specific manner in which the first analysis module analyzes a device energy consumption curve corresponding to each computing device based on an energy consumption curve analysis model according to the device sensing information includes:

[0039] For each computing device, based on a neural network, predict energy consumption information corresponding to the time point sensing information at each time point in the device sensing information corresponding to the computing device;

[0040] According to the time points and energy consumption information corresponding to all the time point sensing information, establish a device energy consumption curve with variables of time and energy consumption corresponding to the computing device.

[0041] As an optional implementation manner, in the second aspect of the present invention, the specific manner in which the first analysis module predicts the energy consumption information corresponding to the time-point sensing information based on a neural network includes:

[0042] Input the data of each sensing type in the time-point sensing information into a trained multi-source information energy consumption prediction neural network model to obtain the energy consumption prediction information corresponding to the data of each sensing type; the multi-source information energy consumption prediction neural network model is trained through a training data set including training data of multiple sensing types and corresponding energy consumption annotations; the sensing type is device temperature, device humidity, device performance, device image, or device displacement;

[0043] Calculate the weighted sum average of the energy consumption prediction information corresponding to the data of all sensing types in the time-point sensing information to obtain the energy consumption information corresponding to the time-point sensing information.

[0044] As an optional implementation manner, in the second aspect of the present invention, the specific manner in which the second analysis module analyzes the regional temperature change curve corresponding to each device area based on the environmental temperature analysis model according to the environmental sensing information includes:

[0045] For each device area, based on the time-point environmental sensing information corresponding to each time point in the environmental sensing information corresponding to the device area, predict the temperature information corresponding to the time-point environmental sensing information based on a neural network;

[0046] Establish a regional temperature change curve with variables of time and temperature corresponding to the device area according to the time points and temperature information corresponding to all the time-point environmental sensing information.

[0047] As an optional implementation manner, in the second aspect of the present invention, the specific manner in which the second analysis module predicts the temperature information corresponding to the time-point environmental sensing information based on a neural network includes:

[0048] Input each non-temperature sensing information in the time-point environmental sensing information into a trained multi-source information temperature prediction neural network model to obtain the temperature prediction information corresponding to each non-temperature sensing information; the multi-source information energy consumption prediction neural network model is trained through a training data set including training non-temperature sensing information of multiple sensing types and corresponding energy consumption annotations; the non-temperature sensing information is environmental humidity, environmental image, or environmental infrared detection information;

[0049] Calculate the weighted sum average of the temperature prediction information corresponding to all non-temperature sensing information in the time-point environmental sensing information to obtain the corrected temperature information corresponding to the time-point environmental sensing information;

[0050] Calculate the average value between the corrected temperature information and the ambient temperature information in the ambient sensing information at the time point to obtain the temperature information corresponding to the ambient sensing information at the time point.

[0051] As an optional implementation manner, in the second aspect of the present invention, the specific manner in which the training module trains the prediction neural network of energy consumption and temperature corresponding to the target data center according to the device energy consumption curve and the regional temperature change curve includes:

[0052] Calculate the overall curve positive correlation degree between the device energy consumption curve of any one of the computing devices and the regional temperature change curve of any one of the device regions;

[0053] Determine the computing device and the device region with the overall curve positive correlation degree greater than the first threshold as a device region corresponding group;

[0054] For any one of the device region corresponding groups, calculate the curve positive correlation degree in any time period in the device energy consumption curve and the regional temperature change curve corresponding to the device region corresponding group;

[0055] Determine the curve part with the curve positive correlation degree greater than the second threshold in the device energy consumption curve and the regional temperature change curve as the training curve part;

[0056] Determine the energy consumption information, the temperature information, and the ambient sensing information corresponding to the temperature information at any same time point in any one of the training curve parts to obtain a plurality of training data pairs;

[0057] Input each training data pair into the prediction neural network for training until convergence to obtain a prediction neural network capable of predicting energy consumption information according to ambient sensing information.

[0058] The third aspect of the present invention discloses another data center data processing system for energy-saving control, and the system includes:

[0059] A memory storing executable program code;

[0060] A processor coupled to the memory;

[0061] The processor calls the executable program code stored in the memory and executes some or all of the steps in the data center data processing method for energy-saving control disclosed in the first aspect of the present invention.

