Calculation power and electric power cooperative control method, device and equipment and storage medium

Abstract

The embodiment of the invention provides a computing power and electric power cooperative control method and device, equipment and a storage medium. The method comprises the steps of obtaining current operation data of a data center, current computing resource use conditions of the data center and current power supply information of a power grid system; inputting the operation data into a prediction model to predict an operation demand and a power demand of the data center in a preset time; inputting the operation demand, the power demand, the resource use condition and the power supply information into a neural network model to determine a cooperative scheduling scheme of the power grid system and the data center at the preset time; and when the preset time is reached, the computing resources of the data center and the power supply of the power grid system are adjusted based on the collaborative scheduling method, so that a collaborative scheduling scheme of the power grid system and the data center can be formulated in advance, and the optimization effect is improved.

Description

Technical Field

[0001] The present application belongs to the field of data center technology, and in particular, relates to a computing power and power coordinated control method, device, equipment and storage medium. Background Art

[0002] As a core facility for processing and storing massive amounts of data, data centers have an increasing demand for computing power. Data centers need power systems to power them so that their equipment can operate. The stability of data center operations, computing efficiency, and energy consumption are hot topics of concern in this field. In related technologies, when it is determined that the power system or computing tasks have changed, the power supply of the power supply system or the computing power of the data center is adjusted to achieve stable operation of the data center. There is a correlation between the computing resources and power supply of the data center. In related technologies, since only the power supply of the power system or only the computing resources of the data center are adjusted, there is a problem of poor optimization effect, and the adjustment in the existing technology has a lag. Summary of the invention

[0003] In response to the above problems, the embodiments of the present application provide a computing power and power collaborative control method, device, equipment and storage medium, which can formulate a collaborative scheduling plan for the power grid system and the data center in advance to improve the optimization effect.

[0004] The present application provides a method for coordinated control of computing power and electricity, including:

[0005] Obtaining current operating data of the data center, current computing resource usage of the data center, and current power supply information of the power grid system;

[0006] Inputting the operation data into a prediction model to predict the computing demand and power demand of the data center at a preset time;

[0007] Inputting the computing demand, the power demand, the resource usage, and the power supply information into a neural network model to determine a coordinated scheduling scheme for the power grid system and the data center at the preset time;

[0008] When the preset time is reached, the computing resources of the data center and the power supply of the power grid system are adjusted based on the collaborative scheduling method.

[0009] In some embodiments, the method further comprises:

[0010] Obtaining operation data of each computing node in the data center;

[0011] Determine whether each computing node has a fault based on the operation data of each computing node;

[0012] In the case that a first target computing node has a fault, a computing task of the first target computing node is allocated to a second target computing node, wherein the second target computing node has no fault.

[0013] In some embodiments, the power supply method includes: multiple power supply methods, adjusting the power supply of the power grid system, including:

[0014] Switch the power supply mode, or increase the power supply mode, or reduce the power supply mode.

[0015] In some embodiments, the method further comprises:

[0016] In the case of obtaining request information from the power grid system for requesting the data center to reduce power demand, determining whether to reduce the power demand of the data center;

[0017] When it is determined that the power demand of the data center is reduced, feedback information is given to the power grid system so that the power grid system calculates a contribution value based on the reduced power resources and allocates the contribution value to a power account corresponding to the data center.

[0018] In some embodiments, the method further comprises:

[0019] Obtain information on electricity price changes in the power grid system;

[0020] The operation strategy of the data center is adjusted based on the electricity price change information to reduce electricity price costs.

[0021] In some embodiments, the method further comprises:

[0022] The computing resources of the data center are dynamically configured based on the operating data.

[0023] In some embodiments, the method further comprises:

[0024] When an abnormality is detected in the power grid system, switch to emergency power supply.

[0025] The present application provides a computing power and power coordinated control device, including:

[0026] An acquisition module is used to acquire the current operation data of the data center, the current computing resource usage of the data center, and the current power supply information of the power grid system;

[0027] A prediction module, used for inputting the operation data into a prediction model to predict the computing demand and power demand of the data center at a preset time;

[0028] A determination module, configured to input the computing demand, the power demand, the resource usage, and the power supply information into a neural network model to determine a coordinated scheduling scheme of the power grid system and the data center at the preset time;

[0029] A control module is used to adjust the computing resources of the data center and the power supply of the power grid system based on the collaborative scheduling method when the preset time is reached.

