Power consumption regulation and control method and system for data center, terminal equipment and medium

Abstract

The invention discloses a power utilization regulation and control method and system for a data center, terminal equipment and a medium, and the method comprises the steps: obtaining the site information of the data center, and determining the incidence relation between all equipment in the data center; acquiring equipment power consumption and temperature data of each piece of equipment in the data center, and determining overall power consumption and temperature data of the data center; constructing a digital twinborn model based on the association relationship, the equipment power consumption and temperature data and the overall power consumption and temperature data; and on the basis of a digital twinborn model, simulating the change of the power consumption and temperature data of the equipment to obtain the change information of the overall power consumption and temperature data, and performing power consumption control on the equipment on the basis of the change information. According to the method, the digital twinborn model is constructed, power consumption control is actively simulated, an optimal power consumption control scheme is selected, the power consumption of the data center is optimized, and the problems that the energy consumption of the data center is too high and the energy utilization efficiency is too low are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power consumption regulation and control, and in particular to a data center power consumption regulation and control method, system, terminal equipment and medium. Background Art

[0002] In the digital age, data centers are the key carriers of the informationization process and are of great significance to the development of cloud computing, 5G, artificial intelligence and other fields. They are like "data brains" that support data processing and services in various fields.

[0003] However, data centers face the problem of high power consumption, with a growth rate of more than 12% for eight consecutive years, much higher than other industries. In traditional manufacturing and commercial fields, the growth of power consumption is relatively stable or can be controlled within a reasonable range. However, data centers need to operate servers uninterruptedly, and their scale is constantly expanded and equipment is upgraded as business develops, resulting in a continuous increase in power consumption. This not only increases operating costs, but also does not conform to the concept of energy conservation and emission reduction, and puts pressure on energy and the environment.

[0004] Moreover, generally speaking, the power consumption of the cooling equipment in the data center accounts for about 30%-50% of the total power consumption of the data center. This is because the servers and storage devices inside the data center will generate a lot of heat. In order to ensure that these devices can work in a suitable temperature environment, the cooling equipment needs to be continuously operated to maintain a stable temperature.

[0005] Therefore, how to intelligently balance the power consumption of refrigeration equipment and electrical equipment and keep the temperature of the data center within an appropriate range is an important issue facing existing data centers. It can be seen that in terms of reducing data center energy consumption and improving energy utilization efficiency, existing technologies still need to be improved and enhanced. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide a data center power consumption control method, system, terminal equipment and medium in view of the above-mentioned defects of the prior art, aiming to solve the problems of excessive energy consumption and low energy utilization efficiency in data centers.

[0007] In order to solve the above technical problems, the technical solution adopted by the present invention is as follows:

[0008] In a first aspect, the present invention provides a method for controlling power consumption in a data center, the method comprising:

[0009] Acquire site information of a data center, and determine association relationships between various devices in the data center based on the site information;

[0010] Obtaining device power consumption data of each device in the data center, and determining overall power consumption data of the data center based on the device power consumption data;

[0011] Acquire device temperature data of each device in the data center, and determine overall temperature data of the data center based on the device temperature data;

[0012] Building a digital twin model based on the association relationship, the device power consumption data and the device temperature data, and the overall power consumption data and the overall temperature data;

[0013] Based on the digital twin model, the changes in the power consumption data and the temperature data of the device are simulated to obtain the change information of the overall power consumption and the overall temperature data, and the power consumption of the device is controlled based on the change information.

[0014] In one implementation, determining the association relationship between the devices in the data center based on the site information includes:

[0015] Based on the site information of the data center, obtaining the equipment location relationship and the circuit layout of the data center;

[0016] Based on the device position relationship, determining the physical distance between the devices;

[0017] Based on the data center circuit layout, determine the electrical connection relationship between the devices;

[0018] Based on the physical distance and the electrical connection relationship, an association relationship between various devices in the data center is determined.

[0019] In one implementation, the acquiring of the device power usage data of each device in the data center and determining the overall power usage data of the data center based on the device power usage data includes:

[0020] Deploy several physical sensors, each of which corresponds to a device;

[0021] Based on a number of physical sensors, the power consumption of each device in a preset time period is collected to obtain the device power consumption data corresponding to each device;

[0022] Based on the device power consumption data corresponding to each device, the overall power consumption data of the data center is calculated.

[0023] In one implementation, acquiring device temperature data of each device in the data center and determining overall temperature data of the data center based on the device temperature data includes:

[0024] Deploy several temperature sensors, each corresponding to one device;

[0025] Based on a plurality of temperature sensors, the temperature of each device within a preset time period is collected to obtain device temperature data corresponding to each device;

[0026] Based on the temperature data corresponding to each device, the overall temperature data of the data center is calculated.