[0062] The fourth aspect of the present invention discloses a computer storage medium, and the computer storage medium stores computer instructions, which are used to execute some or all of the steps in the data center data processing method for energy-saving control disclosed in the first aspect of the present invention when the computer instructions are called.

[0063] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0064] The present invention can analyze the device energy consumption curve corresponding to each computing device based on the energy consumption curve analysis model and analyze the regional temperature change curve corresponding to each device area based on the environmental temperature analysis model, so as to train the prediction neural network corresponding to the target data center, and subsequently realize predicting the optimal energy consumption according to the current environmental sensing information to control the computing devices of the target data center, thereby enabling the training of the prediction neural network for the energy consumption and temperature corresponding to the target data center to achieve a dynamic and flexible energy-saving control effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0066] Figure 1 FIG. is a schematic flow chart of a data center data processing method for energy-saving control disclosed in an embodiment of the present invention.

[0067] Figure 2 FIG. is a schematic structural diagram of a data center data processing system for energy-saving control disclosed in an embodiment of the present invention.

[0068] Figure 3 FIG. is a schematic structural diagram of another data center data processing system for energy-saving control disclosed in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0069] In order to enable those skilled in the art to better understand the solutions of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0070] In the description, claims and above-mentioned drawings of the present invention, terms such as "first", "second", etc. are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "comprise" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products or equipment.

[0071] Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the present invention. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0072] The present invention discloses a data center data processing method and system for energy-saving control, which can respectively analyze the device energy consumption curve corresponding to each computing device based on an energy consumption curve analysis model and analyze the regional temperature change curve corresponding to each device area based on an environmental temperature analysis model, so as to train a prediction neural network corresponding to a target data center, and subsequently realize predicting the optimal energy consumption according to the current environmental sensing information to control the computing devices of the target data center, thereby enabling the training of a prediction neural network for energy consumption and temperature corresponding to the target data center to achieve a dynamic and flexible energy-saving control effect. The following will be described in detail respectively.

[0073] Embodiment 1

[0074] Please refer to Figure 1 , Figure 1 which is a schematic flow chart of a data center data processing method for energy-saving control disclosed in an embodiment of the present invention. Among them, Figure 1 the described data center data processing method for energy-saving control can be applied to a data processing system / data processing device / data processing server (wherein, the server includes a local processing server or a cloud processing server). As Figure 1 shown, the data center data processing method for energy-saving control can include the following operations:

[0075] 101. Obtain the device sensing information of multiple computing devices in the target data center and the environmental sensing information of multiple device areas.

[0076] 102. Based on the energy consumption curve analysis model, analyze the device energy consumption curve corresponding to each computing device according to the device sensing information.

[0077] 103. Based on the environmental temperature analysis model, analyze the regional temperature change curve corresponding to each equipment area according to the environmental sensing information.

[0078] 104. According to the equipment energy consumption curve and the regional temperature change curve, train a prediction neural network for energy consumption and temperature corresponding to the target data center.

[0079] Optionally, the prediction neural network is used to predict the optimal energy consumption according to the current environmental sensing information to control the computing devices of the target data center.

[0080] It can be seen that the above-mentioned invention embodiments can respectively analyze the equipment energy consumption curve corresponding to each computing device based on the energy consumption curve analysis model and analyze the regional temperature change curve corresponding to each equipment area based on the environmental temperature analysis model, so as to train a prediction neural network corresponding to the target data center, and subsequently realize predicting the optimal energy consumption according to the current environmental sensing information to control the computing devices of the target data center, thereby enabling the training of the prediction neural network for energy consumption and temperature corresponding to the target data center to achieve a dynamic and flexible energy-saving control effect.

[0081] As an optional embodiment, in the above steps, the equipment sensing information includes equipment temperature, equipment humidity, equipment performance, equipment image, and equipment displacement.

[0082] It can be seen that through the above optional embodiment, the data content of the equipment sensing information is defined, which can fully represent the working condition of the equipment, and is subsequently used to accurately analyze the equipment energy consumption curve corresponding to each computing device, assisting in the training of the prediction neural network for energy consumption and temperature corresponding to the target data center to achieve a dynamic and flexible energy-saving control effect.

