Cooperative scheduling method and device of electric power and computing power, computer equipment and medium

Abstract

The embodiment of the invention is suitable for the technical field of electric power, and provides an electric power and computing power cooperative scheduling method and device, computer equipment and a medium, and the method comprises the steps: obtaining the power utilization information and computing resource information of a plurality of computing power servers in a computing power network; based on the power consumption information, determining a power consumption factor of each computing power server, the power consumption factor being used for representing a power resource consumption level of the computing power server; calculating a resource vacancy rate and a resource balance rate of each computing power server based on the computing resource information; determining a scheduling value of each computing power server based on the power utilization factor, the resource vacancy rate and the resource balance rate; and determining a target computing power server for executing the computing task from the computing power servers based on the scheduling value. Through the method, cooperative scheduling of computing power and electric power can be realized.

Description

Technical Field

[0001] The present application belongs to the field of electric power technology, and in particular, relates to a method, device, computer equipment and medium for coordinated scheduling of electric power and computing power. Background Art

[0002] The execution of computing tasks requires power support from the power grid and computing power support from the computing network. The computing network may include multiple computing servers for executing computing tasks. When computing tasks need to be executed, the computing server is called for calculation, which will cause the business load of the computing network to change, and the power demand will also fluctuate, thus causing fluctuations in the power grid.

[0003] Therefore, when executing computing tasks, the scheduling of computing servers needs to consider the load of both the computing network and the power grid. Currently, when scheduling, usually only the computing resources are considered, and the coordinated scheduling of computing power and electricity cannot be achieved. Summary of the invention

[0004] In view of this, the embodiments of the present application provide a method, apparatus, computer equipment and medium for coordinated scheduling of electricity and computing power, so as to realize coordinated scheduling of computing power and electricity.

[0005] A first aspect of an embodiment of the present application provides a method for coordinated scheduling of power and computing power, including:

[0006] Obtain power consumption information and computing resource information of multiple computing servers in the computing network;

[0007] Based on the power consumption information, determine the power consumption factor of each of the computing power servers, where the power consumption factor is used to characterize the power resource consumption level of the computing power server;

[0008] Based on the computing resource information, calculate the resource idle rate and resource balance rate of each computing power server;

[0009] Determine the scheduling value of each of the computing power servers based on the power consumption factor, the resource idle rate and the resource balancing rate;

[0010] Based on the scheduling value, a target computing server for executing the computing task is determined from among the computing servers.

[0011] A second aspect of an embodiment of the present application provides a coordinated scheduling device for power and computing power, including:

[0012] The acquisition module is used to obtain the power consumption information and computing resource information of multiple computing servers in the computing network;

[0013] A power consumption factor determination module, used to determine the power consumption factor of each of the computing power servers based on the power consumption information, wherein the power consumption factor is used to characterize the power resource consumption level of the computing power server;

[0014] A resource information calculation module, used to calculate the resource idle rate and resource balance rate of each computing power server based on the computing resource information;

[0015] A scheduling value determination module, used to determine the scheduling value of each of the computing power servers based on the power consumption factor, the resource idle rate and the resource balance rate;

[0016] The target computing power server determination module is used to determine the target computing power server that executes the computing task from each of the computing power servers based on the scheduling value.

[0017] A third aspect of an embodiment of the present application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in the first aspect when executing the computer program.

[0018] A fourth aspect of 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, the method described in the first aspect above is implemented.

[0019] A fifth aspect of the embodiments of the present application provides a computer program product. When the computer program product is run on a computer device, the computer device executes the method described in the first aspect.