[0027] In one implementation, the constructing of the digital twin model based on the association relationship, the device power consumption data and the device temperature data, and the overall power consumption data and the overall temperature data includes:

[0028] Based on the association relationship, determining an impact factor, wherein the impact factor is used to reflect the degree of impact of different association relationships on the device power consumption data or the device temperature data;

[0029] Classifying the equipment to obtain equipment category information, wherein the equipment category information includes refrigeration equipment and electrical equipment, the equipment power consumption data of the refrigeration equipment is negatively correlated with the equipment temperature data, and the equipment power consumption data of the electrical equipment is positively correlated with the equipment temperature data;

[0030] Using the influencing factors and the equipment category information, and taking the equipment power consumption data and the equipment temperature data, the overall power consumption data and the overall temperature data as variables, the digital twin model is trained with a data fitting algorithm to obtain the trained digital twin model.

[0031] In one implementation, simulating changes in the power consumption data and the temperature data of the device based on the digital twin model to obtain change information of the overall power consumption and the overall temperature data, and controlling power consumption of the device based on the change information includes:

[0032] Taking the current association relationship, the device power consumption data and the device temperature data, and the overall power consumption data and the overall temperature data as reference data, any device is taken as a controlled device, and simulated power control is performed on the controlled device to obtain change information of the device power consumption data and the device temperature data of the controlled device;

[0033] Input the change information of the device power consumption data and the device temperature data of the controlled device into the trained data twin model, determine the change information of the device power consumption data and the device temperature data of the remaining devices, and determine the change information of the overall power consumption data and the overall temperature data of the data center;

[0034] Based on the change information of the overall power consumption data of the data center and the overall temperature data, determining whether the overall temperature data is within a preset safety range, and determining whether the overall power consumption data is lower than the benchmark data;

[0035] If the overall temperature data is within the safety range and the overall power consumption data is lower than the reference data, the same power consumption control is performed on the data center based on the simulated power consumption control.

[0036] In one implementation, the method further includes:

[0037] Monitor the device power consumption data and device temperature data of each device in the data center;

[0038] When the device power consumption data or the device temperature data changes, and the change exceeds a preset threshold, the changed association relationship, the device power consumption data and the device temperature data, the overall power consumption data and the overall temperature data are used as reference data, and based on the digital twin model, simulated control of power consumption is performed;

[0039] Based on the simulated power consumption result, power consumption of the device is controlled.

[0040] In a second aspect, an embodiment of the present invention further provides a data center power control system, the system comprising:

[0041] An association relationship acquisition module, used to acquire site information of a data center, and determine the association relationship between various devices in the data center based on the site information;

[0042] A power consumption data acquisition module, used to acquire the device power consumption data of each device in the data center, and determine the overall power consumption data of the data center based on the device power consumption data;

[0043] A temperature data acquisition module is used to acquire the device temperature data of each device in the data center, and determine the overall temperature data of the data center based on the device temperature data;

[0044] A digital twin model construction module, used to construct a digital twin model based on the association relationship, the device power consumption data and the device temperature data, and the overall power consumption data and the overall temperature data;

[0045] The power consumption control module is used to simulate the changes in the power consumption data and the temperature data of the device based on the digital twin model, obtain the change information of the overall power consumption and the overall temperature data, and control the power consumption of the device based on the change information.

[0046] In a third aspect, an embodiment of the present invention further provides a terminal device, comprising a memory, a processor, and a data center power consumption control program stored in the memory and executable on the processor, wherein when the processor executes the data center power consumption control program, the steps of the data center power consumption control method of any one of the above-mentioned schemes are implemented.

[0047] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a data center power consumption regulation program is stored. When the data center power consumption regulation program is executed by a processor, the steps of the data center power consumption regulation method described in any one of the above-mentioned schemes are implemented.

[0048] Beneficial effects: The present invention provides a method for controlling power consumption in a data center. Compared with the prior art, the method first obtains the site information of the data center, and determines the association between the various devices in the data center based on the site information; obtains the device power consumption data of each device in the data center, and determines the overall power consumption data of the data center based on the device power consumption data; obtains the device temperature data of each device in the data center, and determines the overall temperature data of the data center based on the device temperature data. Then, a digital twin model is constructed based on the association, the device power consumption data and the device temperature data, and the overall power consumption data and the overall temperature data. Finally, based on the digital twin model, the changes of the device power consumption data and the device temperature data are simulated to obtain the change information of the overall power consumption and the overall temperature data, and the power consumption of the device is controlled based on the change information. The present invention constructs a digital twin model that can reflect the real operating status and interaction mechanism of the power consumption and temperature in the data center from a single device to the overall system. By actively simulating power consumption control, the optimal power consumption control scheme is selected to optimize the power consumption of the data center, solve the problems of excessive energy consumption and low energy utilization efficiency in the data center, and help the data center to operate green, efficiently and sustainably. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A flowchart of a specific implementation of the data center power control method provided in an embodiment of the present invention.