[0083] As an optional embodiment, in the above steps, the environmental sensing information includes environmental temperature, environmental humidity, environmental image, and environmental infrared detection information.

[0084] It can be seen that through the above optional embodiment, the data content of the environmental sensing information is defined, which can fully represent the situation of the environment affected by the equipment operation, and is subsequently used to accurately analyze the regional temperature change curve corresponding to each equipment area, assisting in the training of the prediction neural network for energy consumption and temperature corresponding to the target data center to achieve a dynamic and flexible energy-saving control effect.

[0085] As an optional embodiment, in the above steps, based on the energy consumption curve analysis model, according to the equipment sensing information, analyzing the equipment energy consumption curve corresponding to each computing device includes:

[0086] For each computing device, based on the neural network, predict the energy consumption information corresponding to the time-point sensing information according to the time-point sensing information corresponding to each time point in the equipment sensing information corresponding to the computing device.

[0087] Based on the time points and energy consumption information corresponding to the sensing information at all time points, establish an equipment energy consumption curve with variables of time and energy consumption for this computing device.

[0088] It can be seen that through the above optional embodiments, the energy consumption information corresponding to the time point sensing information can be predicted by a neural network to establish an equipment energy consumption curve with variables of time and energy consumption for the computing device, which is subsequently used to accurately train the prediction neural network corresponding to the target data center, assisting in the training of the prediction neural network for energy consumption and temperature corresponding to the target data center, so as to achieve a dynamic and flexible energy-saving control effect.

[0089] As an optional embodiment, in the above steps, predicting the energy consumption information corresponding to the time point sensing information based on a neural network includes:

[0090] Input the data of each sensing type in the time point sensing information into the trained multi-source information energy consumption prediction neural network model to obtain the energy consumption prediction information corresponding to the data of each sensing type; optionally, the multi-source information energy consumption prediction neural network model is trained by a training data set including training data of multiple sensing types and corresponding energy consumption annotations; the sensing type is equipment temperature, equipment humidity, equipment performance, equipment image, or equipment displacement.

[0091] Calculate the weighted sum average of the energy consumption prediction information corresponding to the data of all sensing types in the time point sensing information to obtain the energy consumption information corresponding to the time point sensing information.

[0092] It can be seen that through the above optional embodiments, the energy consumption prediction information corresponding to the data of each sensing type can be predicted by the trained multi-source information energy consumption prediction neural network model. Among them, the neural network trained with multi-source information can predict prediction results with reference value for each other for multi-source sensing information, and then determine the energy consumption information corresponding to the time point sensing information based on weighted sum calculation. Subsequently, it is used to establish an equipment energy consumption curve with variables of time and energy consumption for the computing device, assisting in the training of the prediction neural network for energy consumption and temperature corresponding to the target data center, so as to achieve a dynamic and flexible energy-saving control effect.

[0093] As an optional embodiment, in the above steps, based on the environmental temperature analysis model, analyzing the regional temperature change curve corresponding to each equipment area according to the environmental sensing information includes:

[0094] For each equipment area, based on the time point environmental sensing information corresponding to each time point in the environmental sensing information corresponding to this equipment area, predict the temperature information corresponding to the time point environmental sensing information based on a neural network.

[0095] Based on the time points and temperature information corresponding to the environmental sensing information at all time points, establish a regional temperature change curve with variables of time and temperature corresponding to the device area.

[0096] It can be seen that through the above optional embodiments, the temperature information corresponding to the environmental sensing information at the time point can be predicted through a neural network to establish a regional temperature change curve with variables of time and temperature corresponding to the device area, which is subsequently used to accurately train the prediction neural network corresponding to the target data center, assisting in the training of the prediction neural network for energy consumption and temperature corresponding to the target data center, so as to achieve a dynamic and flexible energy-saving control effect.