[0030] An embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any of the above-described methods when executing the computer program.

[0031] An embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, any of the above-mentioned methods is implemented.

[0032] An embodiment of the present application provides a computer program product. When the computer program product is executed on a terminal device, the electronic device executes any one of the above methods.

[0033] The embodiments of the present application provide a method, apparatus, device and storage medium for coordinated control of computing power and electricity, which obtains the current operating data of a data center, the current computing resource usage of the data center and the current power supply information of a power grid system; inputs the operating data into a prediction model to predict the computing demand and power demand of the data center at a preset time; based on the computing demand, the power demand, the resource usage and the power supply information, the neural network model is input to determine a coordinated scheduling scheme for the power grid system and the data center at the preset time; when the preset time is reached, the computing resources of the data center and the power supply of the power grid system are adjusted based on the coordinated scheduling method, so that a coordinated scheduling scheme for the power grid system and the data center can be formulated in advance to improve the optimization effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Hereinafter, the present application will be described in more detail based on embodiments and with reference to the accompanying drawings.

[0035] Figure 1 A schematic diagram of the implementation flow of a computing power and power coordinated control method provided for the implementation of this application;

[0036] Figure 2 A schematic diagram of the structure of a computing power and power collaborative control device provided in an embodiment of the present application;

[0037] Figure 3A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0038] In the drawings, the same components use the same selection of welding guns, and the drawings are not drawn according to the actual scale. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

[0040] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0041] If similar descriptions of "first\second\third" appear in the application documents, the following instructions are added. In the following description, the terms "first\second\third" involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that "first\second\third" can be interchanged in a specific order or sequence where permitted, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0042] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0043] Based on the problems existing in the related art, the embodiment of the present application provides a method for collaborative control of computing power and electricity. The method for collaborative control of computing power and electricity provided in the embodiment of the present application can be applied to mobile phones, tablet computers, wearable devices, vehicle-mounted devices, augmented reality (AR) / virtual reality (VR) devices, laptops, ultra-mobile personal computers (UMPC), netbooks, personal digital assistants (PDA) and other electronic devices. The embodiment of the present application does not impose any restrictions on the specific type of electronic device. The electronic device can be connected to the data center and the power grid system. The functions implemented by the collaborative control method of computing power and electricity provided in the embodiment of the present application can be implemented by calling the program code by the processor of the electronic device, wherein the program code can be stored in a computer storage medium.

[0044] The present application provides a method for collaboratively controlling computing power and electricity. Figure 1 A schematic diagram of the implementation flow of a computing power and power coordinated control method provided for the implementation of this application is as follows: Figure 1 As shown, including:

[0045] Step S101, obtaining the current operation data of the data center, the current computing resource usage of the data center, and the current power supply information of the power grid system.

[0046] In the embodiment of the present application, the operation data may include at least one of computing load, energy consumption, etc. The computing resource usage includes at least one of CPU, GPU usage, memory occupancy, etc. The power supply information may include power supply quantity, voltage, frequency, etc.

[0047] In the embodiments of the present application, the data center generally includes: servers, network equipment, storage equipment, power supply equipment, etc. The power grid system can have multiple power supply modes. The power grid system can include: power supply stations, and can also integrate renewable energy systems such as solar energy and wind energy.

[0048] In the embodiments of the present application, the current operating data of the data center and the current computing resource usage of the data center can be obtained through the monitoring system and management tools.

[0049] In an embodiment of the present application, the data center is equipped with a monitoring system, which can monitor the current operating data and computing resources of the data center in real time. In some embodiments, the management tool can provide real-time resource utilization reports.

[0050] In some embodiments, the electronic device may also obtain the log files of the data center, and obtain the current operation data and the current computing resource usage of the data center through the log files.

[0051] In some embodiments, the electronic device may also be connected to communicate with a sensor to acquire data through the sensor.

[0052] In the embodiment of the present application, the electronic device can be connected to the power monitoring system for communication, and the power load, voltage, frequency, etc. can be monitored in real time through the power monitoring system.

[0053] Step S102, inputting the operating data into a prediction model to predict the computing demand and power demand of the data center at a preset time.