[0020] Compared with the prior art, the embodiments of the present application have the following advantages:

[0021] By applying the method in the embodiment of the present application, a computing power server can be determined for the computing task to be executed. The computing power network may include multiple computing power servers, and each computing power server may have power consumption information and computing resource information. Based on the power consumption information, the power consumption factor of each computing power server can be determined, and the power consumption factor can characterize the power resource consumption level of the computing power server; based on the computing resource information, the resource idle rate and resource balance rate of each computing power server can be calculated; then, based on the power consumption factor, the resource idle rate and the resource balance rate, the scheduling value of each computing power server is determined; the scheduling value can be used to characterize the scheduling priority level, so the target computing power server that executes the computing task can be determined from each computing power server based on the scheduling value. In the embodiment of the present application, when scheduling the computing power server, the power consumption information can be used to consider the volatility of the power system, the resource idle rate can be used to ensure the smooth execution of the computing task, and the resource balance rate can be used to improve the system's balance rate. In an embodiment of the present application, when scheduling the computing power server, dynamic adjustments can be made based on the power consumption information and computing resource information of the computing power server, thereby achieving coordinated scheduling of power resources and computing power resources, thereby ensuring the stability, security and load balance of the energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art.

[0023] Figure 1 It is a schematic diagram of the steps of a method for coordinated scheduling of power and computing power provided in an embodiment of the present application;

[0024] Figure 2 It is a schematic diagram of the steps of another method for coordinated scheduling of electricity and computing power provided in an embodiment of the present application;

[0025] Figure 3 is a schematic diagram of a coordinated scheduling device for power and computing power provided in an embodiment of the present application;

[0026] Figure 4 It is a schematic diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0027] In the following description, specific details such as specific system structures, technologies, etc. are proposed for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from hindering the description of the present application.

[0028] Computing power is a new type of productivity in the digital economy era. The computing power network is a key infrastructure to support the high-quality development of the digital economy. It can connect multi-source heterogeneous and massive ubiquitous computing power through the network to achieve efficient resource scheduling, green and low-carbon facilities, flexible computing power supply, and intelligent on-demand services. The computing power network can provide computing power support for privacy computing, federated learning, blockchain and other technologies. The normal operation of the computing power network requires electricity as energy support.

[0029] At present, the theories in the field of electricity are relatively mature and complete, and the theories in the field of computing power are also gradually maturing. However, the theoretical research on the synergistic integration of computing power and electricity is still in its infancy. Since the green electricity introduced by the data center of the computing power network has large fluctuations, and the business load of the data center is constantly changing, the electricity demand also fluctuates. However, the fluctuations between power generation and power consumption do not match, resulting in reduced stability and security of the energy system and more complex load balancing.

[0030] Therefore, how to utilize computing power server operations optimization technology based on computing power scenarios to efficiently and timely manage power resources such as municipal power, green power, and energy storage in a complex energy system with multiple variables to maximize the effects of safety, green energy saving, fee reduction, and revenue increase is an important research direction for the coordinated integration of computing power and electricity.

[0031] Based on this, an embodiment of the present application provides a method for coordinated scheduling of electricity and computing power to achieve coordinated scheduling of computing power and electricity.

[0032] The technical solution of the present application is described below through specific embodiments.

[0033] Reference Figure 1 , shows a schematic flow chart of the steps of a method for coordinated scheduling of power and computing power provided in an embodiment of the present application, which may specifically include the following steps:

[0034] S101, obtaining power consumption information and computing resource information of multiple computing servers in the computing network.

[0035] The execution subject of the embodiment of the present application may be a computer device. For example, the computer device may be a central scheduling device of a computing power network, and the specific type of the computer device is not limited in this embodiment.

[0036] The computing network can include multiple computing servers, each of which can be used to provide computing services. The computing network can receive computing tasks and coordinate the scheduling of power and computing power based on the computing tasks, that is, determine the computing server that executes the computing task.

[0037] The computing network can include data centers distributed in various regions, and each data center can include multiple computing servers. Since data centers are distributed in different regions, and different regions use different power resources, the electricity prices may be different. For example, in areas with more green electricity, computing servers can use green electricity as energy, and the price of green electricity may be relatively low.

