Calculation power scheduling potential analysis method and device of power data center

Abstract

The invention relates to the technical field of regional computing power scheduling, and particularly provides a computing power scheduling potential analysis method and device of an electric power data center. Comprising the following steps: substituting the weather temperature of a region to which a to-be-analyzed data center belongs and the power load of the to-be-analyzed data center into a pre-fitted computing power polynomial regression model of the to-be-analyzed data center, and solving to obtain the computing power of the to-be-analyzed data center; determining the computing power scheduling potential of the to-be-analyzed data center based on the computing power of the to-be-analyzed data center; wherein when the time required for data transmission of the communication line exceeds a threshold value, the computing power scheduling potential of the data center to be analyzed is corrected based on the load rate of the communication line. According to the technical scheme provided by the invention, technical support can be provided for regional computing power scheduling, cross-domain power-computing power cooperative scheduling is promoted, and the operation effect of computing power infrastructure is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of regional computing power scheduling, and in particular to a computing power scheduling potential analysis method and device for an electric power data center. Background Art

[0002] With the rapid development of digital technology, data has become an important strategic resource. In order to solve the problem of data center resource conflicts, by optimizing the layout of data centers, data processing needs in areas with insufficient data center resources are directed to areas with abundant data center resources, and the rich natural resources and lower operating costs in areas with abundant data center resources are utilized to achieve reasonable allocation and efficient use of data resources.

[0003] However, there are many challenges in the actual promotion process. On the one hand, the computing power load rate of data centers in areas with abundant data center resources is generally low. The main reason is the lack of an effective computing power scheduling mechanism, which makes it difficult to accurately match the computing power demand in areas with insufficient data center resources with the computing power resources in areas with abundant data center resources. The traditional static computing power scheduling method cannot adapt to the fluctuations in computing power demand in areas with insufficient data center resources and the dynamic changes in computing power resources in areas with abundant data center resources, resulting in a high resource idle rate and waste of resources.

[0004] On the other hand, data centers in areas with abundant data center resources have difficulty in timely grasping the computing power available, and lack the ability to perceive computing power resources in real time and dynamically adjust them. Most existing computing power scheduling systems rely on manual monitoring and regular statistics, and cannot quickly respond to changes in computing power demand, resulting in an imbalance between computing power supply and demand, affecting data transmission efficiency and computing power utilization.

[0005] In addition, data centers in areas with abundant data center resources have difficulty fully utilizing the real-time nature of power data, and cannot combine power data with computing power scheduling to achieve refined management and dynamic scheduling. Power data can reflect the operating status and resource usage of data centers, but the existing computing power scheduling system has not yet effectively utilized power data, resulting in a lack of accuracy and real-time computing power scheduling. Summary of the invention

[0006] In order to overcome the above-mentioned defects, the present invention proposes a method and device for analyzing the computing power scheduling potential of an electric power data center.

[0007] In a first aspect, a method for analyzing computing power scheduling potential of a power data center is provided, and the method for analyzing computing power scheduling potential of a power data center comprises:

[0008] Substitute the weather temperature of the area where the data center to be analyzed belongs and the power load of the data center to be analyzed into the pre-fitted computing power polynomial regression model of the data center to be analyzed and solve it to obtain the computing power of the data center to be analyzed;

[0009] Determining the computing power scheduling potential of the data center to be analyzed based on the computing power of the data center to be analyzed;

[0010] Among them, when the time required for data transmission on the communication line exceeds a threshold, the computing power scheduling potential of the data center to be analyzed is corrected based on the load rate of the communication line.

[0011] Preferably, the fitting process of the pre-fitted computing power polynomial regression model of the data center to be analyzed includes:

[0012] The initial polynomial regression model is trained by machine learning based on the historical weather temperature of the area where the data center to be analyzed is located, the historical power load of the data center to be analyzed, and the historical computing power of the data center to be analyzed, so as to obtain the pre-fitted computing power polynomial regression model of the data center to be analyzed.

[0013] Preferably, the pre-fitted computing power polynomial regression model of the data center to be analyzed is as follows:

[0014] CP=a0+a1*PL+a2*T+a3*PL 2 +a4*T 2 +a5*PL*T

[0015] In the above formula, CP is the computing power of the data center to be analyzed, a0, a1…a5 are fitting coefficients, T is the weather temperature of the area where the data center to be analyzed is located, and PL is the power load of the data center to be analyzed.

[0016] Preferably, the computing power scheduling potential of the data center to be analyzed is as follows:

[0017] CP 潜力 =R comp -CP

[0018] In the above formula, CP 潜力 R is the computing power scheduling potential of the data center to be analyzed. comp is the rated computing power of the data center to be analyzed, and CP is the computing power of the data center to be analyzed.