[0097] As an optional embodiment, in the above steps, predicting the temperature information corresponding to the environmental sensing information at the time point based on a neural network includes:

[0098] Input each non-temperature sensing information in the environmental sensing information at the time point into the trained multi-source information temperature prediction neural network model to obtain the temperature prediction information corresponding to each non-temperature sensing information; optionally, the multi-source information energy consumption prediction neural network model is trained through a training data set including training non-temperature sensing information of various sensing types and corresponding energy consumption annotations; the non-temperature sensing information is environmental humidity, environmental image or environmental infrared detection information;

[0099] Calculate the weighted sum average of the temperature prediction information corresponding to all non-temperature sensing information in the environmental sensing information at the time point to obtain the corrected temperature information corresponding to the environmental sensing information at the time point;

[0100] Calculate the average value between the corrected temperature information and the environmental temperature information in the environmental sensing information at the time point to obtain the temperature information corresponding to the environmental sensing information at the time point.

[0101] It can be seen that through the above optional embodiments, the temperature prediction information corresponding to each non-temperature sensing information can be predicted through the trained multi-source information temperature prediction neural network model. Among them, the neural network trained through multi-source information can predict prediction results with reference value for each other for multi-source sensing information, and then determine the corrected temperature information based on weighted sum calculation to correct the environmental temperature information in the environmental sensing information at the time point, so as to calculate more accurate temperature data, which is subsequently used to establish a regional temperature change curve with variables of time and temperature corresponding to the device area, assisting in the training of the prediction neural network for energy consumption and temperature corresponding to the target data center, so as to achieve a dynamic and flexible energy-saving control effect.

[0102] As an optional embodiment, in the above steps, training the prediction neural network for energy consumption and temperature corresponding to the target data center according to the device energy consumption curve and the regional temperature change curve includes:

[0103] Calculate the overall curve positive correlation degree between the device energy consumption curve of any computing device and the regional temperature change curve of any device area;

[0104] Determine the computing devices and device areas with an overall curve positive correlation degree greater than the first threshold as a corresponding group of device areas;

[0105] For any corresponding group of device areas, calculate the curve positive correlation degree in any time period of the device energy consumption curve and the regional temperature change curve corresponding to this corresponding group of device areas;

[0106] Determine the curve part with a curve positive correlation degree greater than the second threshold in the device energy consumption curve and the regional temperature change curve as the training curve part;

[0107] Determine the energy consumption information, temperature information, and environmental sensing information corresponding to the temperature information at any same time point in any training curve part to obtain multiple training data pairs;

[0108] Input each training data pair into the prediction neural network for training until convergence to obtain a prediction neural network that can predict energy consumption information based on environmental sensing information.

[0109] It can be seen that through the above optional embodiments, it is possible to determine a more relevant training data set by calculating and screening the positive correlation degree between curves, so as to train a prediction neural network that can predict energy consumption information based on environmental sensing information, thereby enabling the training of the prediction neural network for energy consumption and temperature corresponding to the target data center to achieve a dynamic and flexible energy-saving control effect.

[0110] Embodiment 2

[0111] Please refer to Figure 2 , Figure 2 which is a schematic structural diagram of a data center data processing system for energy-saving control disclosed in an embodiment of the present invention. Among them, Figure 2 The described data center data processing system for energy-saving control can be applied to a data processing system / data processing device / data processing server (wherein, the server includes a local processing server or a cloud processing server). As Figure 2 shown, the data center data processing system for energy-saving control can include:

[0112] An acquisition module 201, configured to acquire device sensing information of multiple computing devices and environmental sensing information of multiple device areas of the target data center.

[0113] A first analysis module 202, configured to analyze the device energy consumption curve corresponding to each computing device based on the energy consumption curve analysis model according to the device sensing information.

[0114] The second analysis module 203 is configured to analyze the regional temperature change curve corresponding to each device area according to the environmental sensing information based on the environmental temperature analysis model.

[0115] The training module 204 is configured to train a prediction neural network for energy consumption and temperature corresponding to the target data center according to the device energy consumption curve and the regional temperature change curve.

[0116] Optionally, the prediction neural network is used to predict the optimal energy consumption according to the current environmental sensing information to control the computing devices of the target data center.