[0038] In addition, computing servers may consume different amounts of electricity when performing the same computing tasks due to different configurations.

[0039] Based on the different power resources used, the amount of electricity consumed to execute the same computing task may be different, which may result in different costs for different computing servers to execute the same computing task.

[0040] The above-mentioned electricity usage information may include the power consumption of the computing server, the corresponding electricity price, etc. The above-mentioned electricity usage information can be used to characterize the electricity cost of the computing server.

[0041] Computing servers consume computing resources when performing computing tasks. Each computing server may have corresponding computing resources, which may include central processing unit (CPU), graphics processing unit (GPU), memory, disk capacity, bandwidth and other types of computing resources.

[0042] The above computing resource information may include the total amount of each type of computing resources on the computing power server and the resource occupancy of each type of computing resources.

[0043] S102: Determine a power consumption factor of each of the computing power servers based on the power consumption information, wherein the power consumption factor is used to characterize a power resource consumption level of the computing power server.

[0044] In a possible implementation, the power consumption factor of each computing server can be determined based on the power consumption information by the following formula:

[0045]

[0046] Among them, ES is the electricity consumption factor, p local is the electricity price at the location of the computing server, p max It is the highest electricity price among the electricity prices at the locations of the computing servers in the computing network.

[0047] In the above formula, the lower the electricity price at the location of the computing server, the higher the corresponding electricity consumption factor value.

[0048] The electricity price itself can be determined based on the balanced scheduling of power resources, and therefore based on the power consumption factor, it is equivalent to comprehensively reflecting the impact of the power consumption of the computing power server on the power grid.

[0049] S103: Calculate the resource idle rate and resource balance rate of each computing power server based on the computing resource information.

[0050] The computing resource information includes multiple types of computing resources. For example, computing resources may include CPU, GPU, memory, disk capacity, bandwidth and other types. When calculating the resource idle rate and resource balance rate of each computing server based on the computing resource information, the total resource amount and resource occupancy of each type of computing resources in each computing server can be obtained. Exemplarily, each computing server can send a data packet to the central scheduling device in real time to report the total resource amount and resource occupancy of each type of computing resources, so that the central scheduling device can obtain the total resource amount and resource occupancy of each type of computing resources of each computing server. In addition, the central scheduling device can also actively read the total resource amount and resource occupancy of the computing resources of the corresponding computing server.

[0051] The execution of computing tasks is supported by the idle computing resources of the computing power server. Therefore, the idle resource amount of each type of computing resource can be determined based on the total resource amount and resource usage of each type of computing resource. Exemplarily, the idle resource amount of each type of computing resource can be obtained by subtracting the resource usage from the total resource amount of each type of computing resource.

[0052] Based on the total amount of computing resources and the idle amount of each type of computing resources, the idle rate of each computing server can be calculated. The idle rate of resources can represent the amount of remaining usable computing resources on the computing server, that is, the amount of computing resources that can be provided for new computing tasks. Therefore, the idle rate of resources can be used as a scheduling basis.

[0053] In a possible implementation, the resource idle rate of each computing server can be calculated based on the total amount of each type of computing resources and the idle amount of each type of computing resources using the following formula:

[0054]

[0055] Among them, LS is the resource idle rate, i is a positive integer, A i is the total amount of computing resources of the i-th type in the computing server, F i is the idle amount of computing resources of the i-th category in the computing server, and n is the total number of computing resource categories.

[0056] In the above formula, the idle rate of the computing server is determined by the ratio of the idle resources of the computing server to the total resources of the computing server. The higher the idle rate score, the more idle resources the computing server has.

[0057] The occupancy of computing resources on the computing power server may be uneven. Unbalanced allocation of computing resources may cause more time to be spent when executing new computing tasks, which may consume more electricity. For example, when the memory usage on the computing power server is relatively high and other resources are sufficient, due to memory limitations, it is necessary to wait continuously when executing computing tasks, resulting in a waste of time. Therefore, when coordinating electricity and computing power, the resource balance rate of the computing power server can be considered. That is, the resource balance rate of each computing power server can be calculated based on the total resources and resource usage of each type of computing resources.

