Multi-node computing network collaborative planning method for multi-temporal and spatial dimension scheduling of computing tasks

Abstract

The present invention relates to the field of computer technology, and in particular to a multi-node computing network energy collaborative planning for multi-temporal and spatial dimension scheduling of computing power tasks. The computing power multi-objective planning design of the present invention takes the minimum carbon emissions, the lowest energy consumption, and the maximum profit as the goals, and takes the various energy supply curves and prices of each node data center as boundary conditions. The energy multi-objective planning and design module takes the computing power load demand curve of each node data center as the boundary condition, and takes the maximum profit and the minimum carbon emissions during the planning period as the goals, and optimizes the energy supply facility capacity and energy transaction scale of the integrated energy system. The computing power multi-objective planning and energy multi-objective planning and design uses the multi-temporal and spatial dimension allocation of computing power tasks and the multi-node energy supply as coupling points and boundary interactions, and through multiple iterations, finally optimizes and obtains the optimal planning scheme for computing network energy integration, and realizes the multi-node computing network energy collaborative planning for multi-temporal and spatial dimension scheduling of computing power tasks.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a multi-node computing network collaborative planning method for scheduling computing tasks in multiple spatiotemporal dimensions. Background Art

[0002] Currently, multi-node computing network energy planning involves separate planning for multi-node computing network systems and multi-node energy supply systems. During the planning process, the computing network system and energy supply system for each node are unknown. Spatial and temporal allocation of computing tasks in multi-node computing network system planning requires consideration of the energy supply characteristics of each node's energy supply system. Furthermore, when planning a multi-node energy supply system, it is necessary to account for changes in the load demands of all nodes' computing network systems.

[0003] Currently, multi-node computing network energy planning is a separate process. The planning of multi-node computing network systems and energy supply systems is typically performed separately. Each node's computing network system typically proposes energy requirements after planning, and each node's energy supply system then matches these requirements. This prevents the two modules from interacting and coordinating. Therefore, it is necessary to consider the multi-level interaction and coordination of computing resources, computing tasks, load requirements, and energy supply, and propose a multi-node computing network energy collaborative planning method that considers the multi-temporal and spatial scheduling of computing tasks.

[0004] Based on this, there is an urgent need for a multi-node computing network collaborative planning for multi-temporal and multi-spatial scheduling of computing tasks to solve the technical problem of how to achieve multi-node computing network collaborative planning for multi-temporal and multi-spatial scheduling of computing tasks. Summary of the Invention

[0005] In order to solve the technical problem of how to achieve collaborative planning of multi-node computing networks for scheduling computing tasks in multiple spatiotemporal dimensions, an embodiment of the present invention provides a collaborative planning of multi-node computing networks for scheduling computing tasks in multiple spatiotemporal dimensions.

[0006] In a first aspect, an embodiment of the present invention provides a multi-node computing network capable of collaborative planning for multi-temporal and multi-spatial scheduling of computing tasks. The method is applied to a multi-node computing network and includes:

[0007] Establishing a multi-node computing power network planning model and a multi-node energy supply planning model; wherein the multi-node computing power network planning model and the multi-node energy supply planning model are both two-layer model structures, the multi-node computing power network planning model includes a first upper layer model and a first lower layer model, and the multi-node energy supply planning model includes a second upper layer model and a second lower layer model;

[0008] The first upper-layer model takes the maximum benefit and the minimum carbon emission of the multi-node data center as the objective function, and takes the latency constraint and the multi-node computing power scale constraint as the constraint conditions, determines the multi-node center computing power scale, and sends the multi-node center computing power scale to the first lower-layer model;

[0009] The first lower-layer model takes maximizing the revenue and minimizing the carbon emissions of the multi-node data center as the objective function, and takes energy constraints and multi-node computing power scale constraints as constraints, determines the carbon emissions and profits of the multi-node data center, and sends the carbon emissions and profits of the multi-node data center to the first upper-layer model;

[0010] The first upper-layer model and the first lower-layer model are repeatedly iterated to determine a first optimal solution; wherein the first optimal solution includes a multi-node computing power equipment planning scheme and a multi-node computing power energy consumption load curve, and the multi-node computing power energy consumption load curve is used as an input of the energy supply planning model to perform coupled interaction;

[0011] The second upper-layer model takes the maximization of multi-node planning period profit and the minimization of carbon emissions as the objective function, and takes the multi-node equipment planning capacity upper limit constraint and the multi-node equipment commissioning planning constraint as the constraint conditions, determines the multi-node equipment configuration strategy and planned capacity, and sends the multi-node equipment configuration strategy and planned capacity to the second lower-layer model;

[0012] The second lower-layer model uses the minimization of multi-node operating costs as the objective function, and uses multi-node power balance constraints, equipment commissioning planning constraints, energy system and upper-level network power exchange constraints, and green power ratio constraints as constraints to determine the optimal operating plan and total operating cost, and sends the optimal operating plan and total operating cost to the second upper-layer model;

[0013] The second upper-layer model and the second lower-layer model are repeatedly iterated to determine a second optimal solution; wherein the second optimal solution includes an energy supply planning scheme, a power curve of power generation equipment, a public grid thermal power curve, and a green electricity trading curve, and the power curve of power generation equipment, the public grid thermal power curve, and the green electricity trading curve are used as inputs of a multi-node computing power network planning model for coupled interaction;

[0014] The multi-node computing power network planning model and the multi-node energy supply planning model are cyclically coupled and interacted, and multi-node computing network collaborative planning for multi-temporal and spatial dimension scheduling of computing power tasks has been realized.

[0015] In a second aspect, an embodiment of the present invention further provides a multi-node computing network collaborative planning device for multi-temporal and multi-spatial scheduling of computing tasks, including:

[0016] A model construction module for establishing a multi-node computing power network planning model and a multi-node energy supply planning model; wherein the multi-node computing power network planning model and the multi-node energy supply planning model are both two-layer model structures, the multi-node computing power network planning model includes a first upper layer model and a first lower layer model, and the multi-node energy supply planning model includes a second upper layer model and a second lower layer model;

[0017] A first data processing module is configured to determine the multi-node center computing power scale based on the objective function of maximizing the multi-node data center revenue and minimizing carbon emissions, and based on the constraints of latency and multi-node computing power scale, and to send the multi-node center computing power scale to the first lower-layer model;

[0018] a second data processing module, configured for the first lower-layer model to determine the carbon emissions and profits of the multi-node data center with maximizing the revenue and minimizing the carbon emissions of the multi-node data center as objective functions, and with energy constraints and multi-node computing power scale constraints as constraints, and to send the carbon emissions and profits of the multi-node data center to the first upper-layer model;

[0019] a third data processing module configured to repeatedly iterate the first upper-layer model and the first lower-layer model to determine a first optimal solution; wherein the first optimal solution includes a multi-node computing power equipment planning scheme and a multi-node computing power energy consumption load curve, and the multi-node computing power energy consumption load curve is used as an input of the energy supply planning model for coupled interaction;

[0020] A fourth data processing module is configured for the second upper-layer model to determine a multi-node device configuration strategy and planned capacity based on the objective function of maximizing profits and minimizing carbon emissions during the multi-node planning period, and based on the upper limit constraint of the multi-node device planning capacity and the multi-node device commissioning planning constraint as constraints, and to send the multi-node device configuration strategy and planned capacity to the second lower-layer model;

[0021] A fifth data processing module is configured for the second lower-layer model to determine an optimal operation plan and total operation cost with the objective function of minimizing multi-node operation cost, and with multi-node power balance constraints, equipment commissioning planning constraints, power exchange constraints between the energy system and the upper-level network, and green power ratio constraints as constraints, and to send the optimal operation plan and total operation cost to the second upper-layer model;

[0022] a sixth data processing module configured to repeatedly iterate the second upper-layer model and the second lower-layer model to determine a second optimal solution; wherein the second optimal solution includes an energy supply planning scheme, a power curve of power generation equipment, a public grid thermal power curve, and a green electricity trading curve; and the power curve of power generation equipment, the public grid thermal power curve, and the green electricity trading curve are used as inputs of a multi-node computing power network planning model for coupled interaction;

[0023] The cyclic coupling interaction module is used for cyclic coupling interaction between the multi-node computing power network planning model and the multi-node energy supply planning model, and has realized the collaborative planning of multi-node computing network energy for multi-temporal and multi-spatial scheduling of computing power tasks.