[0117] It can be seen that the above-mentioned invention embodiments can respectively analyze the device energy consumption curve corresponding to each computing device based on the energy consumption curve analysis model and analyze the regional temperature change curve corresponding to each device area based on the environmental temperature analysis model, so as to train the prediction neural network corresponding to the target data center, and subsequently realize predicting the optimal energy consumption according to the current environmental sensing information to control the computing devices of the target data center, thereby enabling the training of the prediction neural network for energy consumption and temperature corresponding to the target data center to achieve a dynamic and flexible energy-saving control effect.

[0118] As an optional embodiment, the device sensing information includes device temperature, device humidity, device performance, device image, and device displacement.

[0119] It can be seen that through the above optional embodiment, the data content of the device sensing information is defined, which can fully characterize the working condition of the device, and is subsequently used to accurately analyze the device energy consumption curve corresponding to each computing device, assisting in the training of the prediction neural network for energy consumption and temperature corresponding to the target data center to achieve a dynamic and flexible energy-saving control effect.

[0120] As an optional embodiment, the environmental sensing information includes environmental temperature, environmental humidity, environmental image, and environmental infrared detection information.

[0121] It can be seen that through the above optional embodiment, the data content of the environmental sensing information is defined, which can fully characterize the situation of the environment affected by the device operation, and is subsequently used to accurately analyze the regional temperature change curve corresponding to each device area, assisting in the training of the prediction neural network for energy consumption and temperature corresponding to the target data center to achieve a dynamic and flexible energy-saving control effect.

[0122] As an optional embodiment, the specific manner in which the first analysis module analyzes the device energy consumption curve corresponding to each computing device based on the energy consumption curve analysis model according to the device sensing information includes:

[0123] For each computing device, based on the time-point sensing information corresponding to each time point in the device sensing information corresponding to the computing device, predict the energy consumption information corresponding to the time-point sensing information based on the neural network;

[0124] Based on the time points and energy consumption information corresponding to the sensing information at all time points, establish an equipment energy consumption curve with variables of time and energy consumption for this computing device.

[0125] It can be seen that through the above optional embodiments, the energy consumption information corresponding to the time point sensing information can be predicted by a neural network, so as to establish an equipment energy consumption curve with variables of time and energy consumption for the computing device, which is subsequently used to accurately train the prediction neural network corresponding to the target data center, assisting in realizing the training of the prediction neural network for energy consumption and temperature corresponding to the target data center, so as to achieve a dynamic and flexible energy-saving control effect.

[0126] As an optional embodiment, the specific manner in which the first analysis module predicts the energy consumption information corresponding to the time point sensing information based on the neural network includes:

[0127] Input the data of each sensing type in the time point sensing information into the trained multi-source information energy consumption prediction neural network model to obtain the energy consumption prediction information corresponding to the data of each sensing type; optionally, the multi-source information energy consumption prediction neural network model is trained through a training data set including training data of multiple sensing types and corresponding energy consumption annotations; the sensing types are equipment temperature, equipment humidity, equipment performance, equipment image, or equipment displacement;

[0128] Calculate the weighted sum average of the energy consumption prediction information corresponding to the data of all sensing types in the time point sensing information to obtain the energy consumption information corresponding to the time point sensing information.

[0129] It can be seen that through the above optional embodiments, the energy consumption prediction information corresponding to the data of each sensing type can be predicted by the trained multi-source information energy consumption prediction neural network model. Among them, the neural network trained with multi-source information can predict prediction results with reference value for each other for multi-source sensing information, and then determine the energy consumption information corresponding to the time point sensing information based on weighted sum calculation. Subsequently, it is used to establish an equipment energy consumption curve with variables of time and energy consumption for the computing device, assisting in realizing the training of the prediction neural network for energy consumption and temperature corresponding to the target data center, so as to achieve a dynamic and flexible energy-saving control effect.

[0130] As an optional embodiment, the specific manner in which the second analysis module analyzes the regional temperature change curve corresponding to each equipment area based on the environmental temperature analysis model according to the environmental sensing information includes:

[0131] For each equipment area, based on the time point environmental sensing information corresponding to each time point in the environmental sensing information corresponding to this equipment area, predict the temperature information corresponding to the time point environmental sensing information based on the neural network;

[0132] Based on the time points and temperature information corresponding to the environmental sensing information at all time points, establish a regional temperature change curve with variables of time and temperature corresponding to the device area.