[0058] In a possible implementation, the resource balance rate of each computing server is calculated based on the total amount and resource usage of each type of computing resources using the following formula:

[0059] BS=1-σ(H)

[0060] H={U1 / A1,U2 / A2,…,U i / A i ,…,U n / A n}

[0061] Among them, BS is the resource balancing rate, i is a positive integer, A i is the total amount of computing resources of the i-th type in the computing server, U i is the resource usage of the i-th type of computing resources in the computing power server, and n is the total number of computing resource categories.

[0062] In the above formula, the more balanced the resources are, the lower the variance of the corresponding resource occupancy is, and thus the higher the value of the resource balance rate is.

[0063] S104: Determine the scheduling value of each of the computing power servers based on the power consumption factor, the resource idle rate and the resource balance rate.

[0064] The above scheduling values ​​can be used to represent the scheduling priority level of the computing power server.

[0065] In a possible implementation, the scheduling value of each computing server can be determined based on the power consumption factor, resource idle rate, and resource balance rate using the following formula:

[0066]

[0067] Among them, NS is the scheduling value, ES is the power consumption factor, LS is the resource idle rate, BS is the resource balancing rate, α is the weight coefficient of the power consumption factor, β is the weight coefficient of the resource idle rate, and γ is the weight coefficient of the resource balancing rate.

[0068] The above weight coefficients can be set according to different requirements. For example, when faster computing speed is required, higher weight coefficients can be set for resource idle rate and resource balance rate; when the stability of the power system needs to be maintained, higher weight coefficients can be set for the power consumption factor.

[0069] S105: Based on the scheduling value, determine a target computing server for executing the computing task from among the computing servers.

[0070] The lower the electricity price at the location of the computing server, the higher the corresponding power factor value; the more idle resources the computing server has, the higher the resource idle rate value; the more balanced the resources of the computing server, the higher the resource balanced rate value. Therefore, when the scheduling value is higher, it can be said that the combined value of the power factor, resource idle rate and resource balanced rate corresponding to the computing server is higher, so that when executing computing tasks, it can have sufficient computing resources and avoid large fluctuations in electricity. Therefore, the computing server with a higher scheduling value can be selected as the target computing server for executing computing tasks.

[0071] In the embodiment of the present application, when coordinating the power and computing power, the power consumption of the computing power server is taken into consideration, and the distribution of computing resources of the computing power server is also considered, which is equivalent to realizing the coordinating scheduling of the computing power server based on power and computing power, improving the utilization rate of resources, making the use of computing resources more balanced, and reducing the operating cost of the computing power server. In addition, the method in the embodiment of the present application introduces the power consumption factor as the scheduling basis, which can be dynamically adjusted and optimized in combination with the price fluctuations of the electricity spot market, so as to maximize the effects of safety, green energy saving, fee reduction, and revenue increase.

[0072] Reference Figure 2 , shows a schematic flow chart of another method for coordinated scheduling of power and computing power provided in an embodiment of the present application, which may specifically include the following steps:

[0073] S201, determining the resource requirements of various types of computing resources required for the computing task to be executed.

[0074] The execution subject of the embodiment of the present application may be the computer device of the previous embodiment.

[0075] The method in the present application can be executed when the computing power network receives a computing task. The execution of the computing task requires the consumption of various computing resources. For example, it requires the occupation of processors, memory, disks, bandwidth, etc. When receiving a computing task, the computer device can predict the resource demand of the computing resources to be consumed.

[0076] In a possible implementation, the computer device can obtain the computing resource usage information of the historical computing tasks in the computing power network. When receiving a new computing task, the computing resource usage information of the historical computing tasks matching the new computing task can be obtained, so as to estimate the resource demand of various types of computing resources required by the new computing task.