[0054] In the embodiment of the present application, the preset duration can be configured, and can be configured as a short-term duration or a long-term duration. For example, the computing demand and power demand in 10 minutes can be predicted, the computing demand and power demand in 1 day can be predicted, or even the computing demand and power demand in a longer period of time can be predicted.

[0055] In the embodiment of the present application, the operation data may be preprocessed before being input, and the preprocessing may include: cleaning, denoising, normalization, etc.

[0056] In the embodiments of the present application, the prediction model can be established based on machine learning algorithms, time series analysis, neural networks and other methods.

[0057] In the embodiment of the present application, the operation data can be collected, and the collected data can be cleaned, denoised, normalized, feature selected and other pre-processing operations to ensure the quality and applicability of the data. According to the domain knowledge and data characteristics, the data is subjected to feature extraction, conversion, combination and other operations to extract information useful for the prediction target. According to the characteristics of the predicted computing demand, power demand and data, a suitable prediction model is selected, such as linear regression, decision tree, random forest, neural network, etc. The selected model is trained using the prepared data, that is, the parameters of the model are adjusted by the data so that it can accurately predict the data. The trained model is evaluated using test data, and the evaluation indicators may include mean square error, accuracy, recall rate, etc. to evaluate the performance of the model. The model is optimized according to the evaluation results, and the hyperparameters, feature selection, data sampling, etc. of the model can be adjusted to improve the prediction accuracy of the model. After the prediction model is trained and optimized, its operation data can be input into the prediction model to predict the computing demand and power demand.

[0058] Step S103, inputting the computing demand, the power demand, the resource usage, and the power supply information into a neural network model to determine a coordinated scheduling plan for the power grid system and the data center at the preset time.

[0059] In the embodiment of the present application, computing demand, power demand, resource usage and power supply information are input into the neural network model, and the model is used to determine the collaborative scheduling plan at a preset time. The neural network model can provide a more intelligent and effective collaborative scheduling plan for the data center and the power grid system by learning the complex relationships in the historical data.

[0060] In an embodiment of the present application, the neural network model may include: a multi-layer perceptron (MLP), a recurrent neural network (RNN), a long short-term memory network (LSTM), etc.

[0061] In the embodiment of the present application, a neural network model can be pre-established, and establishing the neural network model can be achieved in the following ways:

[0062] A sample data set is obtained, wherein each sample data in the sample data set includes: sample computing demand, sample power demand, sample resource usage, sample power supply information, and a corresponding sample collaborative scheduling scheme. The sample data set can be preprocessed to convert the computing demand, power demand, resource usage, and power supply information into an input format that can be understood and processed by the neural network. The processed sample data set is divided into a training set and a test set. The neural network model is trained using the training set, and the model parameters are adjusted so that it can accurately learn the complex relationship between computing demand, power demand, resource usage, and power supply information. The trained neural network model is evaluated using the test set to evaluate the performance and accuracy of the model. When the trained neural network model can accurately predict the relationship between computing demand, power demand, resource usage, and power supply information, the model can be used to determine the collaborative scheduling scheme. The model can propose the best collaborative scheduling scheme based on future computing demand and power demand, combined with resource usage and power supply information, to ensure the normal operation of the data center and maximize energy efficiency.

[0063] In the embodiment of the present application, when training continues, the optimization function of the neural network model can be the one with the highest energy utilization rate.

[0064] Step S104: When the preset time is reached, the computing resources of the data center and the power supply of the power grid system are adjusted based on the collaborative scheduling method.

[0065] In an embodiment of the present application, the collaborative scheduling method may include: adjusting the computing resource allocation of the data center and adjusting the supply of the power grid system.

[0066] In the embodiment of the present application, adjusting the computing resource allocation of the data center may include: increasing or decreasing the number of servers, adjusting the virtual machine configuration, etc.

[0067] In the embodiment of the present application, adjusting the power supply of the power grid system may include: adjusting the output power of the generator, adjusting the load of the transmission line, adjusting the power supply mode of the power grid, etc.

[0068] In an embodiment of the present application, the electronic device can be communicatively connected with the data center and the power grid system, and when a preset time is reached, control instructions can be issued to the data center and the power grid system to enable the data center and the power grid system to make adjustments.