[0024] In a third aspect, an embodiment of the present invention further provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method described in any embodiment of the present invention is implemented.

[0025] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to execute the method described in any embodiment of the present invention.

[0026] An embodiment of the present invention provides a multi-node computing network collaborative planning for scheduling computing tasks in multiple time and space dimensions. When the multi-node computing network is collaboratively planned, each node data center establishes an integrated energy system (electricity-cold-heat-hydrogen-gas, etc.) and a computing network equipment construction plan with optimal economy and lowest carbon emissions based on resource endowment. The computing power multi-objective planning and design aims to minimize carbon emissions, minimize energy consumption, and maximize profits. It uses the various energy supply curves and prices of each node data center as boundary conditions to optimize the timing of computing power tasks such as interactive workloads and batch workloads and optimize the spatial allocation of multi-node data centers. The energy multi-objective planning and design module uses the computing power load demand curve of each node data center as a boundary condition, and aims to maximize profits and minimize carbon emissions during the planning period to optimize the capacity of energy supply facilities and the scale of energy transactions of the integrated energy system. The multi-objective planning and design of computing power and the multi-objective planning and design of energy use the multi-temporal and spatial dimensional allocation of computing power tasks and the multi-node energy supply as coupling points and boundary interactions. Through multiple iterations, the optimal planning scheme for computing network energy integration is finally optimized. It can overcome the inconsistency problem of the configuration scheme of traditional independent planning, achieve a high degree of coupling between multi-temporal and spatial computing power tasks and renewable energy power generation, improve the renewable energy absorption rate, reduce the energy consumption cost of data centers, and realize multi-level interaction of computing power resources-computing power tasks-load demand-energy supply, so as to achieve the optimal overall configuration scheme of multi-node computing network energy, and realize the multi-node computing network energy collaborative planning of multi-temporal and spatial dimensional scheduling of computing power tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0028] Figure 1 This is a multi-node computing network collaborative planning flow chart for multi-temporal and multi-spatial scheduling of computing tasks provided by an embodiment of the present invention;

[0029] Figure 2 This is a hardware architecture diagram of an electronic device provided by an embodiment of the present invention;

[0030] Figure 3 This is a structural diagram of a multi-node computing network collaborative planning device for scheduling computing tasks in multiple time and space dimensions provided by an embodiment of the present invention;

[0031] Figure 4 This is a structural diagram of a multi-node energy supply planning model provided by an embodiment of the present invention;

[0032] Figure 5 This is a structural diagram of a multi-node computing power network planning model provided by an embodiment of the present invention;

[0033] Figure 6 This is a multi-node computing network collaborative planning architecture diagram for scheduling computing tasks in multiple time and space dimensions provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions 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 the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0035] Please refer to Figure 1 An embodiment of the present invention provides a multi-node computing network collaborative planning method for scheduling computing tasks in multiple spatiotemporal dimensions, the method comprising:

[0036] Step 100: Establish a multi-node computing power network planning model and a multi-node energy supply planning model; wherein the multi-node computing power network planning model and the multi-node energy supply planning model are both two-layer model structures, the multi-node computing power network planning model includes a first upper layer model and a first lower layer model, and the multi-node energy supply planning model includes a second upper layer model and a second lower layer model;

[0037] Step 102: The first upper-layer model uses the maximum revenue and minimum carbon emissions of the multi-node data center as the objective function, and uses the latency constraint and the multi-node computing power scale constraint as the constraint conditions to determine the multi-node center computing power scale, and sends the multi-node center computing power scale to the first lower-layer model;

[0038] Step 104: The first lower-layer model uses maximizing the revenue and minimizing the carbon emissions of the multi-node data center as its objective function, and uses energy constraints and multi-node computing power scale constraints as constraints to determine the carbon emissions and profits of the multi-node data center. The carbon emissions and profits of the multi-node data center are then sent to the first upper-layer model.

[0039] Step 106: The first upper-layer model and the first lower-layer model are repeatedly iterated to determine a first optimal solution; wherein the first optimal solution includes a multi-node computing power equipment planning scheme and a multi-node computing power energy consumption load curve, and the multi-node computing power energy consumption load curve is used as an input of the energy supply planning model for coupled interaction;

[0040] Step 108: The second upper-layer model uses the maximum profit and minimum carbon emissions during the multi-node planning period as the objective function, and uses the multi-node equipment planning capacity upper limit constraint and the multi-node equipment commissioning planning constraint as constraints to determine the multi-node equipment configuration strategy and planned capacity, and sends the multi-node equipment configuration strategy and planned capacity to the second lower-layer model;

[0041] Step 110: The second lower-layer model uses minimizing the multi-node operating cost as the objective function, and uses the multi-node power balance constraint, the equipment commissioning planning constraint, the power exchange constraint between the energy system and the upper-level network, and the green power ratio constraint as constraints to determine the optimal operating plan and total operating cost. The optimal operating plan and total operating cost are sent to the second upper-layer model.

[0042] Step 112: The second upper-layer model and the second lower-layer model are repeatedly iterated to determine a second optimal solution; wherein the second optimal solution includes the energy supply planning scheme, the power curve of the power generation equipment, the public grid thermal power curve, and the green power trading curve. The power curve of the power generation equipment, the public grid thermal power curve, and the green power trading curve are used as inputs of the multi-node computing power network planning model for coupled interaction.

[0043] Step 114: The multi-node computing network planning model and the multi-node energy supply planning model are cyclically coupled and interacted, and the multi-node computing network collaborative planning for multi-temporal and spatial dimension scheduling of computing tasks has been realized.

[0044] In an embodiment of the present invention, a multi-node computing power network planning model and a multi-node energy supply planning model are established; wherein the multi-node computing power network planning model and the multi-node energy supply planning model are both two-layer model structures, the multi-node computing power network planning model includes a first upper layer model and a first lower layer model, and the multi-node energy supply planning model includes a second upper layer model and a second lower layer model;

[0045] The first upper-level model takes the maximum profit and minimum carbon emission of multi-node data centers as the objective function, and takes the delay constraint and multi-node computing power scale constraint as the constraint conditions, determines the multi-node center computing power scale, and sends the multi-node center computing power scale to the first lower-level model; the first lower-level model takes the maximum profit and minimum carbon emission of multi-node data centers as the objective function, and takes the energy constraint and multi-node computing power scale constraint as the constraint conditions, determines the carbon emissions and profit of the multi-node data center, and sends the carbon emissions and profit of the multi-node data center to the first upper-level model; the first upper-level model and the first lower-level model are repeatedly iterated to determine the first optimal solution; wherein, the first optimal solution includes the multi-node computing power equipment planning scheme and the multi-node computing power energy consumption load curve, and the multi-node computing power energy consumption load curve is used as the input of the energy supply planning model for coupling interaction; the second upper-level model takes the maximum profit and minimum carbon emission in the multi-node planning period as the objective function, and takes the multi-node equipment planning capacity upper limit constraint and the multi-node equipment commissioning planning constraint as the constraint conditions Constraint conditions are set to determine the multi-node equipment configuration strategy and planned capacity, and send the multi-node equipment configuration strategy and planned capacity to the second lower-level model; the second lower-level model takes the minimization of multi-node operation cost as the objective function, and takes the multi-node power balance constraint, equipment commissioning planning constraint, energy system and upper network power exchange constraint, and green electricity proportion constraint as constraints to determine the optimal operation plan and total operation cost, and send the optimal operation plan and total operation cost to the second upper-level model; the second upper-level model and the second lower-level model are repeatedly iterated to determine the second optimal solution; among which, the second optimal solution includes the energy supply planning scheme, the power curve of power generation equipment, the public grid thermal power curve and the green electricity trading curve, and the power curve of power generation equipment, the public grid thermal power curve and the green electricity trading curve are used as the input of the multi-node computing power network planning model for coupling interaction; the multi-node computing power network planning model and the multi-node energy supply planning model are cyclically coupled and interacted, and the multi-node computing network collaborative planning of multi-temporal and spatial dimension scheduling of computing power tasks has been realized.

[0046] Described below Figure 1 How to perform the steps shown.