[0077] S202, determining a plurality of computing power servers that meet the resource demand from the power grid.

[0078] In order to enable the computing task to be successfully executed on a computing power server, a computing power server that meets the resource requirements of the computing task can be determined from the computing power network. The power grid can obtain the idle resources of various types of computing resources of each computing power server, and thus determine the computing power server that can execute the new computing task based on the idle resources of various types of computing resources of each computing power server and the resource requirements of various types of computing resources required by the new computing task.

[0079] By determining the computing power server that can execute the new computing task based on the resource demand, in the subsequent computing process, it is only necessary to calculate the scheduling value of the computing power server that can execute the new computing task, and there is no need to calculate the scheduling value of the computing power server that cannot execute the new computing task, thereby avoiding unnecessary calculation processes and saving computing resources.

[0080] S203, obtaining power consumption information and computing resource information of multiple computing servers in the computing network.

[0081] S204: Based on the power usage information, determine the power usage factor of each of the computing power servers, where the power usage factor is used to characterize the power resource consumption level of the computing power server.

[0082] S205: Calculate the resource idle rate and resource balance rate of each computing power server based on the computing resource information.

[0083] S206: Determine the scheduling value of each of the computing power servers based on the power consumption factor, the resource idle rate and the resource balance rate.

[0084] S207: Based on the scheduling value, determine a target computing server for executing the computing task from among the computing servers.

[0085] S202 - S207 in this embodiment are similar to S101 - S105 in the previous embodiment, and they can refer to each other and are not described in detail here.

[0086] If the coordinated scheduling strategy of electricity and computing power only considers the arithmetic mean of CPU and memory resource utilization, it is easy for the resource usage to be not refined enough, and an imbalance phenomenon may easily occur, that is, servers with high CPU usage and low memory usage and servers with low CPU usage and high memory usage will have the same score results, resulting in underutilization of server resources.

[0087] In an embodiment of the present application, the power consumption factor of the computing power server is used, and the status information of various types of computing resources is taken into consideration. An improved method for coordinated scheduling of electricity and computing power based on multi-dimensional resources is proposed, which takes into account resource status information of multiple dimensions, thereby effectively reducing the situation where a certain type of resource has a high load while other resources are largely idle.

[0088] It should be noted that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. 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.

[0089] Reference Figure 3 , shows a schematic diagram of a coordinated scheduling device for power and computing power provided in an embodiment of the present application, which may specifically include an acquisition module 31, a power factor determination module 32, a resource information calculation module 33, a scheduling value determination module 34 and a target computing power server determination module 35, wherein:

[0090] An acquisition module 31 is used to acquire power consumption information and computing resource information of multiple computing servers in the computing network;

[0091] A power consumption factor determination module 32, configured to determine a power consumption factor of each of the computing power servers based on the power consumption information, wherein the power consumption factor is used to characterize a power resource consumption level of the computing power server;

[0092] A resource information calculation module 33, used to calculate the resource idle rate and resource balance rate of each computing power server based on the computing resource information;

[0093] A scheduling value determination module 34, configured to determine a scheduling value of each of the computing power servers based on the power consumption factor, the resource idle rate, and the resource balancing rate;

[0094] The target computing power server determination module 35 is used to determine the target computing power server that executes the computing task from each of the computing power servers based on the scheduling value.

[0095] In a possible implementation, the power consumption factor of each computing power server is determined based on the power consumption information by the following formula:

[0096]

[0097] Wherein, ES is the electricity consumption factor, p local is the electricity price at the location of the computing server, p max It is the highest electricity price among the electricity prices at the locations of the computing servers in the computing network.