[0069] A method for coordinated control of computing power and electricity provided in an embodiment of the present application obtains current operating data of a data center, current computing resource usage of the data center, and current power supply information of a power grid system; inputs the operating data into a prediction model to predict the computing demand and power demand of the data center at a preset time; based on the computing demand, the power demand, the resource usage, and the power supply information, the computing demand is input into a neural network model to determine a coordinated scheduling scheme for the power grid system and the data center at the preset time; when the preset time is reached, the computing resources of the data center and the power supply of the power grid system are adjusted based on the coordinated scheduling method, so that a coordinated scheduling scheme for the power grid system and the data center can be formulated in advance to improve the optimization effect.

[0070] In some embodiments, after the adjustment is completed, the computing resources of the data center and the power supply of the power grid system need to be monitored to ensure that they can operate normally and meet current needs. Monitoring can be performed through real-time monitoring systems, data collection and analysis, etc. The collaborative scheduling scheme can be optimized based on actual conditions and feedback information to improve its efficiency and reliability. The collaborative scheduling scheme can be optimized based on historical data and prediction results to improve resource utilization and energy efficiency.

[0071] In some embodiments, the method further comprises:

[0072] Step S105, obtaining the operating data of each computing node in the data center.

[0073] In the embodiment of the present application, the operating data may include: CPU, memory, disk, network and other indicators.

[0074] The operating status of each computing node can be monitored in real time through the monitoring system.

[0075] Step S106: determining whether each computing node has a fault based on the operation data of each computing node.

[0076] In the embodiment of the present application, the operation data can be analyzed in real time to determine whether there is a fault in each computing node.

[0077] In an embodiment of the present application, the CPU utilization of the computing node can be monitored. If the CPU utilization of a node is abnormally low or abnormally high, it may indicate that the node is faulty. In some embodiments, the memory usage of the computing node can be determined, and abnormal memory usage may indicate that the node is faulty. In some embodiments, the disk space occupancy of the computing node is monitored. If the disk space occupancy is abnormal or a disk read or write error occurs, it may indicate that the node is faulty. In some embodiments, the network traffic of the computing node is checked. Abnormal network traffic may indicate that the node is faulty, such as network interruption or packet loss. In some embodiments, the system log of the computing node can be analyzed to find records of hardware failures, software errors, or other anomalies. In some embodiments, a health check is performed on the computing node by running diagnostic tools or scripts through running data to detect the hardware and software status of the node.

[0078] Step S107: when the first target computing node has a fault, allocate the computing task of the first target computing node to a second target computing node, wherein the second target computing node has no fault.

[0079] In the embodiment of the present application, if the first target computing node fails, its computing task needs to be allocated to the second target computing node. When allocating computing tasks, it is necessary to ensure that the second target computing node does not fail to complete the task or cause data loss.

[0080] In an embodiment of the present application, once it is confirmed that the first target computing node has a fault, the computing task on it can be migrated to the second target computing node. Before allocation, the task status needs to be saved and restored to ensure that no data or task progress is lost during the migration process. Resources, including CPU, memory, storage, etc., are reallocated on the second target computing node to meet the needs of the newly added tasks. The resource allocation strategy needs to be dynamically adjusted to ensure that the task can obtain sufficient computing resources.

[0081] The method provided in the embodiments of the present application can monitor and analyze the operating status of each computing node in the data center, and in the event of a failure in a computing node, distribute computing tasks to other normal computing nodes to ensure the normal operation of the data center.

[0082] In some embodiments, when it is determined that the first target computing node has a storage failure, relevant operation and maintenance personnel, developers, or business personnel may be notified to be informed of the failure handling status and the status after task migration.

[0083] In some embodiments, the power supply method may include multiple power supply methods, for example, it may include: city power supply, solar energy, wind energy and other renewable energy systems installed by the data center itself.

[0084] In the embodiment of the present application, adjusting the power supply of the power grid system includes:

[0085] Switch the power supply mode, or increase the power supply mode, or reduce the power supply mode.

[0086] For example, disconnecting from the mains and switching to renewable energy systems such as itself.

[0087] In some embodiments, the method further comprises:

[0088] Step S108: determining whether to reduce the power demand of the data center when obtaining request information from the power grid system for requesting the data center to reduce the power demand.