[0050] Figure 2 It is a principle block diagram of a data center power control device provided by an embodiment of the present invention.

[0051] Figure 3 It is a block diagram of the internal structure principle of the intelligent terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solution and effect of the present invention clearer and more specific, the present invention is further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0053] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations or steps, nor must they be executed in the order described. For example, some operations or steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0054] It should be understood that the terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0055] It should be understood that, in order to clearly describe the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, words such as "first" and "second" are used to distinguish between identical or similar items with substantially identical functions and effects. For example, the first control information and the second control information are only used to distinguish different control information, and their order is not limited.

[0056] Those skilled in the art can understand that the words "first", "second", etc. do not limit the quantity and execution order, and the words "first", "second", etc. do not necessarily limit the differences.

[0057] It should be further understood that the term “and / or” used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0058] In the prior art, the method for controlling the number of registered devices is usually based on fixed rules or strategies. Too many or too few devices will affect the operating efficiency and stability of the system, and it is impossible to flexibly respond to changes in the number of devices. In addition, the control rules for the number of registered devices are not intelligent and flexible enough, and cannot be adjusted in time according to actual conditions. When the system capacity is insufficient or there are too many device connection requests, it may cause system performance degradation or service interruption.

[0059] In order to solve the problems of the prior art, the present invention provides a method for regulating and controlling power consumption in a data center. By constructing a digital twin model that can reflect the actual operating status and interaction mechanism of power consumption and temperature from a single device to the overall system in the data center, and actively simulating power consumption control, the optimal power consumption control scheme is selected, and the power consumption of the data center is optimized, ultimately solving the problems of excessive energy consumption and low energy utilization efficiency in the data center.

[0060] This embodiment provides a data center power control method, such as Figure 1 As shown, the specific steps include:

[0061] Step S100: Acquire site information of a data center, and determine association relationships between various devices in the data center based on the site information.

[0062] In one implementation, determining the association relationship between the devices in the data center based on the site information specifically includes the following steps:

[0063] Step S110: based on the site information of the data center, obtaining the equipment location relationship and the circuit layout of the data center;

[0064] Step S120: determining the physical distance between the devices based on the device position relationship;

[0065] Step S130: determining the electrical connection relationship between the devices based on the circuit layout of the data center;

[0066] Step S140: Determine the association relationship between the various devices in the data center based on the physical distance and the electrical connection relationship.

[0067] In this embodiment, the site information is the objective physical conditions of the data center, specifically including the building structure of the data center, the number of equipment, the type of equipment, the location of the equipment, the circuit connection relationship of the equipment, the network connection relationship of the equipment, and other information. The site information can be collected by collecting the architectural design blueprint of the data center, the relevant information and the specific location of the equipment in the field survey, and the direction and connection point location of the electrical line. The above-mentioned multiple site information is uniformly summarized and recorded and stored in a structured manner. Based on the above-mentioned site information, the equipment position relationship is sorted out and the equipment position relationship is expressed in the form of a three-dimensional space structure diagram. On this basis, the physical distance between the computing device and the device is further sorted out and marked in the above-mentioned three-dimensional space structure diagram. Similarly, the circuit connection relationship between the devices in the site information is also marked in the above-mentioned structure diagram. In this way, the technical solution sorts out a data center simulation three-dimensional space structure diagram containing equipment, physical distance of equipment, and electrical connection relationship of equipment. The structure diagram expresses the association relationship of all equipment in the data center. The above-mentioned simulation space architecture is the basic framework of the subsequent digital twin model of the technical solution, and the above-mentioned equipment association relationship is used for the calculation of the subsequent digital twin model.

[0068] Step S200: Obtain the device power consumption data of each device in the data center, and determine the overall power consumption data of the data center based on the device power consumption data.

[0069] In one implementation, the step of acquiring the device power consumption data of each device in the data center and determining the overall power consumption data of the data center based on the device power consumption data specifically includes the following steps:

[0070] Step S210: deploy a number of physical sensors, each of which corresponds to a device;

[0071] Step S220: Based on a plurality of physical sensors, the power consumption of each device in a preset time period is collected to obtain the device power consumption data corresponding to each device;

[0072] Step S230: Based on the device power consumption data corresponding to each device, the overall power consumption data of the data center is calculated.