[0098] In a possible implementation, the computing resource information includes multiple types of computing resources, and the resource information computing module 33 includes:

[0099] Various resource information acquisition submodules are used to obtain the total amount and resource usage of each type of computing resources in each computing power server;

[0100] A resource idle amount determination submodule, used to determine the resource idle amount of each type of computing resource based on the total amount of resources and the resource occupancy of each type of computing resource;

[0101] A resource idle rate calculation submodule, used to calculate the resource idle rate of each computing power server based on the total amount of resources and the idle amount of resources of each type of computing resources;

[0102] The resource balance rate calculation submodule is used to calculate the resource balance rate of each computing power server based on the total resource amount and resource occupancy of each type of computing resources.

[0103] In a possible implementation, the resource idle rate of each computing power server is calculated based on the total amount of resources and idle amount of resources of each type of computing resources by the following formula:

[0104]

[0105] Where LS is the resource idle rate, i is a positive integer, A i is the total amount of computing resources of the i-th type in the computing power server, F i is the idle amount of the i-th type of computing resources in the computing power server, and n is the total number of types of computing resources.

[0106] In a possible implementation, the resource balancing rate of each computing power server is calculated based on the total amount of resources and resource usage of each type of computing resources using the following formula:

[0107] BS=1-σ(H)

[0108] H={U1 / A1,U2 / A2,…,U i / A i ,…,U n / A n}

[0109] Where BS is the resource balancing rate, i is a positive integer, and A i is the total amount of computing resources of the i-th type in the computing server, U i is the resource usage of the i-th type of computing resources in the computing power server, and n is the total number of computing resource categories.

[0110] In a possible implementation, the scheduling value of each computing power server is determined based on the power consumption factor, the resource idle rate, and the resource balancing rate by the following formula:

[0111]

[0112] Among them, NS is the scheduling value, ES is the power consumption factor, LS is the resource idle rate, BS is the resource balancing rate, α is the weight coefficient of the power consumption factor, β is the weight coefficient of the resource idle rate, and γ is the weight coefficient of the resource balancing rate.

[0113] In a possible implementation, the above device further includes:

[0114] A resource requirement determination module, used to determine the resource requirements of various types of computing resources required for the computing task to be executed;

[0115] The computing power server determination module is used to determine multiple computing power servers that meet the resource demand from the power grid.

[0116] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the method embodiment part.

[0117] Figure 4 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present application. Figure 4 As shown, the computer device 4 of this embodiment includes: at least one processor 40 ( Figure 4 Only one is shown in the figure), a memory 41, and a computer program 42 stored in the memory 41 and executable on the at least one processor 40, wherein the processor 40 implements the steps of any of the above-mentioned method embodiments when executing the computer program 42.

[0118] The computer device 4 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud computing device. The computer device may include, but is not limited to, a processor 40 and a memory 41. Those skilled in the art will appreciate that Figure 4 It is only an example of the computer device 4 and does not constitute a limitation on the computer device 4. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include input and output devices, network access devices, etc.

[0119] The processor 40 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 gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0120] In some embodiments, the memory 41 may be an internal storage unit of the computer device 4, such as a hard disk or memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the computer device 4. Further, the memory 41 may also include both an internal storage unit and an external storage device of the computer device 4. The memory 41 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program. The memory 41 may also be used to temporarily store data that has been output or is to be output.

[0121] An embodiment of the present application further 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, the steps in the above-mentioned method embodiments can be implemented.

[0122] An embodiment of the present application provides a computer program product. When the computer program product is run on a computer device, the computer device can implement the steps in the above-mentioned method embodiments when executing the computer device.

[0123] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application is described in detail with reference to the above-mentioned embodiments, a person skilled in the art should understand that the technical solutions described in the above-mentioned embodiments can still be modified, or some of the technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method for coordinated scheduling of power and computing power, characterized in that: include: Obtain power consumption information and computing resource information of multiple computing servers in the computing network; Based on the power consumption information, determine the power consumption factor of each of the computing power servers, where the power consumption factor is used to characterize the power resource consumption level of the computing power server; Based on the computing resource information, calculate the resource idle rate and resource balance rate of each computing power server; Determine the scheduling value of each of the computing power servers based on the power consumption factor, the resource idle rate and the resource balancing rate; Based on the scheduling value, a target computing server for executing the computing task is determined from among the computing servers.