[0047] Regarding step 102:

[0048] In one embodiment of the present invention, the computing power scale constraint: Meaning: The total computing power scale needs to consider the sum of the computing power scales corresponding to delay-tolerant and delay-sensitive requests. The computing power scale of the sensitive request at time t is K′m (t), the computing power scale at time t of the tolerant request is K m (t). The final computing power scale of the data center can be expressed as:

[0049]

[0050] C2: K′≤K max

[0051] Where: Constraints indicate that the task processing time must be before the tolerable time point and the data center size cannot exceed the maximum computing resource capacity. max Indicates the maximum computing capacity that a data center can support.

[0052] In one embodiment of the present invention, the objective function is to maximize the revenue of a multi-node data center and minimize carbon emissions, including the following formula:

[0053]

[0054]

[0055] S t,c =C t,c ·(E t,nom -E t,co2 ) / 10000

[0056]

[0057] f i,ntp =f i,tp (1-λ int )=(f i,ep -f i,ep λ vat λ umt -f i,ep λ vat λ est )(1-λ int )

[0058] f i,ep =f i,in -f i,c

[0059]

[0060] Where, The number of energy supply production equipment put into operation in year t; The capacity of the selected model of production equipment for the i-th energy supply; is the number of the i-th energy storage device put into operation in year t; is the capacity of the model selected for the i-th energy storage device; C invis the initial investment cost; T is the life cycle; r is the capital discount rate; is the investment cost in year t; α lr,t is the loan ratio in year t; i is the number of energy supply production equipment; Ω1 is the set of energy supply production equipment; The unit capacity investment cost of the production equipment for the i-th energy supply; is the investment and construction capacity of the i-th energy supply production equipment in year t; j is the energy storage equipment number; Ω2 is the energy storage equipment set; is the unit capacity investment cost of the j-th energy storage device; is the investment and construction capacity of the j-th energy storage equipment in year t, The annual operation and maintenance cost per unit capacity of the production equipment supplying the i-th energy source; is the annual operation and maintenance cost per unit capacity of the j-th energy storage device, is the purchase cost of thermal power from the public grid in year t, is the medium- and long-term transaction cost of the i-th green electricity in year t, is the normal green electricity purchase cost in year t, is the penalty cost for the deviation of the i-th green power medium- and long-term transaction in the t-th year, is the natural gas purchase cost in year t, is the hydrogen purchase cost in year t, S is the number of typical daily scenarios per year; N s is the duration of the typical day scene s in a year; H is the total number of time periods in a day, C e,t,s,h P is the unit price of electricity purchased from the public grid during the h period of the typical day scenario in year t; e,t,s,h is the amount of electricity purchased from the public grid during the h period of the typical day scenario in year t, C le,t,i,s,h P is the purchase price of green electricity in the i-th medium- and long-term transaction in the h-th period of the typical day scenario in the t-th year; le,t,i,s,h C is the purchase amount of green electricity in the i-th medium- and long-term transaction in the h-th period of the typical day scenario in the t-th year, ge,t,s,h P is the normal green electricity purchase price for the typical day scenario h period in year t; ge,t,s,h is the normal green electricity purchase amount in the h period of the typical day scenario in year t, C g,t,s,h G is the natural gas purchase price in the typical day scenario h period in year t; g,t,s,h is the natural gas purchase amount in the typical day scenario h period in year t, is the unit price of hydrogen purchased during the h period of the typical day scenario in year t; is the amount of hydrogen purchased in the typical day scenario h period in year t, C le,t,i,s,h,dp The penalty price for deviation of green electricity medium- and long-term transaction in the i-th green electricity medium- and long-term transaction in the h-th period of the typical day scenario in the t-th year; P le,t,i,s,h,dp P is the deviation of the i-th green electricity medium- and long-term transaction caused by the responsibility of the power purchaser in the h-th period of the typical day scenario in the t-th year;lest,t,i,s,h The transaction volume of the i-th green power medium- and long-term trading contract in the h-th period of the typical day scenario in the t-th year; P legm,t,i,s,h The amount of green electricity provided by medium- and long-term transactions in the i-th period of time in the typical day scenario of the t-th year is C fc,t is the financial cost in year t; r i,tt is the annual loan interest rate for the loan in year tt; C inv,tt is the investment cost in year tt; α lr,tt is the loan ratio in year tt; N tt is the repayment period of the loan in year tt, SR t is the energy supply income in year t, C e,t,s,h,S are the electricity selling price in the typical day scenario h period in year t; P eL,t,s,h P is the other electric load in the typical day scenario h period in year t; cpeL,t,s,h HPR is the power load consumed by computing equipment in the typical day scenario h period in year t; FJ is the heating price per unit area; CPR FJ is the cooling price per unit area; A r,HFJ is the heating area; A r,CFJ is the cooling area, S t,up is the surplus access income in year t; S is the number of typical daily scenarios each year; N s is the duration of the typical day scene s in a year; H is the total number of time periods in a day, C up,e,t,s,h P is the unit price of surplus electricity on the grid in the h period of the typical day scenario in year t; up,e,t,s,h is the on-grid power consumption in the h period of the typical day scenario in year t, S inv Subsidize total investment; is the investment subsidy for year t; i is the number of the energy supply production equipment; Ω1 is the set of energy supply production equipment; Subsidy for initial investment per unit capacity of the i-th energy supply production equipment; is the investment and construction capacity of the i-th energy supply production equipment in year t; j is the energy storage equipment number; Ω2 is the energy storage equipment set; is the initial investment subsidy per unit capacity of the jth energy storage device; is the investment capacity of the j-th energy storage equipment in year t, S t,sub is the power generation subsidy income in year t; S is the number of typical day scenarios each year; N s is the duration of the typical day scene s in a year; H is the total number of time periods in a day, C wt,e,t,s,h 、C pv,q,t,s,h are the electricity subsidies for wind power generation and photovoltaic power generation in the h period of the typical day scenario in year t; P wt,e,t,s,h 、P pv,e,t,s,hare the wind power generation and photovoltaic power generation in the typical day scenario h period in year t, S t,c is the environmental benefit in year t; C t,c is the carbon trading price in year t; E t,nom is the carbon quota for year t; E t,co2 is the carbon dioxide emissions in year t; The carbon dioxide emission coefficient generated by the consumption of electricity; S is the carbon dioxide emission coefficient generated by the i-th energy supply production equipment that consumes natural gas. t,i,le,dp is the deviation income of the i-th green power medium- and long-term transaction in the t-th year, C le,t,i,s,h,dp The penalty price for deviation of green electricity medium- and long-term transaction in the i-th green electricity medium- and long-term transaction in the h-th period of the typical day scenario in the t-th year; P le,t,i,s,h,dp P is the deviation of the i-th green electricity medium- and long-term transaction caused by the responsibility of the power purchaser in the h-th period of the typical day scenario in the t-th year; lest,t,i,s,h The transaction volume of the i-th green power medium- and long-term trading contract in the h-th period of the typical day scenario in the t-th year; P legm,t,i,s,h The amount of green electricity provided by medium- and long-term transactions in the i-th period of time in the typical day scenario of the t-th year is E RV is the total residual value of the integrated energy system equipment at the end of the planning period, M n is the total number of equipment in the integrated energy system, δ i is the residual value rate of the i-th equipment, C INV,i is the initial investment of the i-th device, N is the number of nodes, f i,ntp is the net profit of the energy system of the ith node throughout its life cycle, f i,tp is the total profit of the entire life cycle of the i-th node, λ int is the income tax rate, f i,ep is the economic benefit of the entire life cycle of the i-th node, λ vat is the value-added tax rate, λ umt Maintaining tax rates for urban construction, λ est is the education surcharge rate, f i,in is the total life cycle benefit of the integrated energy system of the i-th node, f i,c is the total investment cost of the integrated energy system at the i-th node, f i,t,in is the total revenue of the integrated energy system at the i-th node in year t, SR i,t is the energy supply income of the i-th node in the t-th year, S i,t,up is the surplus grid access income of the i-th node in the t-th year, is the investment subsidy for the i-th node in the t-th year, S i,t,sub is the power generation subsidy income of the i-th node in the t-th year, S i,t,vs is the value-added service income of the i-th node in the t-th year, S i,t,c is the environmental benefit of the i-th node in the t-th year, Si,t,le,dp The deviation income of green electricity medium- and long-term transaction in the tth year at the i-th node, E i,RV is the total residual value of the integrated energy system equipment at the end of the planning period of the i-th node, is the initial investment cost of the integrated energy system at the i-th node in the t-th year, α lr, t is the loan ratio in year t, is the equipment maintenance cost of the i-th node in the t-th year, is the energy purchase cost of the i-th node in the t-th year, C i,fc,t is the financial cost of the i-th node in the t-th year, N is the number of nodes, f i,e is the life cycle carbon dioxide emissions of the energy system of the i-th node; T is the planning period, that is, the total planning period; is the CO2 emission of the energy system of the ith node in year t; S is the number of typical daily scenarios per year; N s is the duration of the typical day scene s in a year; H is the total number of time periods in a day; The carbon dioxide emission coefficient generated by consuming public grid electricity; is the emission coefficient of carbon dioxide produced by natural gas; P i,e,t,s,h G is the amount of electricity purchased from the public grid during the h period of the typical day scenario in year t; i,g,t,s,h is the natural gas purchase volume in the h period of the typical day scenario in year t.