[0133] It can be seen that through the above optional embodiments, the temperature information corresponding to the environmental sensing information at the time point can be predicted through the neural network, so as to establish a regional temperature change curve with variables of time and temperature corresponding to the device area, which is subsequently used to accurately train the prediction neural network corresponding to the target data center, and assist in realizing the training of the prediction neural network for the energy consumption and temperature corresponding to the target data center, so as to achieve a dynamic and flexible energy-saving control effect.

[0134] As an optional embodiment, the specific manner in which the second analysis module predicts the temperature information corresponding to the environmental sensing information at the time point based on the neural network includes:

[0135] Input each non-temperature sensing information in the environmental sensing information at the time point into the trained multi-source information temperature prediction neural network model to obtain the temperature prediction information corresponding to each non-temperature sensing information; optionally, the multi-source information energy consumption prediction neural network model is trained through a training data set including various sensing types of training non-temperature sensing information and corresponding energy consumption annotations; the non-temperature sensing information is environmental humidity, environmental image or environmental infrared detection information;

[0136] Calculate the weighted sum average of the temperature prediction information corresponding to all non-temperature sensing information in the environmental sensing information at the time point to obtain the corrected temperature information corresponding to the environmental sensing information at the time point;

[0137] Calculate the average value between the corrected temperature information and the environmental temperature information in the environmental sensing information at the time point to obtain the temperature information corresponding to the environmental sensing information at the time point.

[0138] It can be seen that through the above optional embodiments, the temperature prediction information corresponding to each non-temperature sensing information can be predicted through the trained multi-source information temperature prediction neural network model. Among them, the neural network trained through multi-source information can predict prediction results with reference value for each other for multi-source sensing information, and then determine the corrected temperature information based on the weighted sum calculation to correct the environmental temperature information in the environmental sensing information at the time point, so as to calculate more accurate temperature data, which is subsequently used to establish a regional temperature change curve with variables of time and temperature corresponding to the device area, and assist in realizing the training of the prediction neural network for the energy consumption and temperature corresponding to the target data center, so as to achieve a dynamic and flexible energy-saving control effect.

[0139] As an optional embodiment, the specific manner in which the training module trains the prediction neural network for the energy consumption and temperature corresponding to the target data center according to the device energy consumption curve and the regional temperature change curve includes:

[0140] Calculate the overall curve positive correlation degree between the device energy consumption curve of any computing device and the regional temperature change curve of any device area;

[0141] Determine the computing devices and device areas with an overall curve positive correlation degree greater than the first threshold as a corresponding group of device areas;

[0142] For any corresponding group of device areas, calculate the curve positive correlation degree in any time period in the device energy consumption curve and the regional temperature change curve corresponding to this corresponding group of device areas;

[0143] Determine the curve parts with a curve positive correlation degree greater than the second threshold in the device energy consumption curve and the regional temperature change curve as the training curve parts;

[0144] Determine the energy consumption information, temperature information, and environmental sensing information corresponding to the temperature information at any same time point in any training curve part, and obtain multiple training data pairs;

[0145] Input each training data pair into the prediction neural network for training until convergence, and obtain a prediction neural network that can predict energy consumption information based on environmental sensing information.

[0146] It can be seen that through the above optional embodiments, it is possible to determine a more relevant training data set by calculating and screening the positive correlation degree between curves, so as to train a prediction neural network that can predict energy consumption information based on environmental sensing information, thereby enabling the training of the prediction neural network for energy consumption and temperature corresponding to the target data center, so as to achieve a dynamic and flexible energy-saving control effect.

[0147] Embodiment III

[0148] Please refer to Figure 3 , Figure 3 which is another data center data processing system for energy-saving control disclosed in the embodiments of the present invention. Figure 3 The data center data processing system for energy-saving control described is applied to a data processing system / data processing device / data processing server (wherein, the server includes a local processing server or a cloud processing server). As Figure 3 shown, the data center data processing system for energy-saving control may include:

[0149] A memory 301 storing executable program code;

[0150] A processor 302 coupled to the memory 301;

[0151] Among them, the processor 302 calls the executable program code stored in the memory 301 to execute the steps of the data center data processing method for energy-saving control described in Embodiment I.