2. The method according to claim 1, characterized in that The power consumption factor of each computing server is determined based on the power consumption information by the following formula: Wherein, ES is the electricity consumption factor, p local is the electricity price at the location of the computing server, p max It is the highest electricity price among the electricity prices at the locations of the computing servers in the computing network.

3. The method according to claim 1, characterized in that The computing resource information includes multiple types of computing resources, and the resource idle rate and resource balance rate of each computing server are calculated based on the computing resource information, including: Obtaining the total amount and resource usage of each type of computing resources in each of the computing power servers; Determine the idle amount of each type of computing resource based on the total amount of resources and the resource usage of each type of computing resource; Based on the total amount of resources and idle amount of each type of computing resources, calculate the idle rate of resources of each computing power server; Based on the total amount of resources and resource usage of each type of computing resources, the resource balance rate of each computing power server is calculated.

4. The method according to claim 3, characterized in that The resource idle rate of each computing server is calculated based on the total amount of computing resources and the idle amount of computing resources of each type using the following formula: Where LS is the resource idle rate, i is a positive integer, A i is the total amount of computing resources of the i-th type in the computing power server, F i is the idle amount of the i-th type of computing resources in the computing power server, and n is the total number of types of computing resources.

5. The method according to claim 3, characterized in that The resource balance rate of each computing server is calculated based on the total amount and resource usage of each type of computing resources using the following formula: BS=1-σ(H) <h2 style=";text-align:left;direction:ltr">H = {U1 / A1,U2 / A2,…,U<h2 style=";text-align:left;direction:ltr"> i <h2 style=";text-align:left;direction:ltr"> / A<h2 style=";text-align:left;direction:ltr"> i <h2 style=";text-align:left;direction:ltr"> ,…,U<h2 style=";text-align:left;direction:ltr"> n <h2 style=";text-align:left;direction:ltr"> / A<h2 style=";text-align:left;direction:ltr"> n <h2 style=";text-align:left;direction:ltr">} Where BS is the resource balancing rate, i is a positive integer, A i is the total amount of computing resources of the i-th type in the computing server, U i is the resource usage of the i-th type of computing resources in the computing power server, and n is the total number of computing resource categories.

6. The method according to claim 1, characterized in that The scheduling value of each computing power server is determined based on the power consumption factor, the resource idle rate and the resource balancing rate through the following formula: Among them, NS is the scheduling value, ES is the power consumption factor, LS is the resource idle rate, BS is the resource balancing rate, α is the weight coefficient of the power consumption factor, β is the weight coefficient of the resource idle rate, and γ is the weight coefficient of the resource balancing rate.

7. The method according to claim 1, characterized in that Before obtaining the power consumption information and computing resource information of the plurality of computing power servers in the computing power network, the method further includes: Determine the resource requirements of various types of computing resources required for the computing tasks to be performed; Determine a plurality of computing servers that meet the resource demand from the computing network.

8. A coordinated dispatching device for power and computing power, characterized in that: include: The acquisition module is used to obtain the power consumption information and computing resource information of multiple computing servers in the computing network; A power consumption factor determination module, used to determine the power consumption factor of each of the computing power servers based on the power consumption information, wherein the power consumption factor is used to characterize the power resource consumption level of the computing power server; A resource information calculation module, used to calculate the resource idle rate and resource balance rate of each computing power server based on the computing resource information; A scheduling value determination module, used to determine the scheduling value of each of the computing power servers based on the power consumption factor, the resource idle rate and the resource balance rate; The target computing power server determination module is used to determine the target computing power server that executes the computing task from each of the computing power servers based on the scheduling value.

9. A computer 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, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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