[0073] In this embodiment, for each device, a physical sensor compatible with the device is deployed to monitor the power consumption of the device. Specifically, it is necessary to select a power consumption detection sensor with a suitable range and a suitable power range according to the type of device. After deployment, the average power consumption of each device in a preset time period is monitored in real time. This technical solution is aimed at the power consumption optimization needs of the data center. Therefore, the real-time requirements for power consumption monitoring are not high. Generally, minute-level sampling is adopted, that is, sampling is performed once per minute to record the power consumption data of the minute. By summarizing the power consumption data of all devices in the data center, the overall power consumption data of the data center can be obtained. In another way, it is also possible to directly obtain the overall power consumption data of the data center by deploying additional physical sensors to detect the total power inlet and outlet of the data center.

[0074] Step S300: Acquire device temperature data of each device in the data center, and determine overall temperature data of the data center based on the device temperature data.

[0075] In one implementation, the step of acquiring the device temperature data of each device in the data center and determining the overall temperature data of the data center based on the device temperature data specifically includes the following steps:

[0076] Step S310: deploy a number of temperature sensors, each temperature sensor corresponding to a device;

[0077] Step S320: Based on a plurality of temperature sensors, the temperature of each device in a preset time period is collected to obtain device temperature data corresponding to each device;

[0078] Step S330: Based on the temperature data corresponding to each device, calculate the overall temperature data of the data center.

[0079] In this embodiment, for each device, a temperature sensor is deployed inside or outside each device to record the temperature data of the device. Among them, if the temperature sensor is deployed inside the device, the temperature of the core components of the device recorded is more accurate, and it can be more accurately monitored whether the device operation is overloaded, making the device operation safer. If the temperature sensor is deployed outside the device, the recorded device temperature can better reflect the heat propagation effect between the devices, avoiding the problem of inaccurate correlation caused by the device shell blocking the heat dissipation when the correlation between the device association relationship and the device temperature propagation is explored later. Therefore, in the present invention, the deployment location of the temperature sensor can be determined based on the actual considerations of the data center. The frequency of temperature acquisition needs to be consistent with the frequency of electricity data acquisition, and the electricity data is used to correspond to the temperature data. Further, if the temperature sensor is deployed outside the device, the overall temperature data of the data center can be calculated directly based on the temperature data recorded by the temperature sensor of the device. If the temperature sensor is deployed inside the device, it is necessary to deploy several additional temperature sensors in the internal space of the data center to record the overall temperature data of the data center.

[0080] Step S400: construct a digital twin model based on the association relationship, the device power consumption data and the device temperature data, and the overall power consumption data and the overall temperature data.

[0081] In one implementation, the digital twin model is constructed based on the association relationship, the device power consumption data and the device temperature data, and the overall power consumption data and the overall temperature data, and specifically includes the following steps:

[0082] Step S410: determining an impact factor based on the association relationship, wherein the impact factor is used to reflect the degree of impact of different association relationships on the device power consumption data or the device temperature data;

[0083] Step S420: classify the equipment to obtain equipment category information, wherein the equipment category information includes refrigeration equipment and electrical equipment, the equipment power consumption data of the refrigeration equipment is negatively correlated with the equipment temperature data, and the equipment power consumption data of the electrical equipment is positively correlated with the equipment temperature data;

[0084] Step S430: Use the influencing factors and the equipment category information, and use the equipment power consumption data and the equipment temperature data, the overall power consumption data and the overall temperature data as variables to perform data fitting algorithm training on the digital twin model to obtain the trained digital twin model.

[0085] In this embodiment, the equipment is classified based on functionality to obtain equipment category information. The equipment category information marks the type of equipment, specifically including refrigeration equipment and electrical equipment. Among them, the equipment power consumption data of the refrigeration equipment is negatively correlated with the equipment temperature data, that is, the more power the refrigeration equipment uses, the more heat in the data center is transferred out, and the more the equipment temperature drops; the equipment power consumption data of the electrical equipment is positively correlated with the equipment temperature data, that is, the more power the electrical equipment uses, the more heat is generated, and the more the temperature rises. The influencing factors determined by the association relationship include the influence of physical distance on equipment temperature data and the influence of electrical connection relationship on power consumption data. Among them, physical distance is positively correlated with the temperature data of electrical equipment. For example, electrical equipment located in the same cabinet are close to each other, with limited heat dissipation space, and the heat transfer between them is significantly affected. If a high-power electrical equipment continues to heat up, the temperature of the surrounding equipment will rise accordingly. Similarly, heat transfer between cabinets is also affected by physical distance. For refrigeration equipment, the distance between it and the electrical equipment will also affect the effect of heat transfer, and further affect the temperature change effect of the electrical equipment. That is, the closer the physical distance between the refrigeration equipment and the electrical equipment, the higher the efficiency of heat transfer and the better the cooling effect.