[0061] In one embodiment of the present invention, multi-node data center planning involves regulation between data centers, and the decision is to determine the selection of computing nodes and time and further obtain the computing power scale of each data center and the server configuration of each data center.

[0062] Meaning: Time within a day can be expressed as The task arriving at time slot t can be expressed as:

[0063]

[0064] Where:

[0065] As can be seen from 3.2.3 Task Feature Model;

[0066] x∈{delay-tolerant, delay-sensitive}, indicating the type of tasks classified according to their sensitivity to delay;

[0067] sce∈{working day, holiday, weekend}, represents a typical day divided by the amount of computation;

[0068] I represents the number of routers, i = [1, I] represents the router number;

[0069] Indicates a task The corresponding computing power resource requirements;

[0070] Indicates a task The corresponding network resource requirements;

[0071] Indicates a task The corresponding energy resource demand;

[0072] N i，x represents the number of tasks of type x that arrive at router i,

[0073] n=[1,N i，x ] indicates the number of a specific task;

[0074] t∈{1, 2, ..., 24}, represents the arrival time slot. Since multiple data centers require spatial and temporal scheduling, the spatial decision variable of the request is expressed as:

[0075]

[0076] Because a request can only be dispatched to one data center,

[0077]

[0078] Confirm the request The scheduling decision for forwarding time is

[0079]

[0080] Delay Constraints

[0081]

[0082] Meaning: The total latency of each task is defined as the sum of transmission delay, propagation delay and processing delay.

[0083] The delay under scheduling decisions X, Z can be expressed as:

[0084]

[0085] Where: is the transmission delay, that is, the task is summed up through the servers on the transmission path, is the propagation delay (actual physical distance divided by the speed of light), and is the computational delay. The computational delay of each task can be expressed as:

[0086]

[0087] Where: Indicates the computing power obtained by each task, Num task (t) is the number of tasks in time slot t. gen (FLOPS) is the computing resource of the general-purpose server, and Num is the number of general-purpose servers in the data center.

[0088] Regarding step 104:

[0089] In one embodiment of the present invention, the energy supply constraint is: C2:E total ≤Q total

[0090] Where: Q total It is the total amount of energy that can be provided by the energy supplier.

[0091] Regarding step 106:

[0092] like Figure 5 As shown, in one embodiment of the present invention, the upper-level model is the main model for computing system planning. The decision variables are task allocation strategy and data center computing power scale strategy. Its objective function is to minimize carbon emissions and electricity prices. The lower-level model is a sub-model of the upper-level model, which is used to solve the sub-problem of optimizing computing power equipment construction in the computing power system. Based on the differences in cost, revenue, environmental protection, etc. of different computing power equipment, the computing power scale of each computing power equipment is coordinated to jointly process tasks. The data center computing power scale of the upper-level model is passed to the lower-level model as the boundary condition of the lower-level model.

[0093] (1) Upper model

[0094] The upper-level model is the main model for computing system planning. The decision variables are task allocation strategy and data center computing scale strategy. Its objective function is to minimize carbon emissions and electricity prices, as shown below.

[0095] The last generation normalizes the economic and environmental goals by adding weighted values ​​and summing them up. The comprehensive objective function is as follows.

[0096]

[0097] ω1+ω2=1

[0098] In the formula, ω1 and ω2 are weight coefficients, is the normalized value of the economic objective of the i-th solution in the last generation, is the normalized value of the environmental protection target of the i-th solution in the last generation, is the maximum value of the economic objectives among all solutions of the last generation, is the minimum value of the economic objective among all solutions of the last generation, is the maximum value of the environmental protection target among all solutions in the last generation, is the minimum value of the environmental protection target among all solutions of the last generation, and M is the number of solutions of the last generation.

[0099] After the upper-level model determines the scale of the data center, it is transmitted to the lower-level model to complete the configuration of the computing equipment in the data center.

[0100] (2) Lower-level model

[0101] The lower-level model is a submodel of the upper-level model, used to solve the subproblem of optimizing computing equipment deployment within the computing system. It coordinates the joint processing of tasks across different computing equipment based on their differences in cost, revenue, and environmental performance. The data center computing capacity scale of the upper-level model is passed to the lower-level model as its boundary condition.

[0102] The last generation normalizes the economic and environmental goals by adding weighted values ​​and summing them up. The comprehensive objective function is as follows.

[0103]

[0104] ω1+ω2=1

[0105] In the formula, ω1 and ω2 are weight coefficients, is the normalized value of the economic objective of the i-th solution in the last generation, is the normalized value of the environmental protection target of the i-th solution in the last generation, is the maximum value of the economic objectives among all solutions of the last generation, is the minimum value of the economic objective among all solutions of the last generation, is the maximum value of the environmental protection target among all solutions in the last generation, is the minimum value of the environmental protection target among all solutions of the last generation, and M is the number of solutions of the last generation.

[0106] During each optimization process, the upper-level model optimizes the computing power scale of the data center, and the lower-level model uses this as the boundary condition to optimize the optimal computing power configuration of the data center.

[0107] Regarding step 108:

[0108] In one embodiment of the present invention, the maximum profit of the multi-node planning period is determined by the following formula:

[0109]

[0110]

[0111] TrueProfit Total =Profit Total *(1-λ)

[0112] In the formula, CostDevice Cost Device,l represents the total initial equipment acquisition cost of data center l; Indicates the number of type a CPU computing devices in data center l; represents the number of b types of GPU computing devices in data center l; represents the number of c types of network devices in data center l; represents the number of d types of cabinets in data center l; represents the price of type a CPU computing power equipment in data center l; represents the price of b types of GPU computing devices in data center l; represents the price of c types of network equipment in data center l; represents the price of d types of cabinet equipment in data center l, where represents the planned equipment maintenance cost in year t; Planned equipment maintenance costs for data center l in year t; Indicates the number of type a CPU computing devices in data center l; represents the number of b types of GPU computing devices in data center l; represents the number of c types of network devices in data center l; represents the number of d types of cabinets in data center l; represents the maintenance cost of type a CPU computing equipment in data center l in year t; represents the maintenance cost of b types of GPU computing equipment in data center l in year t; represents the maintenance cost of c types of network equipment in data center l in year t; represents the maintenance cost of d types of cabinet equipment in data center l in year t, represents the annual equipment electricity consumption cost; represents the annual equipment power consumption cost of data center l; represents the heating price per unit area of ​​data center l; represents the heating area of ​​data center l; represents the cooling price per unit area of ​​data center l; represents the cooling area of ​​data center l; represents the energy efficiency coefficient of data center l in different seasons, where The default value is 1.05; represents the terminal power supply price of data center l in year t; P l task P represents the energy consumption of the computing power request equipment of data center l in year t; l idle represents the basic energy consumption of the equipment in data center l in year t, Coste，total is the total electricity price,