[0089] In the embodiment of the present application, after obtaining the request information for the power grid system to request the data center to reduce the power demand, the following steps can be taken to determine whether to reduce the power demand of the data center:

[0090] Analyze the current power demand of the data center, including the energy consumption of servers, network equipment, air conditioners and other equipment, and analyze whether there is any power demand that can be reduced. Based on the evaluation of power demand, you can decide whether to reduce the power demand of the data center. For example, you can reduce power demand by dynamically adjusting the operating status of the server, using energy-saving mode, suspending some tasks or shutting down some equipment.

[0091] Step S109, when it is determined that the power demand of the data center is reduced, feedback information is given to the power grid system so that the power grid system calculates the contribution value based on the reduced power resources and allocates the contribution value to the power account corresponding to the data center.

[0092] In the embodiment of the present application, when it is determined to reduce the power demand of the data center, it may be necessary to send feedback information to the power grid system to notify it that measures have been taken to reduce the power demand.

[0093] In the embodiment of the present application, after receiving the feedback information of reducing the power demand of the data center, the power grid system can calculate the corresponding contribution value based on the reduced power resources. When calculating the contribution value, the calculation is performed based on factors such as the reduced power demand, time period, energy market price, etc., and the contribution value is allocated to the power account corresponding to the data center.

[0094] In some embodiments, after the data center reduces its power demand, it can continuously monitor energy consumption and adjust power demand according to actual conditions. At the same time, the power grid system also needs to monitor the distribution of contribution values ​​and adjust the distribution strategy according to the actual contribution of the data center.

[0095] The method provided in the embodiment of the present application determines whether to reduce the power demand of the data center when request information from the power grid system is obtained to request the data center to reduce the power demand; when it is determined to reduce the power demand of the data center, feedback information is given to the power grid system so that the power grid system calculates the contribution value based on the reduced power resources, and allocates the contribution value to the power account corresponding to the data center. This can realize energy collaborative management, help improve energy utilization efficiency, reduce energy costs, and is also beneficial to the smooth operation of the power grid system and the sustainability of energy supply.

[0096] In some embodiments, the method further comprises:

[0097] Step S110, obtaining electricity price change information of the power grid system.

[0098] In the embodiment of the present application, the electricity price change information of the power grid system can be obtained, including the change trend of the electricity price, time period, peak and valley electricity prices, etc.

[0099] Step S111: adjusting the operation strategy of the data center based on the electricity price change information to reduce electricity prices.

[0100] In the embodiment of the present application, based on the information of electricity price changes, the operation strategy can be adjusted to reduce the electricity price. For example, during the peak period of electricity price, the energy consumption of the data center can be reduced, some tasks can be suspended or some equipment can be shut down to reduce the electricity cost.

[0101] The method provided in the embodiment of the present application adjusts its operation strategy based on the information of electricity price changes to reduce electricity price costs. This energy cost management method helps to improve the energy utilization efficiency of the data center and reduce energy costs.

[0102] In some embodiments, the method further comprises:

[0103] The computing resources of the data center are dynamically configured based on the operating data.

[0104] In some embodiments, the method further comprises:

[0105] When an abnormality is detected in the power grid system, switch to emergency power supply.

[0106] In the embodiment of the present application, the data center is equipped with backup emergency power supply equipment, such as a UPS (uninterruptible power supply) system or a diesel generator, etc. These devices can provide temporary power support when an abnormality occurs in the power grid system.

[0107] In the embodiment of the present application, the electronic device can monitor the state of the power grid system in real time, including parameters such as voltage, frequency, current, etc. This can be achieved through monitoring devices and sensors to detect abnormal conditions of the power grid in a timely manner.

[0108] In the embodiment of the present application, when an abnormality in the power grid is detected, the emergency power supply is automatically switched to ensure the continuous power supply of the data center. This may involve the use of automatic switching equipment and systems to achieve this.

[0109] In the embodiment of the present application, by timely switching to the emergency power supply when an abnormality in the power grid system is obtained, the continuous power supply and normal operation of the data center are ensured. This emergency power supply switching method helps to improve the reliability and stability of the data center and ensure the continuity of the data center business.