[0086] After determining the influencing factors and the equipment category information, a multi-layer perceptron neural network model is constructed, including an input layer, a hidden layer, and an output layer. The equipment power consumption data, temperature data, influencing factors, and equipment category information are encoded as input layer nodes of the neural network. Several hidden layers are set in the middle. Specifically, for the above-mentioned complex data center system, the equipment power consumption data and temperature data are affected by the above-mentioned influencing factors and equipment category information, and there is a complex nonlinear and dynamic relationship between the above-mentioned factors. Therefore, at least three layers of hidden layers need to be set to achieve a suitable effect. The output layer includes two parts, one part of the neurons is used to output values ​​related to power consumption information, specifically including equipment power consumption data and overall power consumption data; the other part is used to output values ​​related to temperature information, specifically including equipment temperature data and overall temperature data. The historical data sets of the influencing factors, the equipment category information, the equipment power consumption data and the equipment temperature data, the overall power consumption data and the overall temperature data are divided into a training set and a test set, and the neural network model is trained. The loss function of the training can select a mean square error loss function or a mean absolute error loss function based on the discrete characteristics of temperature. The trained neural network model, combined with the basic framework of the digital twin model, constitutes the digital twin model of the present invention.

[0087] Step S500: Based on the digital twin model, simulate the changes in the power consumption data and the temperature data of the device to obtain the change information of the overall power consumption and the overall temperature data, and control the power consumption of the device based on the change information.

[0088] In one implementation, based on the digital twin model, simulating the changes in the power consumption data and the temperature data of the device, obtaining the change information of the overall power consumption and the overall temperature data, and controlling the power consumption of the device based on the change information specifically includes the following steps:

[0089] Step S510: taking the current association relationship, the device power consumption data and the device temperature data, and the overall power consumption data and the overall temperature data as reference data, taking any device as a controlled device, and performing simulated power control on the controlled device to obtain change information of the device power consumption data and the device temperature data of the controlled device;

[0090] Step S520: Input the change information of the device power consumption data and the device temperature data of the controlled device into the trained data twin model, determine the change information of the device power consumption data and the device temperature data of the remaining devices, and determine the change information of the overall power consumption data and the overall temperature data of the data center;

[0091] Step S530: Based on the change information of the overall power consumption data and the overall temperature data of the data center, determine whether the overall temperature data is within a preset safety range, and determine whether the overall power consumption data is lower than the benchmark data;

[0092] Step S540: If the overall temperature data is within the safety range and the overall power consumption data is lower than the benchmark data, the same power consumption control is performed on the data center based on the simulated power consumption control.

[0093] In this embodiment, after obtaining the digital twin model, the present invention simulates the control of power consumption of the data center. The simulated control of power consumption step specifically includes, first, storing the association relationship at the current moment, the device power consumption data and the device temperature data, the overall power consumption data and the overall temperature data as reference data. Next, select a device as the controlled device. The method of selecting the controlled device can be random selection, or sequential selection based on a manually set priority, or sequential selection based on the device-related information. The controlled device can be a refrigeration device or an electrical device. On the premise of satisfying the computing service function, the power consumption of the controlled device is simulated and adjusted. The changed power consumption data is input into the trained data twin model, and the result is output. The result includes the power consumption data and temperature data of all devices, and the overall power consumption data and overall temperature data of the data center. The above output results are compared with the above benchmark data. If the overall temperature data is within the safety range and the overall power consumption data is lower than the benchmark data, it is determined that the simulation has the effect of optimizing power consumption. The simulation adjustment is applied to the actual data center to complete a power consumption optimization regulation, so that the overall energy consumption of the data center is lower than before the regulation.

[0094] In one implementation, the method further includes the following steps:

[0095] Step S610: monitoring the device power consumption data and device temperature data of each device in the data center;

[0096] Step S620: when the device power consumption data or the device temperature data changes and the change exceeds a preset threshold, the changed association relationship, the device power consumption data and the device temperature data, the overall power consumption data and the overall temperature data are used as reference data, and based on the digital twin model, simulate and control power consumption;

[0097] Step S630: Based on the simulated power consumption result, control the power consumption of the device.

[0098] In this embodiment, the device power consumption data and device temperature data of all devices in the data center are monitored in real time through the above-mentioned physical sensors and temperature sensors. According to the characteristics of different devices in the data center, the corresponding thresholds of device power consumption data and device temperature data changes are manually set. If the monitored change exceeds the threshold range, the simulated control of power consumption is triggered, which is used to achieve a new energy consumption optimal power consumption plan through regulation after the device power consumption data and device temperature data change. Among them, the reasons for the change of the device power consumption data and device temperature data include the above-mentioned optimized power consumption control, the change of power consumption caused by the change of equipment load, and the change of data center temperature caused by the change of the weather temperature of the external environment of the data center building. When the simulated control of power consumption is triggered, the association relationship after the change, the device power consumption data and the device temperature data, the overall power consumption data and the overall temperature data are used as the reference data, and the controlled device is selected based on the digital twin model and its power consumption is simulated and adjusted. Compare the relevant data after the simulated adjustment with the reference data. If the performance is better after the simulated power control, the same power control is performed on the data center. If there is no better power consumption control solution after simulating power consumption control of all devices in the data center, it means that the current energy consumption state of the data center is optimal. Further, based on the relevant data after the change, the digital twin model continues to be learned online, for example, by using a small batch gradient descent method to update the model, so as to continuously improve the accuracy and adaptability of the digital twin model.