[0113] T cycle is the life cycle in years, is the total electricity price of the data center corresponding to the typical day scei, T scei is the number of days of typical daily scei, is the total electricity price of the data center in one day, The electricity price of data center m in time slot t, represents the electricity price of data center m, P m (t) represents the electricity price of data center m at time t, S t,vs is the value-added service income in year t; β vs,l The value-added service revenue ratio coefficient of the data center; is the comprehensive energy purchase cost of the data center, including: Indicates the total cost of the data center; Total cost of data center l; Energy t,l Indicates the power consumption of all devices in the data center; Expense t,l Cost represents the construction cost of data center l. Inv Indicates the initial investment cost; Cost Inv,l Cost represents the initial investment cost of data center l; Device,l represents the total initial equipment acquisition cost of data center l; represents the total construction cost of data center l; considering the delay in project planning and construction, the discount rate needs to be increased, r is the capital discount rate; α l is the loan ratio of data center l, is the financial cost in year t; is the financial cost of data center l in year t; rate l The annual loan interest rate for the data center 1 loan; is the investment cost of data center l; α l is the loan ratio of data center l; lim l is the repayment period of the data center loan, Cost Total Plan the total cost of the integrated computing network system; Cost Total,l is the total planning period cost of the data center l of the integrated computing network system, where Income t Indicates the computing power income in the tth year; Income t,l represents the computing power income of data center l in year t; represents the unit price of computing power in data center l in year t; represents the maximum computing power demand at center l in year t, where Residual is the total residual value of computing network system equipment at the end of the planning period, H is the total number of equipment in the computing network system and H = A + B + C + D, β i is the residual value rate of the i-th equipment, Price i is the initial purchase price of the i-th device, where Income Total is the total income of the network system over its entire life cycle; r is the capital discount rate,

[0114] To calculate the economic benefits of the network system throughout its life cycle, TrueProfit Total is the total net profit over the entire life cycle; λ is the income tax rate.

[0115] The upper limit constraint of multi-node device planning capacity is determined by the following formula:

[0116]

[0117] Where, The investment and construction capacity of the i-th energy supply production equipment of the n-th node data center in year t, is the investment and construction capacity of the j-th energy storage device in the n-th node data center in year t, are the planned capacity upper limits of energy production equipment and energy storage equipment of the nth node data center in year t.

[0118] Regarding step 110:

[0119] In one embodiment of the present invention, the multi-node power balance constraint includes an electric power balance constraint, a thermal power balance constraint, a cooling power balance constraint, a natural gas power balance constraint, and a hydrogen power balance constraint;

[0120] The multi-node power balance constraint is determined by the following formula:

[0121]

[0122]

[0123] Where n represents the nth node, N is the number of nodes, Ω4 is the set of energy supply and production equipment that generates electricity; Ω5 is the set of energy storage equipment; Ω6 is the set of energy supply and production equipment that consumes electricity; P n,e,t,s,h P is the amount of electricity purchased from the public grid by the data center of the nth node in the typical day scenario of the tth year and the hth period; n,le,t,i,s,h P is the mid- to long-term green electricity purchase amount of the n-th node data center in the i-th typical day scenario h period in the t-th year; n,ge,t,s,hP is the normal green electricity purchase amount of the nth node data center in the typical day scenario h period in the tth year; n,e,m,t,s,h The power generated by the energy supply production equipment of the mth energy generating power in the typical day scenario of the nth node data center in the tth year and the hth period; P n,e,j,ES-dis,t,s,h P is the discharge power of the jth storage device in the typical day scenario h period in the tth year in the nth node data center; n,e,k,t,s,h P is the power consumed by the kth energy supply device consuming electricity in the nth node data center in the typical day scenario h period of the tth year; n,eL,t,s,h P is the other electrical load of the data center of the nth node in the typical day scenario of the tth year and the hth period; n,e,j,ES-ch,t,s,h P is the charging power of the jth energy storage device in the nth node data center in the tth year s typical day scenario h period; n,cpeL,t,s,h is the power load consumed by the computing equipment in the data center of the nth node during the typical day scenario h in the tth year; Ω7 is the set of energy supply and production equipment that generates heat energy; Ω8 is the set of heat storage equipment; Ω9 is the set of energy supply and production equipment that consumes heat energy; Q n,q,i,t,s,h The thermal power generated by the energy supply production equipment of the i-th heat-generating device in the n-th node data center in the typical day scenario of the t-th year and the h-th period; Q n,q,j,HS-dis,t,s,h Q is the heat release power of the jth heat storage device in the nth node data center in the tth year s typical day scenario h period; n,q,k,t,s,h Q is the heat load of the energy supply production equipment of the kth energy consumption heat energy in the typical day scenario h period of the nth node data center in the tth year; n,qL,t,s,h Q is the heat load of the data center of the nth node in the typical day scenario of the tth year and the hth period; n,q,j,HS-ch,t,s,h is the charging power of the jth heat storage device in the nth node data center in the tth year s typical day scenario h period, Ω 10 A collection of energy supply and production equipment for generating cold energy; 11 A collection of cold storage equipment; C n,c,i,t,s,h C is the cooling power generated by the energy supply production equipment of the i-th cooling energy generating device in the typical day scenario of the n-th node data center in the t-th year and the h-th period; n,c,j,CS-dis,t,s,h C is the cooling power of the jth cold storage device in the nth node data center in the tth year s typical day scenario h period; n,cL,t,s,h C is the cooling load of the data center of the nth node in the h-th period of the typical day scenario of the t-th year; n,c,j,CS-ch,t,s,h is the cooling power of the jth cold storage device in the nth node data center in the tth year s typical day scenario h period, Ω3 is the set of energy supply production equipment that consumes natural gas; 12 G is a collection of gas storage equipment; n,g,t,s,h G is the natural gas power purchased by the data center of the nth node in the typical day scenario of the tth year and the hth period; n,g,i,GS-dis,t,s,hG is the gas discharge power of the jth gas storage device in the nth node data center in the tth year s typical day scenario h period; n,g,j,t,s,h The natural gas power of the energy supply production equipment of the jth natural gas consumption in the typical day scenario h period of the nth node data center in the tth year; G n,g,i,GS-ch,t,s,h is the filling power of the jth gas storage device in the nth node data center in the tth year s typical day scenario h period, Ω 14 A collection of energy supply and production equipment for consuming hydrogen; 13 Assemble for hydrogen storage equipment; The amount of hydrogen purchased by the nth node data center during the h-hour period in the s-day typical scenario in the t-th year; The hydrogen discharge power of the jth hydrogen storage device in the nth node data center in the tth year s typical day scenario h period; The hydrogen power of the energy supply production equipment for the jth energy consumption of hydrogen energy in the typical day scenario h period of the tth year s of the nth node data center; is the hydrogen charging power of the jth hydrogen storage device in the typical day scenario h period in the tth year in the nth node data center.

[0124] In one embodiment of the present invention, the multi-node equipment operation planning constraint is determined by the following formula:

[0125]

[0126] 1≤n≤N

[0127] Where, The minimum value of the green electricity medium- and long-term transaction power supply of the i-th node data center; The maximum power consumed by the kth energy supply device consuming electricity in the nth node data center in year t; The maximum thermal power of the energy supply production equipment for the kth energy consumption of heat energy in the nth node data center in the tth year; The maximum natural gas power of the energy supply production equipment for the jth natural gas consumption in the nth node data center in year t; The maximum hydrogen power of the energy supply production equipment for the jth hydrogen-consuming energy in the nth node data center in the tth year; are the rated capacities of the electricity, heat, and cooling energy production equipment of the nth node data center in year t, They are the peak values ​​of other power loads, power load consumed by computing equipment, heat load, and cooling load of the nth node data center in year t.

[0128] In one embodiment of the present invention, the power exchange constraint between the energy system and the upper-level network is determined by the following formula:

[0129]

[0130] Where, are the minimum and maximum power purchased from the public grid thermal power of the nth node data center in year t; are the minimum and maximum purchased natural gas power for the nth node data center in year t; The minimum and maximum purchased hydrogen power for the nth node data center in year t respectively; are the minimum and maximum green electricity purchase power of the nth node data center in year t; P n,legm,t,i,s,h It is the amount of green electricity provided by the data center of the nth node in the medium- and long-term transaction during the i-th period of the typical day scenario of the t-th year and the h-th period.

[0131] Green electricity ratio constraints

[0132] GE n,rate ≥GE n,rate_nor

[0133] In the formula, GE n,rate is the green power ratio of the nth node data center; GE n,rate_nor The lower limit of the green power ratio of the nth node data center.