[0152] Example 4

[0153] An embodiment of the present invention discloses a computer-readable storage medium that stores a computer program for electronic data exchange. Among them, the computer program enables a computer to execute the steps of the data center data processing method for energy-saving control described in Example 1.

[0154] Example 5

[0155] An embodiment of the present invention discloses a computer program product. The computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps of the data center data processing method for energy-saving control described in Example 1.

[0156] The above describes specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve the desired result. Additionally, the processes depicted in the drawings do not necessarily have to be performed in the specific order or continuous order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0157] The systems, devices, modules, or units illustrated in the above embodiments can be specifically implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer can be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0158] For convenience of description, when describing the above devices, they are described separately as various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0159] Those skilled in the art should understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the embodiments of this specification can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program code.

[0160] This specification is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the specification. It should be understood that each flow and / or block in the flowchart and / or block diagram, and combinations of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general purpose computers, special purpose computers, embedded processors, or other programmable data processing devices to produce a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices generate means for implementing the functions specified in the Figure 1 one or more flows and / or blocks Figure 1 one or more blocks

[0161] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufacture including instruction means that implement the functions specified in the Figure 1 one or more flows and / or blocks Figure 1 one or more blocks

[0162] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in the Figure 1 one or more flows and / or blocks Figure 1 one or more blocks

[0163] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0164] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of computer-readable media.

[0165] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0166] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0167] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0168] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0169] Finally, it should be noted that: What is disclosed by a data center data processing method and system for energy-saving control disclosed in the embodiments of the present invention is only the preferred embodiments of the present invention, which are only used to illustrate the technical solutions of the present invention, rather than limiting it; Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; And these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A data center data processing method for energy-saving control, characterized in that: The method comprises: Acquire device sensor information of a plurality of computing devices in a target data center and environmental sensor information of a plurality of device areas; Based on the energy consumption curve analysis model, according to the device sensing information, analyzing the device energy consumption curve corresponding to each of the computing devices; Based on the environmental temperature analysis model, analyzing the regional temperature change curve corresponding to each of the equipment areas according to the environmental sensing information; According to the equipment energy consumption curve and the regional temperature change curve, a prediction neural network of energy consumption and temperature corresponding to the target data center is trained, including: Calculating the overall curve positive correlation between the device energy consumption curve of any one of the computing devices and the regional temperature change curve of any one of the device regions; Determine the computing devices and the device regions whose overall curve positive correlation is greater than a first threshold as a device region corresponding group; For any of the equipment area corresponding groups, calculating the positive correlation between the equipment energy consumption curve corresponding to the equipment area corresponding group and the curve in any time period of the regional temperature change curve; Determine a curve portion in which the curve positive correlation degree is greater than a second threshold value in the device energy consumption curve and the regional temperature change curve as a training curve portion; Determine the energy consumption information and temperature information at any same time point in any of the training curve parts and the environmental sensor information corresponding to the temperature information to obtain a plurality of training data pairs; Each of the training data pairs is input into a predictive neural network for training until convergence, thereby obtaining a predictive neural network that can predict energy consumption information based on environmental sensor information; the predictive neural network is used to predict optimal energy consumption based on current environmental sensor information to control the computing equipment in the target data center.

2. The data center data processing method for energy-saving control according to claim 1, characterized in that: The device sensing information includes device temperature, device humidity, device performance, device image and device displacement.

3. The data center data processing method for energy-saving control according to claim 1, characterized in that: The environmental sensing information includes environmental temperature, environmental humidity, environmental image and environmental infrared detection information.

4. The data center data processing method for energy-saving control according to claim 1, characterized in that: The energy consumption curve analysis model is based on the device sensing information, and the device energy consumption curve corresponding to each computing device is analyzed, including: For each of the computing devices, based on the time point sensing information corresponding to each time point in the device sensing information corresponding to the computing device, predicting the energy consumption information corresponding to the time point sensing information based on the neural network; According to the time points and energy consumption information corresponding to the sensing information at all the time points, an energy consumption curve of the device corresponding to the computing device with the variables of time and energy consumption being established.