[0099] In summary, under the technical solution of the above embodiment, a digital twin model is constructed that can reflect the actual operating status and interaction mechanism of power consumption and temperature from a single device to the overall system in the data center. By actively simulating power consumption control, the optimal power consumption control scheme is selected to optimize the power consumption of the data center.

[0100] like Figure 2 As shown in the figure, an embodiment of the present invention provides a data center power consumption control system, which includes: an association relationship acquisition module 10, a power consumption data acquisition module 20, a temperature data acquisition module 30, a digital twin model construction module 40, and a power consumption control module 50.

[0101] Specifically, the association relationship acquisition module 10 is used to acquire the site information of the data center, and based on the site information, determine the association relationship between each device in the data center; the power consumption data acquisition module 20 is used to acquire the equipment power consumption data of each device in the data center, and based on the equipment power consumption data, determine the overall power consumption data of the data center; the temperature data acquisition module 30 is used to acquire the equipment temperature data of each device in the data center, and based on the equipment temperature data, determine the overall temperature data of the data center; the digital twin model construction module 40 is used to construct a digital twin model based on the association relationship, the equipment power consumption data and the equipment temperature data, and the overall power consumption data and the overall temperature data; the power consumption control module 50 is used to simulate the changes of the equipment power consumption data and the equipment temperature data based on the digital twin model, obtain the change information of the overall power consumption and the overall temperature data, and control the power consumption of the equipment based on the change information.

[0102] In one implementation, the association relationship acquisition module 10 includes:

[0103] A position relationship and circuit layout acquisition unit, used to acquire the device position relationship and the data center circuit layout based on the site information of the data center;

[0104] A physical distance acquisition unit, used to determine the physical distance between the devices based on the device position relationship;

[0105] An electrical connection relationship acquisition unit, used to determine the electrical connection relationship between the devices based on the circuit layout of the data center;

[0106] An association relationship determining unit is used to determine the association relationship between various devices in the data center based on the physical distance and the electrical connection relationship.

[0107] In one implementation, the power consumption data acquisition module 20 includes:

[0108] A physical sensor deployment unit, used to deploy a number of physical sensors, each of which corresponds to a device;

[0109] The power consumption data collection unit is used to collect the power consumption of each device in a preset time period based on a plurality of physical sensors to obtain the device power consumption data corresponding to each device;

[0110] The overall power consumption data calculation unit is used to calculate the overall power consumption data of the data center based on the device power consumption data corresponding to each device.

[0111] In one implementation, the temperature data acquisition module 30 includes:

[0112] A temperature sensor deployment unit, used to deploy a number of temperature sensors, each temperature sensor corresponding to a device;

[0113] A temperature data acquisition unit, used to acquire the temperature of each device within a preset time period based on a plurality of temperature sensors, and obtain device temperature data corresponding to each device;

[0114] The overall temperature data calculation unit is used to calculate the overall temperature data of the data center based on the temperature data corresponding to each device.

[0115] In one implementation, the digital twin model building module 40 includes:

[0116] An impact factor acquisition unit, used to determine an impact factor based on the association relationship, wherein the impact factor is used to reflect the degree of influence of different association relationships on the device power consumption data or the device temperature data;

[0117] an equipment category information acquisition unit, configured to classify the equipment to obtain equipment category information, wherein the equipment category information includes refrigeration equipment and electrical equipment, wherein the equipment power consumption data of the refrigeration equipment is negatively correlated with the equipment temperature data, and the equipment power consumption data of the electrical equipment is positively correlated with the equipment temperature data;

[0118] The digital twin model training unit is used to use the influencing factors and the equipment category information, and take the equipment power consumption data and the equipment temperature data, the overall power consumption data and the overall temperature data as variables to perform data fitting algorithm training on the digital twin model to obtain the trained digital twin model.