[0019] Preferably, the time required for data transmission of the communication line is as follows:

[0020]

[0021] In the above formula, T req D is the time required for data transmission over the communication line. req is the amount of data that needs to be transmitted through the communication line, T available,i The available bandwidth of the communication line.

[0022] Furthermore, the available bandwidth of the communication line is as follows:

[0023] T available,i =T max -T load,i

[0024] In the above formula, T load,i is the communication line load, T max The maximum load of the communication line.

[0025] Furthermore, the correction of the computing power scheduling potential of the data center to be analyzed based on the communication line load rate includes:

[0026] The computing power scheduling potential of the data center to be analyzed is corrected as follows:

[0027] CP 修正潜力 =CP 潜力 ×(1-α×ρ line )

[0028] In the above formula, CP 修正潜力 is the potential correction value of computing power scheduling of the data center to be analyzed, α is the adjustment coefficient, and ρ line is the communication line load rate.

[0029] Furthermore, the communication line load rate is as follows:

[0030]

[0031] In the above formula, T load,i is the communication line load, T max The maximum load of the communication line.

[0032] In a second aspect, a computing power scheduling potential analysis device for a power data center is provided, wherein the computing power scheduling potential analysis device for a power data center comprises:

[0033] The first analysis module is used to substitute the weather temperature of the area where the data center to be analyzed belongs and the power load of the data center to be analyzed into the pre-fitted computing power polynomial regression model of the data center to be analyzed and solve it to obtain the computing power of the data center to be analyzed;

[0034] A second analysis module, configured to determine the computing power scheduling potential of the data center to be analyzed based on the computing power of the data center to be analyzed;

[0035] The third analysis module is used to correct the computing power scheduling potential of the data center to be analyzed based on the load rate of the communication line when the time required for data transmission on the communication line exceeds a threshold.

[0036] In a third aspect, a computer device is provided, comprising: one or more processors;

[0037] The processor is used to store one or more programs;

[0038] When the one or more programs are executed by the one or more processors, the computing power scheduling potential analysis method of the power data center is implemented.

[0039] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed, the method for analyzing the computing power scheduling potential of the power data center is implemented.

[0040] The above one or more technical solutions of the present invention have at least one or more of the following beneficial effects:

[0041] The present invention provides a method and device for analyzing the computing power scheduling potential of an electric power data center, including: substituting the weather temperature of the area to which the data center to be analyzed belongs and the power load of the data center to be analyzed into a pre-fitted computing power polynomial regression model of the data center to be analyzed and solving it to obtain the computing power of the data center to be analyzed; determining the computing power scheduling potential of the data center to be analyzed based on the computing power of the data center to be analyzed; wherein, when the time required for data transmission of the communication line exceeds a threshold, the computing power scheduling potential of the data center to be analyzed is corrected based on the load rate of the communication line. The technical solution provided by the present invention can provide technical support for regional computing power scheduling, promote cross-domain power-computing power coordinated scheduling, and improve the operation effectiveness of computing power infrastructure. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a schematic flow chart of the main steps of the method for analyzing the computing power scheduling potential of a power data center according to an embodiment of the present invention;

[0043] Figure 2 is a diagram showing the relationship between power load and computing power of a liquid-cooled data center and an air-cooled data center according to an embodiment of the present invention;

[0044] Figure 3 This is a prediction diagram of computing power scheduling potential for a liquid-cooled data center and an air-cooled data center according to an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The specific implementation modes of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0046] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] As disclosed in the background technology, with the rapid development of digital technology, data has become an important strategic resource. In order to solve the problem of data center resource conflicts, by optimizing the data center layout, the data processing needs in areas with insufficient data center resources are guided to areas with abundant data center resources, and the rich natural resources and lower operating costs in areas with abundant data center resources are utilized to achieve reasonable allocation and efficient use of data resources.

[0048] However, there are many challenges in the actual promotion process. On the one hand, the computing power load rate of data centers in areas with abundant data center resources is generally low. The main reason is the lack of an effective computing power scheduling mechanism, which makes it difficult to accurately match the computing power demand in areas with insufficient data center resources with the computing power resources in areas with abundant data center resources. The traditional static computing power scheduling method cannot adapt to the fluctuations in computing power demand in areas with insufficient data center resources and the dynamic changes in computing power resources in areas with abundant data center resources, resulting in a high resource idle rate and waste of resources.