[0110] Based on the foregoing embodiments, the embodiments of the present application further provide a computing power-electricity collaborative control method, which is applied to a computing power-electricity collaborative system. The computing power-electricity collaboration may include: a data center management unit (DCMU), an intelligent energy management unit (IEMU) and a computing power-electricity collaborative control unit (CPCU), wherein the data center management unit (DCMU) is responsible for the overall operation and management of the data center, including computing power resource scheduling, data storage and processing, etc. Intelligent Energy Management Unit (IEMU): Focuses on the management of power resources to optimize energy consumption and improve energy efficiency. Computing Power-Electricity Collaborative Control Unit (CPCU): Serves as a bridge between DCMU and IEMU to achieve collaborative optimization of computing power resources and power resources.

[0111] In the embodiment of the present application, the computing power and electricity coordination system first collects the operation data inside the data center through sensors and monitoring equipment, including computing load, energy consumption, etc. Using machine learning and big data analysis technology, the system predicts short-term and long-term computing power demand and power demand. Based on the prediction results, CPCU guides DCMU and IEMU to dynamically optimize the configuration of computing power and power resources.

[0112] The method provided in the embodiment of the present application predicts computing power demand and electricity demand, and the system formulates a resource allocation strategy in advance. When encountering emergency or unexpected situations, the system can quickly adjust resource allocation to ensure the stable operation of the data center.

[0113] Based on the foregoing embodiments, an embodiment of the present application provides a computing power and power coordinated control device, and the modules included in the device, as well as the units included in each module, can be implemented by a processor in a computer device; of course, it can also be implemented by a specific logic circuit; in the implementation process, the processor can be a central processing unit (CPU, Central Processing Unit), a microprocessor (MPU, Microprocessor Unit), a digital signal processor (DSP, Digital Signal Processing) or a field programmable gate array (FPGA, Field Programmable Gate Array), etc.

[0114] The present application provides a computing power and electric power collaborative control device, Figure 2 A schematic diagram of the structure of a computing power and power collaborative control device provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, the computing power and electric power coordinated control device 200 includes:

[0115] The first acquisition module 201 is used to acquire the current operation data of the data center, the current computing resource usage of the data center and the current power supply information of the power grid system;

[0116] A prediction module 202, for inputting the operation data into a prediction model to predict the computing demand and power demand of the data center at a preset time;

[0117] A first determination module 203, for inputting the computing demand, the power demand, the resource usage, and the power supply information into a neural network model to determine a coordinated scheduling scheme of the power grid system and the data center at the preset time;

[0118] The first control module 204 is used to adjust the computing resources of the data center and the power supply of the power grid system based on the collaborative scheduling method when the preset time is reached.

[0119] In some embodiments, the computing power and power coordinated control device further includes:

[0120] A second acquisition module is used to acquire the operation data of each computing node in the data center;

[0121] A second determination module, used to determine whether each computing node has a fault based on the operation data of each computing node;

[0122] The allocation module is used to allocate the computing task of the first target computing node to the second target computing node when the first target computing node fails, wherein the second target computing node does not fail.

[0123] In some embodiments, the power supply method includes: multiple power supply methods, adjusting the power supply of the power grid system, including:

[0124] Switch the power supply mode, or increase the power supply mode, or reduce the power supply mode.

[0125] In some embodiments, the computing power and power coordinated control device further includes:

[0126] A third determination module is used to determine whether to reduce the power demand of the data center when obtaining request information for the power grid system to request the data center to reduce the power demand;

[0127] A feedback module is used to provide feedback information to the power grid system when it is determined that the power demand of the data center is reduced, so that the power grid system calculates a contribution value based on the reduced power resources and allocates the contribution value to the power account corresponding to the data center.

[0128] In some embodiments, the computing power and power coordinated control device further includes:

[0129] The third acquisition module is used to obtain the electricity price change information of the power grid system;

[0130] The second control module is used to adjust the operation strategy of the data center based on the electricity price change information to reduce electricity price costs.

[0131] In some embodiments, the computing power and power coordinated control device further includes:

[0132] A dynamic configuration module is used to dynamically configure the computing resources of the data center based on the operating data.

[0133] In some embodiments, the computing power and power coordinated control device further includes:

[0134] The switching module is used to switch to the emergency power supply when an abnormality is detected in the power grid system.

[0135] An embodiment of the present application provides an electronic device, Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application is shown in FIG. Figure 3As shown, the electronic device 600 includes: a processor 601, at least one communication bus 602, a user interface 603, at least one external communication interface 604, and a memory 605. Among them, the communication bus 602 is configured to realize the connection and communication between these components. Among them, the user interface 603 may include a display screen, and the external communication interface 604 may include a standard wired interface and a wireless interface. The processor 601 is configured to execute the program of the computing power and power collaborative control method stored in the memory to implement the steps in the computing power and power collaborative control method provided in the above embodiment.