[0119] In one implementation, the power consumption control module 50 includes:

[0120] The power consumption simulation control unit is used to use the current association relationship, the power consumption data of the device and the temperature data of the device, and the overall power consumption data and the overall temperature data as reference data, take any device as a controlled device, and simulate the power consumption control of the controlled device to obtain the change information of the power consumption data and the temperature data of the controlled device;

[0121] A change information determination unit, used to input the change information of the device power consumption data and the device temperature data of the controlled device into the trained data twin model, determine the change information of the device power consumption data and the device temperature data of the remaining devices, and determine the change information of the overall power consumption data and the overall temperature data of the data center;

[0122] a change information judgment unit, configured to judge whether the overall temperature data is within a preset safety range and whether the overall power consumption data is lower than the benchmark data based on the change information of the overall power consumption data and the overall temperature data of the data center;

[0123] The power consumption control unit is used to perform the same power consumption control on the data center based on the simulated power consumption control if the overall temperature data is within the safety range and the overall power consumption data is lower than the reference data.

[0124] In one implementation, the system further includes:

[0125] A device monitoring unit, used to monitor device power consumption data and device temperature data of each device in the data center;

[0126] A change state simulation power consumption unit is used to simulate and control power consumption based on the digital twin model, taking the association relationship after the change, the device power consumption data and the device temperature data, the overall power consumption data and the overall temperature data as reference data when the device power consumption data or the device temperature data changes and the change exceeds a preset threshold;

[0127] The variable state power consumption control unit is used to control the power consumption of the device based on the simulated power consumption result.

[0128] Based on the above embodiments, the present invention further provides an intelligent terminal, whose principle block diagram can be shown as follows: Figure 3 As shown. The intelligent terminal includes a processor, a memory, a network interface, a display screen, and a temperature sensor connected through a system bus. Among them, the processor of the intelligent terminal is used to provide computing and control capabilities. The memory of the intelligent terminal includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the intelligent terminal is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for optimizing display quality is implemented. The display screen of the intelligent terminal can be a liquid crystal display screen or an electronic ink display screen, and the temperature sensor of the intelligent terminal is pre-set inside the intelligent terminal to detect the operating temperature of the internal device.

[0129] Those skilled in the art will understand that Figure 3 The principle block diagram shown in the figure is only a block diagram of a partial structure related to the scheme of the present invention, and does not constitute a limitation on the smart terminal to which the scheme of the present invention is applied. The specific smart terminal may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0130] In one embodiment, a smart terminal is provided, comprising a memory and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by one or more processors, and the one or more programs include instructions for performing the following operations:

[0131] Acquire site information of a data center, and determine association relationships between various devices in the data center based on the site information;

[0132] Obtaining device power consumption data of each device in the data center, and determining overall power consumption data of the data center based on the device power consumption data;

[0133] Acquire device temperature data of each device in the data center, and determine overall temperature data of the data center based on the device temperature data;

[0134] Building a digital twin model based on the association relationship, the device power consumption data and the device temperature data, and the overall power consumption data and the overall temperature data;

[0135] Based on the digital twin model, the changes in the power consumption data and the temperature data of the device are simulated to obtain the change information of the overall power consumption and the overall temperature data, and the power consumption of the device is controlled based on the change information.

[0136] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing related hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0137] In summary, the present invention discloses that the present invention provides a method, system, terminal device and medium for power consumption control in a data center, and the method includes: obtaining site information of the data center, and determining the association relationship between each device in the data center based on the site information; obtaining equipment power consumption data of each device in the data center, and determining the overall power consumption data of the data center based on the equipment power consumption data; obtaining equipment temperature data of each device in the data center, and determining the overall temperature data of the data center based on the equipment temperature data; constructing a digital twin model based on the association relationship, the equipment power consumption data and the equipment temperature data, and the overall power consumption data and the overall temperature data; based on the digital twin model, simulating the changes in the equipment power consumption data and the equipment temperature data, obtaining the change information of the overall power consumption and the overall temperature data, and controlling the power consumption of the equipment based on the change information. The present invention constructs a digital twin model that can reflect the actual operating status and interaction mechanism of electricity consumption and temperature in the data center from a single device to the overall system. By actively simulating power consumption control and selecting the optimal power control solution, the power consumption of the data center is optimized, and the problems of excessive energy consumption and low energy utilization efficiency in the data center are solved, thereby helping the data center to operate in a green, efficient and sustainable manner.

[0138] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0139] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for regulating power consumption in a data center, characterized in that: The method comprises: Acquire site information of a data center, and determine association relationships between various devices in the data center based on the site information; Obtaining device power consumption data of each device in the data center, and determining overall power consumption data of the data center based on the device power consumption data; Acquire device temperature data of each device in the data center, and determine overall temperature data of the data center based on the device temperature data; Building a digital twin model based on the association relationship, the device power consumption data and the device temperature data, and the overall power consumption data and the overall temperature data; Based on the digital twin model, the changes in the power consumption data and the temperature data of the device are simulated to obtain the change information of the overall power consumption and the overall temperature data, and the power consumption of the device is controlled based on the change information.