[0049] On the other hand, data centers in areas with abundant data center resources have difficulty in timely grasping the computing power available, and lack the ability to perceive computing power resources in real time and dynamically adjust them. Most existing computing power scheduling systems rely on manual monitoring and regular statistics, and cannot quickly respond to changes in computing power demand, resulting in an imbalance between computing power supply and demand, affecting data transmission efficiency and computing power utilization.

[0050] In addition, data centers in areas with abundant data center resources have difficulty fully utilizing the real-time nature of power data, and cannot combine power data with computing power scheduling to achieve refined management and dynamic scheduling. Power data can reflect the operating status and resource usage of data centers, but the existing computing power scheduling system has not yet effectively utilized power data, resulting in a lack of accuracy and real-time computing power scheduling.

[0051] In order to improve the above-mentioned problems, the present invention provides a method and device for analyzing the computing power scheduling potential of an electric power data center, including: substituting the weather temperature of the area to which the data center to be analyzed belongs and the power load of the data center to be analyzed into a pre-fitted computing power polynomial regression model of the data center to be analyzed and solving it to obtain the computing power of the data center to be analyzed; determining the computing power scheduling potential of the data center to be analyzed based on the computing power of the data center to be analyzed; wherein, when the time required for data transmission of the communication line exceeds a threshold, the computing power scheduling potential of the data center to be analyzed is corrected based on the load rate of the communication line. The technical solution provided by the present invention can provide technical support for regional computing power scheduling, promote cross-domain power-computing power coordinated scheduling, and improve the operation efficiency of computing power infrastructure. The above-mentioned scheme is elaborated in detail below.

[0052] Example 1

[0053] See attached Figure 1, Figure 1 FIG. 1 is a flow chart showing the main steps of a method for analyzing computing power scheduling potential of a power data center according to an embodiment of the present invention. Figure 1 As shown, the computing power scheduling potential analysis method of the power data center in the embodiment of the present invention mainly includes the following steps:

[0054] Step S101: Substitute the weather temperature of the area where the data center to be analyzed belongs and the power load of the data center to be analyzed into the pre-fitted computing power polynomial regression model of the data center to be analyzed and solve it to obtain the computing power of the data center to be analyzed;

[0055] Step S102: determining the computing power scheduling potential of the data center to be analyzed based on the computing power of the data center to be analyzed;

[0056] Among them, when the time required for data transmission on the communication line exceeds a threshold, the computing power scheduling potential of the data center to be analyzed is corrected based on the load rate of the communication line.

[0057] In this embodiment, the weather temperature of the area where the data center to be analyzed is located and the power load of the data center to be analyzed can be predicted data, which are obtained through a machine learning model to calculate the scheduling potential of computing power in the future.

[0058] In this embodiment, the fitting process of the pre-fitted computing power polynomial regression model of the data center to be analyzed includes:

[0059] The initial polynomial regression model is trained by machine learning based on the historical weather temperature of the area where the data center to be analyzed is located, the historical power load of the data center to be analyzed, and the historical computing power of the data center to be analyzed, so as to obtain the pre-fitted computing power polynomial regression model of the data center to be analyzed.

[0060] Specifically, a typical data center electricity-computing power sample library will be established, including air-cooled mode data centers and liquid-cooled mode data centers, to obtain electricity data, computing power data, meteorological data, and reporting data, etc.

[0061] Data center total power load E, data center IT equipment load E IT , data center no-load power P idle , the maximum design power load of the data center P max , data center rated power R power , data center rated computing power R comp , ambient temperature T. Sample library X:

[0062]

[0063] Among them, E i,t represents the total power load of data center i at time t, represents the total IT equipment load of data center i at time t, represents the no-load power of data center i, represents the maximum design power load of data center i, represents the rated power of data center i, represents the rated computing power of data center i, T i,t is the ambient temperature of data center i at time t.

[0064] Extract the correlation between the computing power and power load of a typical data center. Through the Pearson correlation analysis method, it is found that the main influencing factors of the computing power of the data center are power load and weather temperature. The relationship between the power load and computing power of the liquid-cooled data center and the air-cooled data center is shown in the figure below: Figure 2 As shown in the figure, a polynomial regression model is trained through machine learning using the data set in the database, and the coefficients in the polynomial are solved.

[0065] Combined with the type, geographical location, total power load, and meteorological data of the data center to be analyzed, the actual operation data of the data center to be analyzed is standardized. According to the rated computing power, rated power and other parameters of the data center to be analyzed, the coefficients in the model are adjusted, and the correlation coefficient between the computing power of the data center and the power load is corrected. The computing power polynomial regression model of the data center to be analyzed is obtained as follows:

[0066] CP=a0+a1*PL+a2*T+a3*PL 2 +a4*T 2 +a5*PL*T

[0067] In the above formula, CP is the computing power of the data center to be analyzed, a0, a1…a5 are fitting coefficients, T is the weather temperature of the area where the data center to be analyzed is located, and PL is the power load of the data center to be analyzed.