[0134] Regarding step 112:

[0135] like Figure 4 As shown, in one embodiment of the present invention, it can be seen from the above model that the decision variables include equipment configuration strategy and system operation optimization variables. The system operation optimization problem takes the system equipment configuration strategy as the boundary condition. It is difficult to optimize both at the same time. It is necessary to split and transform the two parts of the variables. Considering the logical relationship of the variables, the equipment configuration strategy can be determined first, and then the system operation optimization decision is made on this basis. The optimal solution is output through repeated iterations. It can be seen that the two-layer optimization model is suitable for solving the planning model of this project. The original optimization model is converted into a multi-objective two-layer joint optimization configuration model for the integrated energy system. The upper model is the main model, which is an equipment selection and capacity optimization model. It is used to select the optimal equipment configuration combination from the set of candidate equipment, with the goal of minimizing total cost and pollutant gas emissions; the lower model is a sub-model, which is an operation scheduling optimization model. The equipment configuration plan determined by the upper model is used as a boundary condition to formulate the optimal plan for the output of each device under the premise of meeting the regional energy supply and demand balance, with the goal of minimizing total operating cost. The upper-level model optimizes the configuration strategies and planned capacities of various types of equipment and passes them to the lower-level model. The lower-level model optimizes the scheduling and operation of the integrated energy system and returns the optimal operation plan and total operating cost to the upper-level model. Through the optimization iteration of the upper and lower levels, the optimal configuration strategy of the energy system is obtained.

[0136] (1) Upper model

[0137] The upper-level model is the main model for energy system planning. The decision variables are the configuration and planned capacity of various types of equipment, namely the investment capacity of energy supply and production equipment, the investment capacity of energy storage equipment, and the rated power of energy storage equipment. Its objective function is to minimize the total planning cost and the emission of pollutants, as shown below.

[0138] By adding weighted values ​​and normalizing the economic and environmental goals and then summing them up, the comprehensive objective function is as follows.

[0139]

[0140] ω1+ω2=1

[0141] In the formula, ω1 and ω2 are weight coefficients, is the normalized value of the economic objective of the i-th solution in the current iteration, is the normalized value of the environmental protection target of the i-th solution in the current iteration, max i (f i,ntp ) is the maximum value of the economic objective among all solutions in the current iteration, min i (f i,ntp ) is the minimum value of the economic objective among all solutions in the current iteration, max i (f i,e ) is the maximum value of the environmental protection target among all solutions in the current iteration, min i (f i,e ) is the minimum value of the environmental protection target among all solutions in the current iteration, and M is the number of solutions in the current iteration.

[0142] After the lower-level model determines the operating strategy, it is transmitted to the upper-level model to complete the cost and benefit calculation related to the operating strategy.

[0143] (2) Lower-level model

[0144] The lower-level model is a submodel of the upper-level model, solving the subproblem of optimizing the scheduling of various energy supply and production equipment and energy storage devices within the energy system. Based on the varying supply and demand of different energy sources, it coordinates the operation of various energy supply and production equipment and the charging and discharging of energy storage devices to meet diverse energy demands. The configuration and planned capacity of various equipment generated by the upper-level model are transferred to the lower-level model as boundary conditions. Its objective function is to minimize total operating cost, as shown below.

[0145]

[0146] In the nth iteration of the optimization process, the upper-level model optimizes the configuration and planned capacity of various equipment and passes it to the lower-level model. The lower-level model uses this as the boundary condition to optimize the optimal scheduling operation plan, and returns to the upper level to calculate the profit and pollutant gas emissions during the planning period as the initial values ​​for the n+1th iteration.

[0147] Regarding step 114:

[0148] like Figure 6 As shown, in one embodiment of the present invention, when multi-node computing networks are collaboratively planned, each node data center establishes an integrated energy system (electricity-cold-heat-hydrogen-gas, etc.) with optimal economy and lowest carbon emissions based on resource endowment. The multi-objective planning and design of computing power aims to minimize carbon emissions, minimize energy consumption, and maximize profits. The energy supply curves and prices of various energy sources of each node data center are used as boundary conditions to optimize the timing of computing power tasks such as interactive workloads and batch workloads and optimize the spatial allocation of multi-node data centers. The initial scale of each node data center and the computing power load demand curve are determined. The energy multi-objective planning and design module calculates the cooling load, heating load and other electrical loads of the data center based on the computing power load demand curve of each node data center, and uses this as the boundary condition. With the goal of maximizing profits and minimizing carbon emissions during the planning period, the energy supply facility capacity of the integrated energy system of each node data center, the trading scale of public thermal power and green electricity, the trading scale of natural gas and hydrogen, etc. are optimized. The multi-objective planning and design of computing power and energy use the multi-temporal and spatial dimension allocation of computing power tasks and multi-node energy supply as coupling points and boundary interactions. Through multiple iterations, the optimal planning scheme for computing, network and energy integration is finally optimized.

[0149] like Figure 2 、 Figure 3 As shown, the embodiment of the present invention provides a multi-node computing network collaborative planning device for multi-temporal and multi-spatial scheduling of computing tasks. The device embodiment can be implemented by software, or by hardware or a combination of software and hardware. From the hardware level, Figure 2 As shown, a hardware architecture diagram of an electronic device where a multi-node computing network capable of collaborative planning for multi-temporal and multi-spatial scheduling of computing tasks provided by an embodiment of the present invention is located, except Figure 2 In addition to the processor, memory, network interface, and non-volatile memory shown, the electronic device in the embodiment may also include other hardware, such as a forwarding chip responsible for processing messages, etc. Taking software implementation as an example, Figure 3 As shown, as a device in a logical sense, it is formed by the CPU of the electronic device in which it is located reading the corresponding computer program in the non-volatile memory into the internal memory and running it.

[0150] like Figure 3As shown, this embodiment provides a multi-node computing network collaborative planning device for multi-temporal and multi-spatial scheduling of computing tasks, the device comprising:

[0151] Model building module 300, for establishing a multi-node computing network planning model and a multi-node energy supply planning model;

[0152] A first data processing module 302 is configured to determine the multi-node center computing power scale based on the objective function of maximizing the multi-node data center revenue and minimizing carbon emissions, and based on the constraints of latency and multi-node computing power scale, and to send the multi-node center computing power scale to the first lower-layer model;

[0153] A second data processing module 304 is configured to determine the carbon emissions and profits of the multi-node data center using the first lower-layer model as an objective function, with energy constraints and multi-node computing power scale constraints as constraints, and to send the carbon emissions and profits of the multi-node data center to the first upper-layer model;

[0154] A third data processing module 306 is configured to repeatedly iterate the first upper-layer model and the first lower-layer model to determine a first optimal solution; wherein the first optimal solution includes a multi-node computing power equipment planning scheme and a multi-node computing power energy consumption load curve, and the multi-node computing power energy consumption load curve is used as an input of the energy supply planning model for coupled interaction;

[0155] A fourth data processing module 308 is configured for the second upper-layer model to determine a multi-node device configuration strategy and planned capacity based on the objective function of maximizing profits and minimizing carbon emissions during the multi-node planning period, and based on the multi-node device planning capacity upper limit constraint and the multi-node device commissioning planning constraint as constraints, and to send the multi-node device configuration strategy and planned capacity to the second lower-layer model;

[0156] A fifth data processing module 310 is configured to determine the optimal operation plan and total operation cost for the second lower-layer model using the minimization of multi-node operation cost as the objective function, and using multi-node power balance constraints, equipment commissioning planning constraints, energy system and upper-level network power exchange constraints, and green power ratio constraints as constraints, and to send the optimal operation plan and total operation cost to the second upper-layer model;

[0157] A sixth data processing module 312 is configured to repeatedly iterate the second upper-layer model and the second lower-layer model to determine a second optimal solution; wherein the second optimal solution includes an energy supply planning scheme, a power curve of power generation equipment, a public thermal power curve, and a green power trading curve, and the power curve of power generation equipment, the public thermal power curve, and the green power trading curve are used as inputs of a multi-node computing power network planning model for coupled interaction.

[0158] The cyclic coupling interaction module 314 is used for cyclic coupling interaction between the multi-node computing power network planning model and the multi-node energy supply planning model, and has realized the multi-node computing network collaborative planning of multi-temporal and spatial dimension scheduling of computing power tasks.