2. The data center power control method according to claim 1, characterized in that: The determining, based on the site information, the association relationship between the various devices in the data center includes: Based on the site information of the data center, obtaining the equipment location relationship and the circuit layout of the data center; Based on the device position relationship, determining the physical distance between the devices; Based on the data center circuit layout, determine the electrical connection relationship between the devices; Based on the physical distance and the electrical connection relationship, an association relationship between various devices in the data center is determined.

3. The data center power control method according to claim 1, characterized in that: The obtaining of the device power consumption data of each device in the data center and determining the overall power consumption data of the data center based on the device power consumption data includes: Deploy several physical sensors, each of which corresponds to a device; Based on a number of physical sensors, the power consumption of each device in a preset time period is collected to obtain the device power consumption data corresponding to each device; Based on the device power consumption data corresponding to each device, the overall power consumption data of the data center is calculated.

4. The data center power control method according to claim 1, characterized in that: The acquiring device temperature data of each device in the data center and determining the overall temperature data of the data center based on the device temperature data includes: Deploy several temperature sensors, each corresponding to one device; Based on a plurality of temperature sensors, the temperature of each device within a preset time period is collected to obtain device temperature data corresponding to each device; Based on the temperature data corresponding to each device, the overall temperature data of the data center is calculated.

5. The data center power control method according to claim 2, characterized in that: The constructing of a digital twin model based on the association relationship, the device power consumption data and the device temperature data, and the overall power consumption data and the overall temperature data includes: Based on the association relationship, determining an impact factor, wherein the impact factor is used to reflect the degree of influence of different association relationships on the device power consumption data or the device temperature data; Classifying the equipment to obtain equipment category information, wherein the equipment category information includes refrigeration equipment and electrical equipment, the equipment power consumption data of the refrigeration equipment is negatively correlated with the equipment temperature data, and the equipment power consumption data of the electrical equipment is positively correlated with the equipment temperature data; Using the influencing factors and the equipment category information, and taking the equipment power consumption data and the equipment temperature data, the overall power consumption data and the overall temperature data as variables, the digital twin model is trained with a data fitting algorithm to obtain the trained digital twin model.

6. The data center power control method according to claim 5, characterized in that: The method of simulating changes in the power consumption data and the temperature data of the device based on the digital twin model to obtain change information of the overall power consumption and the overall temperature data, and controlling the power consumption of the device based on the change information includes: Taking the current association relationship, the device power consumption data and the device temperature data, and the overall power consumption data and the overall temperature data as reference data, any device is taken as a controlled device, and simulated power control is performed on the controlled device to obtain change information of the device power consumption data and the device temperature data of the controlled device; Input the change information of the device power consumption data and the device temperature data of the controlled device into the trained data twin model, determine the change information of the device power consumption data and the device temperature data of the remaining devices, and determine the change information of the overall power consumption data and the overall temperature data of the data center; Based on the change information of the overall power consumption data of the data center and the overall temperature data, determining whether the overall temperature data is within a preset safety range, and determining whether the overall power consumption data is lower than the benchmark data; If the overall temperature data is within the safety range and the overall power consumption data is lower than the reference data, the same power consumption control is performed on the data center based on the simulated power consumption control.

7. The data center power control method according to claim 6, characterized in that: The method further comprises: Monitor the device power consumption data and device temperature data of each device in the data center; When the device power consumption data or the device temperature data changes, and the change exceeds a preset threshold, the changed association relationship, the device power consumption data and the device temperature data, the overall power consumption data and the overall temperature data are used as reference data, and based on the digital twin model, simulated control of power consumption is performed; Based on the simulated power consumption result, power consumption of the device is controlled.

8. A data center power control system, characterized in that: The system comprises: An association relationship acquisition module, used to acquire site information of a data center, and determine the association relationship between various devices in the data center based on the site information; A power consumption data acquisition module, used to acquire the device power consumption data of each device in the data center, and determine the overall power consumption data of the data center based on the device power consumption data; A temperature data acquisition module is used to acquire the device temperature data of each device in the data center, and determine the overall temperature data of the data center based on the device temperature data; A digital twin model construction module, used to construct a digital twin model based on the association relationship, the device power consumption data and the device temperature data, and the overall power consumption data and the overall temperature data; The power consumption control module is used to simulate the changes in the power consumption data and the temperature data of the device based on the digital twin model, obtain the change information of the overall power consumption and the overall temperature data, and control the power consumption of the device based on the change information.

9. A terminal device, characterized in that: The terminal device includes a memory, a processor, and a data center power consumption control program stored in the memory and executable on the processor. When the processor executes the data center power consumption control program, the steps of the data center power consumption control method as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a data center power consumption regulation program, and when the data center power consumption regulation program is executed by the processor, the steps of the data center power consumption regulation method according to any one of claims 1 to 7 are implemented.

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