[0068] In this embodiment, the computing power scheduling potential of the data center to be analyzed is as follows:

[0069] CP 潜力 =R comp -CP

[0070] In the above formula, CP 潜力 R is the computing power scheduling potential of the data center to be analyzed. comp is the rated computing power of the data center to be analyzed, and CP is the computing power of the data center to be analyzed.

[0071] In this embodiment, the time required for data transmission on the communication line is as follows:

[0072]

[0073] In the above formula, Treq D is the time required for data transmission over the communication line. req is the amount of data that needs to be transmitted through the communication line, T available,i The available bandwidth of the communication line.

[0074] In one embodiment, the available bandwidth of the communication line is as follows:

[0075] T available,i =T max -T load,i

[0076] In the above formula, T load,i is the communication line load, T max The maximum load of the communication line.

[0077] In one embodiment, the modifying of the computing power scheduling potential of the data center to be analyzed based on the communication line load rate includes:

[0078] The computing power scheduling potential of the data center to be analyzed is corrected as follows:

[0079] CP 修正潜力 =CP 潜力 ×(1-α×ρ line )

[0080] In the above formula, CP 修正潜力 is the potential correction value of computing power scheduling of the data center to be analyzed, α is the adjustment coefficient, and ρ line is the communication line load rate.

[0081] In one embodiment, the communication line load rate is as follows:

[0082]

[0083] In the above formula, T load,i is the communication line load, T max The maximum load of the communication line.

[0084] In a specific implementation, the computing power scheduling potential prediction diagram of a liquid cooling data center and an air cooling data center is as follows: Figure 3 shown.

[0085] Example 2

[0086] Based on the same inventive concept, the present invention also provides a computing power scheduling potential analysis device for a power data center, and the computing power scheduling potential analysis device for a power data center includes:

[0087] The first analysis module is used to substitute the weather temperature of the area where the data center to be analyzed belongs and the power load of the data center to be analyzed into the pre-fitted computing power polynomial regression model of the data center to be analyzed and solve it to obtain the computing power of the data center to be analyzed;

[0088] A second analysis module, configured to determine the computing power scheduling potential of the data center to be analyzed based on the computing power of the data center to be analyzed;

[0089] The third analysis module is used to correct the computing power scheduling potential of the data center to be analyzed based on the load rate of the communication line when the time required for data transmission on the communication line exceeds a threshold.

[0090] Preferably, the fitting process of the pre-fitted computing power polynomial regression model of the data center to be analyzed includes:

[0091] The initial polynomial regression model is trained by machine learning based on the historical weather temperature of the area where the data center to be analyzed is located, the historical power load of the data center to be analyzed, and the historical computing power of the data center to be analyzed, so as to obtain the pre-fitted computing power polynomial regression model of the data center to be analyzed.

[0092] Preferably, the pre-fitted computing power polynomial regression model of the data center to be analyzed is as follows:

[0093] CP=a0+a1*PL+a2*T+a3*PL 2 +a4*T 2 +a5*PL*T

[0094] In the above formula, CP is the computing power of the data center to be analyzed, a0, a1…a5 are fitting coefficients, T is the weather temperature of the area where the data center to be analyzed is located, and PL is the power load of the data center to be analyzed.

[0095] Preferably, the computing power scheduling potential of the data center to be analyzed is as follows:

[0096] CP 潜力 =R comp -CP

[0097] In the above formula, CP 潜力 R is the computing power scheduling potential of the data center to be analyzed. comp is the rated computing power of the data center to be analyzed, and CP is the computing power of the data center to be analyzed.

[0098] Preferably, the time required for data transmission of the communication line is as follows:

[0099]

[0100] In the above formula, T reqD is the time required for data transmission over the communication line. req is the amount of data that needs to be transmitted through the communication line, T available,i The available bandwidth of the communication line.

[0101] Furthermore, the available bandwidth of the communication line is as follows:

[0102] T available,i =T max -T load,i

[0103] In the above formula, T load,i is the communication line load, T max The maximum load of the communication line.

[0104] Furthermore, the correction of the computing power scheduling potential of the data center to be analyzed based on the communication line load rate includes:

[0105] The computing power scheduling potential of the data center to be analyzed is corrected as follows:

[0106] CP 修正潜力 =CP 潜力 ×(1-α×ρ line )

[0107] In the above formula, CP 修正潜力 is the potential correction value of computing power scheduling of the data center to be analyzed, α is the adjustment coefficient, and ρ line is the communication line load rate.