[0159] In an embodiment of the present invention, the model building module 300 can be used to execute step 100 in the above method embodiment, the first data processing module 302 can be used to execute step 102 in the above method embodiment, the second data processing module 304 can be used to execute step 104 in the above method embodiment, the third data processing module 306 can be used to execute step 106 in the above method embodiment, the fourth data processing module 308 can be used to execute step 108 in the above method embodiment, the fifth data processing module 310 can be used to execute step 110 in the above method embodiment, the sixth data processing module 312 can be used to execute step 112 in the above method embodiment, and the loop coupling interaction module 314 can be used to execute step 114 in the above method embodiment.

Claims

1. A method for collaborative planning of multi-node computing networks for multi-temporal and multi-spatial scheduling of computing tasks, characterized by: The method is applied to a multi-node computing network and includes: Establishing a multi-node computing power network planning model and a multi-node energy supply planning model; wherein the multi-node computing power network planning model and the multi-node energy supply planning model are both two-layer model structures, the multi-node computing power network planning model includes a first upper layer model and a first lower layer model, and the multi-node energy supply planning model includes a second upper layer model and a second lower layer model; The first upper-layer model takes the maximum benefit and the minimum carbon emission of the multi-node data center as the objective function, and takes the latency constraint and the multi-node computing power scale constraint as the constraint conditions, determines the multi-node center computing power scale, and sends the multi-node center computing power scale to the first lower-layer model; The first lower-layer model takes maximizing the revenue and minimizing the carbon emissions of the multi-node data center as the objective function, and takes energy constraints and multi-node computing power scale constraints as constraints, determines the carbon emissions and profits of the multi-node data center, and sends the carbon emissions and profits of the multi-node data center to the first upper-layer model; The first upper-layer model and the first lower-layer model are repeatedly iterated to determine a first optimal solution; wherein the first optimal solution includes a multi-node computing power equipment planning scheme and a multi-node computing power energy consumption load curve, and the multi-node computing power energy consumption load curve is used as an input of the energy supply planning model to perform coupled interaction; The second upper-layer model takes the maximization of multi-node planning period profit and the minimization of carbon emissions as the objective function, and takes the multi-node equipment planning capacity upper limit constraint and the multi-node equipment commissioning planning constraint as the constraint conditions, determines the multi-node equipment configuration strategy and planned capacity, and sends the multi-node equipment configuration strategy and planned capacity to the second lower-layer model; The second lower-layer model uses the minimization of multi-node operating costs as the objective function, and uses multi-node power balance constraints, equipment commissioning planning constraints, energy system and upper-level network power exchange constraints, and green power ratio constraints as constraints to determine the optimal operating plan and total operating cost, and sends the optimal operating plan and total operating cost to the second upper-layer model; The second upper-layer model and the second lower-layer model are repeatedly iterated to determine a second optimal solution; wherein the second optimal solution includes an energy supply planning scheme, a power curve of power generation equipment, a public grid thermal power curve, and a green electricity trading curve, and the power curve of power generation equipment, the public grid thermal power curve, and the green electricity trading curve are used as inputs of a multi-node computing power network planning model for coupled interaction; The multi-node computing power network planning model and the multi-node energy supply planning model perform cyclic coupling interaction to realize multi-node computing network collaborative planning for multi-temporal and spatial dimension scheduling of computing power tasks.

2. The method according to claim 1, characterized in that The objective function of maximizing the revenue and minimizing the carbon emissions of the multi-node data center includes the following formula: f i,ntp =f i,tp (1-l int )=(f i,ep -f i,ep l vat l umt -f i,ep l vat l est )(1-l int ) f i,ep =f i,in -f i,c Where, The number of energy supply production equipment put into operation in year t; The capacity of the selected model of production equipment for the i-th energy supply; is the number of the i-th energy storage device put into operation in year t; is the capacity of the model selected for the i-th energy storage device; C inv is the initial investment cost; T is the life cycle; r is the capital discount rate; is the investment cost in year t; α lr,t is the loan ratio in year t; i is the number of energy supply production equipment; Ω1 is the set of energy supply production equipment; The unit capacity investment cost of the production equipment for the i-th energy supply; The construction capacity of the i-th energy supply production equipment in year t; is the investment and construction capacity of the i-th energy supply production equipment in year k; j is the energy storage equipment number; Ω2 is the energy storage equipment set; is the unit capacity investment cost of the j-th energy storage device; is the investment and construction capacity of the j-th energy storage equipment in year t, is the investment and construction capacity of the j-th energy storage equipment in the k-th year, The annual operation and maintenance cost per unit capacity of the production equipment supplying the i-th energy source; is the annual operation and maintenance cost per unit capacity of the j-th energy storage device, is the purchase cost of thermal power from the public grid in year t, is the medium- and long-term transaction cost of the i-th green electricity in year t, is the normal green electricity purchase cost in year t, is the penalty cost for the deviation of the i-th green power medium- and long-term transaction in the t-th year, is the natural gas purchase cost in year t, is the purchase cost of hydrogen in year t, C e,t,s,h P is the unit price of electricity purchased from the public grid during the h period of the typical day scenario in year t; e,t,s,h is the amount of electricity purchased from the public grid during the h period of the typical day scenario in year t, C le,t,i,s,h P is the purchase price of green electricity in the i-th medium- and long-term transaction in the h-th period of the typical day scenario in the t-th year; le,t,i,s,h C is the purchase amount of green electricity in the i-th medium- and long-term transaction in the h-th period of the typical day scenario in the t-th year, ge,t,s,h P is the normal green electricity purchase price for the typical day scenario h period in year t; ge,t,s,h is the normal green electricity purchase amount in the h period of the typical day scenario in year t, C g,t,s,h G is the natural gas purchase price in the typical day scenario h period in year t; g,t,s,h is the natural gas purchase amount in the typical day scenario h period in year t, C H2,t,s,h G is the unit price of hydrogen purchased in the typical day scenario h period in year t; H2,t,s,h is the amount of hydrogen purchased in the typical day scenario h period in year t, C le,t,i,s,h,dp The penalty price for deviation of green electricity medium- and long-term transaction in the i-th green electricity medium- and long-term transaction in the h-th period of the typical day scenario in the t-th year; P le,t,i,s,h,dp P is the deviation of the i-th green electricity medium- and long-term transaction caused by the responsibility of the power purchaser in the h-th period of the typical day scenario in the t-th year; lest,t,i,s,h The transaction volume of the i-th green power medium- and long-term trading contract in the h-th period of the typical day scenario in the t-th year; P legm,t,i,s,h The amount of green electricity provided by medium- and long-term transactions in the i-th period of time in the typical day scenario of the t-th year is C fc,t is the financial cost in year t; r i,tt is the annual loan interest rate for the loan in year tt; C inv,tt is the investment cost in year tt; α lr,tt is the loan ratio in year tt; N tt is the repayment period of the loan in year tt, SR t is the energy supply income in year t, C e,t,s,h,shou P is the unit price of electricity sold in the typical day scenario h period in year t; eL,t,s,h P is the other electric load in the typical day scenario h period in year t; cpeL,t,s,h HPR is the power load consumed by computing equipment in the typical day scenario h period in year t; FJ is the heating price per unit area; CPR FJ is the cooling price per unit area; A r,HFJ is the heating area; A r,CFJ is the cooling area, S t,up is the surplus access income in year t; S is the number of typical daily scenarios each year; N s is the duration of the typical day scene s in a year; H is the total number of time periods in a day, C up,e,t,s,h P is the unit price of surplus electricity on the grid in the h period of the typical day scenario in year t; up,e,t,s,h is the on-grid power consumption in the h period of the typical day scenario in year t, S invs Subsidize total investment; is the investment subsidy for year t; Subsidy for initial investment per unit capacity of the i-th energy supply production equipment; is the initial investment subsidy per unit capacity of the j-th energy storage device; S t,sub is the power generation subsidy income in year t; C wt,e,t,s,h 、C pv,e,t,s,h are the electricity subsidies for wind power generation and photovoltaic power generation in the h period of the typical day scenario in year t; P wt,e,t,s,h 、P pv,e,t,s,h are the wind power generation and photovoltaic power generation in the typical day scenario h period in year t, S t,c is the environmental benefit in year t; C t,c is the carbon trading price in year t; E t,nom is the carbon quota for year t; is the carbon dioxide emissions in year t; S is the emission coefficient of carbon dioxide generated by the consumption of electricity; t,le,dp is the deviation income of green power medium and long-term transaction in year t, S t,i,le,dp is the deviation income of the i-th green power medium- and long-term transaction in the t-th year, E RV is the total residual value of the integrated energy system equipment at the end of the planning period, M n is the total number of equipment in the integrated energy system, δ i is the residual value rate of the i-th equipment in the integrated energy system, C inv,i is the initial investment cost of the i-th equipment, f i,ntp is the net profit of the energy system of the ith node throughout its life cycle, f i,tp is the total profit of the entire life cycle of the i-th node, λ int is the income tax rate, f i,ep is the economic benefit of the entire life cycle of the i-th node, λ vat is the value-added tax rate, λ umt Maintaining tax rates for urban construction, λ est is the education surcharge rate, f i,in is the total life cycle benefit of the integrated energy system of the i-th node, f i,e is the life cycle carbon dioxide emissions of the energy system of the i-th node, f i,c is the total investment cost of the integrated energy system at the i-th node, f i,t,in is the total revenue of the integrated energy system at the i-th node in year t, SR i,t is the energy supply income of the i-th node in the t-th year, S i,t,up is the surplus grid access income of the i-th node in the t-th year, is the investment subsidy for the i-th node in the t-th year, S i,t,sub is the power generation subsidy income of the i-th node in the t-th year, S i,t,vs is the value-added service income of the i-th node in the t-th year, S i,t,c is the environmental benefit of the i-th node in the t-th year, S i,t,le,dp The deviation income of green electricity medium- and long-term transaction in the tth year at the i-th node, E i,RV is the total residual value of the integrated energy system equipment at the end of the planning period of the i-th node, is the initial investment cost of the integrated energy system at the i-th node in the t-th year, α lr,t is the loan ratio in year t, is the equipment maintenance cost of the i-th node in the t-th year, is the energy purchase cost of the i-th node in the t-th year, C i,fc,t is the financial cost of the i-th node in the t-th year, N is the number of nodes, is the CO2 emission of the energy system of the ith node in year t; is the emission coefficient of carbon dioxide produced by natural gas; P i,e,t,s,h G is the amount of electricity purchased from the public grid at the i-th node in the t-th year, s-th typical day scenario, h-th period; i,g,t,s,h is the natural gas purchase amount of the i-th node in the typical day scenario of the t-th year and the h-th period.