[0136] In the embodiment of the present application, if the above-mentioned computing power and power collaborative control method is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application is essentially or partly contributed to the prior art can be embodied in the form of a software product, which is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a U disk, a mobile hard disk, a read-only memory (ROM, Read Only Memory), a disk or an optical disk. In this way, the embodiment of the present application is not limited to any specific combination of hardware and software.

[0137] Accordingly, an embodiment of the present application provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the computing power and power coordinated control method provided in the above embodiment are implemented.

[0138] An embodiment of the present application further provides a computer program product, which, when executed on a terminal device, enables the electronic device to execute any of the above-mentioned computing power and power coordinated control methods.

[0139] The description of the above electronic device and storage medium embodiments is similar to the description of the above method embodiments, and has similar beneficial effects as the method embodiments. For technical details not disclosed in the computer device and storage medium embodiments of this application, please refer to the description of the method embodiments of this application for understanding.

[0140] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present application, the size of the sequence number of the above-mentioned processes does not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application. The above-mentioned sequence numbers of the embodiments of the present application are only for description and do not represent the advantages and disadvantages of the embodiments.

[0141] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article 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, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0142] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0143] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0144] In addition, all functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may be a separate unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0145] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read Only Memory), disks or optical disks, etc. Various media that can store program codes.

[0146] Alternatively, if the above-mentioned integrated unit of the present application is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application can essentially or in other words, the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for a controller to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0147] The above is only an implementation method of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for coordinated control of computing power and electricity, characterized in that: include: Obtaining current operating data of the data center, current computing resource usage of the data center, and current power supply information of the power grid system; Inputting the operation data into a prediction model to predict the computing demand and power demand of the data center at a preset time; Inputting the computing demand, the power demand, the resource usage, and the power supply information into a neural network model to determine a coordinated scheduling scheme for the power grid system and the data center at the preset time; When the preset time is reached, the computing resources of the data center and the power supply of the power grid system are adjusted based on the collaborative scheduling method.

2. The method according to claim 1, characterized in that The method further comprises: Obtaining operation data of each computing node in the data center; Determine whether each computing node has a fault based on the operation data of each computing node; In the case that a first target computing node has a fault, a computing task of the first target computing node is allocated to a second target computing node, wherein the second target computing node has no fault.

3. The method according to claim 1, characterized in that The power supply method includes: multiple power supply methods, adjusting the power supply of the power grid system, including: Switch the power supply mode, or increase the power supply mode, or reduce the power supply mode.

4. The method according to claim 1, characterized in that The method further comprises: In the case of obtaining request information from the power grid system for requesting the data center to reduce power demand, determining whether to reduce the power demand of the data center; When it is determined that the power demand of the data center is reduced, feedback information is given to the power grid system so that the power grid system calculates a contribution value based on the reduced power resources and allocates the contribution value to a power account corresponding to the data center.

5. The method according to claim 1, characterized in that The method further comprises: Obtain information on electricity price changes in the power grid system; The operation strategy of the data center is adjusted based on the electricity price change information to reduce electricity price costs.

6. The method according to claim 1, characterized in that The method further comprises: The computing resources of the data center are dynamically configured based on the operating data.

7. The method according to claim 1, characterized in that The method further comprises: When an abnormality is detected in the power grid system, switch to emergency power supply.

8. A computing power and electric power coordinated control device, characterized in that: include: A first acquisition module is used to acquire the current operation data of the data center, the current computing resource usage of the data center, and the current power supply information of the power grid system; A prediction module, used for inputting the operation data into a prediction model to predict the computing demand and power demand of the data center at a preset time; A first determination module is used to input the computing demand, the power demand, the resource usage, and the power supply information into a neural network model to determine a coordinated scheduling scheme of the power grid system and the data center at the preset time; The first control module is used to adjust the computing resources of the data center and the power supply of the power grid system based on the collaborative scheduling method when the preset time is reached.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, it implements the computing power and power coordinated control method as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, a computing power and electric power coordinated control method as claimed in any one of claims 1 to 7 is implemented.

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