[0108] Furthermore, the communication line load rate is as follows:

[0109]

[0110] In the above formula, T load,i is the communication line load, T max The maximum load of the communication line.

[0111] Example 3

[0112] Based on the same inventive concept, the present invention also provides a computer device, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of the computing power scheduling potential analysis method of a power data center in the above embodiment.

[0113] Example 4

[0114] Based on the same inventive concept, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here may include both built-in storage media in a computer device and, of course, an extended storage medium supported by the computer device. The computer-readable storage medium provides a storage space that stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions may be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here may be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor may load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of a method for analyzing the computing power scheduling potential of a power data center in the above embodiment.

[0115] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0116] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0117] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0118] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0119] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for analyzing computing power scheduling potential of a power data center, characterized in that: The method comprises: Substitute the weather temperature of the area where the data center to be analyzed belongs and the power load of the data center to be analyzed into the pre-fitted computing power polynomial regression model of the data center to be analyzed and solve it to obtain the computing power of the data center to be analyzed; Determining the computing power scheduling potential of the data center to be analyzed based on the computing power of the data center to be analyzed; Among them, when the time required for data transmission on the communication line exceeds a threshold, the computing power scheduling potential of the data center to be analyzed is corrected based on the load rate of the communication line.

2. The method according to claim 1, characterized in that The fitting process of the pre-fitted computing power polynomial regression model of the data center to be analyzed includes: The initial polynomial regression model is trained by machine learning based on the historical weather temperature of the area where the data center to be analyzed is located, the historical power load of the data center to be analyzed, and the historical computing power of the data center to be analyzed, so as to obtain the pre-fitted computing power polynomial regression model of the data center to be analyzed.

3. The method according to claim 1, characterized in that The pre-fitted computing power polynomial regression model of the data center to be analyzed is as follows: CP=a0+a1*PL+a2*T+a3*PL 2 +a4*T 2 +a5*PL*T In the above formula, CP is the computing power of the data center to be analyzed, a0, a1…a5 are fitting coefficients, T is the weather temperature of the area where the data center to be analyzed is located, and PL is the power load of the data center to be analyzed.

4. The method according to claim 1, characterized in that The computing power scheduling potential of the data center to be analyzed is as follows: CP 潜力 =R comp -CP In the above formula, CP 潜力 R is the computing power scheduling potential of the data center to be analyzed. comp is the rated computing power of the data center to be analyzed, and CP is the computing power of the data center to be analyzed.

5. The method according to claim 1, characterized in that The time required for data transmission on the communication line is as follows: In the above formula, T req The time required for data transmission over the communication line, D req is the amount of data that needs to be transmitted through the communication line, T available,i The available bandwidth of the communication line.

6. The method according to claim 5, characterized in that The available bandwidth of the communication line is as follows: T available,i =T max -T load,i In the above formula, T load,i is the communication line load, T max The maximum load of the communication line.

7. The method according to claim 4, characterized in that The correction of the computing power scheduling potential of the data center to be analyzed based on the communication line load rate includes: The computing power scheduling potential of the data center to be analyzed is corrected as follows: CP 修正潜力 =CP 潜力 ×(1-α×ρ line ) In the above formula, CP 修正潜力 is the potential correction value of computing power scheduling of the data center to be analyzed, α is the adjustment coefficient, and ρ line is the communication line load rate.

8. The method according to claim 7, characterized in that The communication line load rate is as follows: In the above formula, T load,i is the communication line load, T max The maximum load of the communication line.

9. A device for analyzing the computing power scheduling potential of a power data center based on any one of claims 1 to 8, characterized in that: The device comprises: The first analysis module is used to substitute the weather temperature of the area where the data center to be analyzed belongs and the power load of the data center to be analyzed into the pre-fitted computing power polynomial regression model of the data center to be analyzed and solve it to obtain the computing power of the data center to be analyzed; A second analysis module, configured to determine the computing power scheduling potential of the data center to be analyzed based on the computing power of the data center to be analyzed; The third analysis module is used to correct the computing power scheduling potential of the data center to be analyzed based on the load rate of the communication line when the time required for data transmission on the communication line exceeds a threshold.

10. A computer device, characterized in that: include: one or more processors; The processor is configured to execute one or more programs; When the one or more programs are executed by the one or more processors, the computing power scheduling potential analysis method of the power data center as described in any one of claims 1 to 8 is implemented.

11. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed, the computing power scheduling potential analysis method of the power data center as described in any one of claims 1 to 8 is implemented.

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