5. The data center data processing method for energy-saving control according to claim 4, characterized in that: The predicting of the energy consumption information corresponding to the sensing information at the time point based on the neural network includes: Inputting the data of each sensor type in the sensor information at the time point into the trained multi-source information energy consumption prediction neural network model to obtain energy consumption prediction information corresponding to the data of each sensor type; the multi-source information energy consumption prediction neural network model is trained by a training data set including training data of multiple sensor types and corresponding energy consumption annotations; the sensor type is device temperature, device humidity, device performance, device image or device displacement; The weighted average value of the energy consumption prediction information corresponding to the data of all sensor types in the sensor information at the time point is calculated to obtain the energy consumption information corresponding to the sensor information at the time point.

6. The data center data processing method for energy-saving control according to claim 4, characterized in that: The method of analyzing the regional temperature change curve corresponding to each of the equipment regions based on the environmental temperature analysis model and according to the environmental sensing information includes: For each of the device areas, based on the time point environment sensor information corresponding to each time point in the environment sensor information corresponding to the device area, predicting the temperature information corresponding to the time point environment sensor information based on a neural network; According to the time points and temperature information corresponding to the environmental sensing information at all the time points, a regional temperature change curve corresponding to the device area with the variables of time and temperature is established.

7. The data center data processing method for energy-saving control according to claim 6, characterized in that: The predicting the temperature information corresponding to the environmental sensor information at the time point based on the neural network includes: Input each non-temperature sensor information in the environmental sensor information at the time point into the trained multi-source information temperature prediction neural network model to obtain temperature prediction information corresponding to each non-temperature sensor information; the multi-source information energy consumption prediction neural network model is trained by a training data set including training non-temperature sensor information of multiple sensor types and corresponding energy consumption annotations; the non-temperature sensor information is environmental humidity, environmental image or environmental infrared detection information; Calculating a weighted average of the temperature prediction information corresponding to all non-temperature sensor information in the environmental sensor information at the time point to obtain the corrected temperature information corresponding to the environmental sensor information at the time point; An average value between the corrected temperature information and the ambient temperature information in the ambient sensor information at the time point is calculated to obtain temperature information corresponding to the ambient sensor information at the time point.

8. A data center data processing system for energy-saving control, characterized in that: The system comprises: An acquisition module, used to acquire device sensor information of multiple computing devices in a target data center and environmental sensor information of multiple device areas; A first analysis module, configured to analyze a device energy consumption curve corresponding to each of the computing devices based on an energy consumption curve analysis model and according to the device sensing information; A second analysis module is used to analyze a regional temperature change curve corresponding to each of the equipment regions based on an environmental temperature analysis model and according to the environmental sensing information; A training module, used to train a prediction neural network of energy consumption and temperature corresponding to the target data center according to the equipment energy consumption curve and the regional temperature change curve, including: Calculating the overall curve positive correlation between the device energy consumption curve of any one of the computing devices and the regional temperature change curve of any one of the device regions; Determine the computing devices and the device regions whose overall curve positive correlation is greater than a first threshold as a device region corresponding group; For any of the equipment area corresponding groups, calculating the positive correlation between the equipment energy consumption curve corresponding to the equipment area corresponding group and the curve in any time period of the regional temperature change curve; Determine a curve portion in which the curve positive correlation degree is greater than a second threshold value in the device energy consumption curve and the regional temperature change curve as a training curve portion; Determine the energy consumption information and temperature information at any same time point in any of the training curve parts and the environmental sensor information corresponding to the temperature information to obtain a plurality of training data pairs; Each of the training data pairs is input into a predictive neural network for training until convergence, thereby obtaining a predictive neural network that can predict energy consumption information based on environmental sensor information; the predictive neural network is used to predict optimal energy consumption based on current environmental sensor information to control the computing equipment in the target data center.

9. A data center data processing system for energy-saving control, characterized in that: The system comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the data center data processing method for energy-saving control as described in any one of claims 1-7.

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