3. The method according to claim 2, characterized in that The maximum profit of the multi-node planning period is determined by the following formula: Profit Total =Income Total -Cost Total TrueProfit Total =Profit Total *(1-λ) In the formula, Cost Device Cost Device,l represents the total initial equipment acquisition cost of data center l; Indicates the number of type a CPU computing devices in data center l; represents the number of b types of GPU computing devices in data center l; represents the number of c types of network devices in data center l; represents the number of d types of cabinets in data center l; represents the price of type a CPU computing power equipment in data center l; represents the price of b types of GPU computing devices in data center l; represents the price of c types of network equipment in data center l; represents the price of d types of cabinet equipment in data center l, where represents the planned equipment maintenance cost in year t; Planned equipment maintenance costs for data center l in year t; represents the maintenance cost of type a CPU computing equipment in data center l in year t; represents the maintenance cost of b types of GPU computing equipment in data center l in year t; represents the maintenance cost of c types of network equipment in data center l in year t; represents the maintenance cost of d types of cabinet equipment in data center l in year t, represents the annual equipment electricity consumption cost; represents the annual equipment power consumption cost of data center l; represents the heating price per unit area of ​​data center l; represents the heating area of ​​data center l; represents the cooling price per unit area of ​​data center l; represents the cooling area of ​​data center l; represents the energy efficiency coefficient of data center l in different seasons, where The value is 1.05; represents the terminal power supply price of data center l in year t; represents the energy consumption of the computing power request equipment of data center l in year t; represents the basic energy consumption of the equipment in data center l in year t, Cost e，total is the total electricity price, T cycle is the life cycle in years, is the total electricity price of the data center corresponding to the typical day scei, T scei is the number of days of typical day scei, sce is the typical day, The electricity price of data center m in time slot t, represents the electricity price of data center m, P m (t) represents the electricity price of data center m at time t, S t,vs is the value-added service income in year t; S t,vs,l is the value-added service revenue of data center l in year t, β vs,l The value-added service revenue ratio coefficient of the data center; is the comprehensive energy purchase cost of the data center, including: Indicates the total cost of the data center; Total cost of data center l; Energy t,l Indicates the power consumption of all devices in the data center; Expense t,l Cost represents the construction cost of data center l. Inv Indicates the initial investment cost of the data center; Cost Inv,l Cost represents the initial investment cost of data center l; Device,l represents the total initial equipment purchase cost of data center l; considering the delay in project planning and construction, the discount rate needs to be increased. is the financial cost in year t; is the financial cost of data center l in year t; rate l is the annual loan interest rate of data center l; α l is the loan ratio of data center l; lim l is the repayment period of the data center l loan, limt is the repayment period of the loan in year t, Cost Total Plan the total cost of the integrated computing network system; Cost Total,l is the total planning period cost of the data center l of the integrated computing network system, where Income t Indicates the computing power income in the tth year; Income t,l represents the computing power income of data center l in year t; represents the unit price of computing power in data center l in year t; represents the maximum computing power demand at center l in year t, where Residual is the total residual value of computing network system equipment at the end of the planning period, HH is the total number of equipment in the computing network system and HH=A+B+C+D, β i is the residual value rate of the i-th device in the network system, Price i is the initial purchase price of the i-th device, where Income Total Profit is the total income of the network system throughout its life cycle; Total To calculate the economic benefits of the network system throughout its life cycle, TrueProfit Total is the total net profit over the entire life cycle; λ is the income tax rate.

4. The method according to claim 3, characterized in that The multi-node power balance constraints include electric power balance constraints, thermal power balance constraints, cooling power balance constraints, natural gas power balance constraints and hydrogen power balance constraints; The multi-node power balance constraint is determined by the following formula: Where n represents the nth node, N is the number of nodes, Ω4 is the set of energy supply and production equipment that generates electricity; Ω5 is the set of energy storage equipment; Ω6 is the set of energy supply and production equipment that consumes electricity; P n,e,t,s,h P is the amount of electricity purchased from the public grid by the data center of the nth node in the typical day scenario of the tth year and the hth period; n,le,t,i,s,h P is the mid- to long-term green electricity purchase amount of the n-th node data center in the i-th typical day scenario h period in the t-th year; n,ge,t,s,h P is the normal green electricity purchase amount of the nth node data center in the typical day scenario h period in the tth year; n,e,m,t,s,h The power generated by the energy supply production equipment of the mth energy generating power in the typical day scenario of the nth node data center in the tth year and the hth period; P n,e,j,ES-dis,t,s,h P is the discharge power of the jth storage device in the typical day scenario h period in the tth year in the nth node data center; n,e,k,t,s,h P is the power consumed by the kth energy supply device consuming electricity in the nth node data center in the typical day scenario h period of the tth year; n,eL,t,s,h P is the other electrical load of the data center of the nth node in the typical day scenario of the tth year and the hth period; n,e,j,ES-ch,t,s,h P is the charging power of the jth energy storage device in the nth node data center in the tth year s typical day scenario h period; n,cpeL,t,s,h is the power load consumed by the computing equipment in the data center of the nth node during the typical day scenario h in the tth year; Ω7 is the set of energy supply and production equipment that generates heat energy; Ω8 is the set of heat storage equipment; Ω9 is the set of energy supply and production equipment that consumes heat energy; Q n,q,i,t,s,h The thermal power generated by the energy supply production equipment of the i-th heat-generating device in the n-th node data center in the typical day scenario of the t-th year and the h-th period; Q n,q,j,HS-dis,t,s,h Q is the heat release power of the jth heat storage device in the nth node data center in the tth year s typical day scenario h period; n,q,k,t,s,h Q is the heat load of the energy supply production equipment of the kth energy consumption heat energy in the typical day scenario h period of the nth node data center in the tth year; n,qL,t,s,h Q is the heat load of the data center of the nth node in the typical day scenario of the tth year and the hth period; n,q,j,HS-ch,t,s,h is the charging power of the jth heat storage device in the nth node data center in the tth year s typical day scenario h period, Ω 10 A collection of energy supply and production equipment for generating cold energy; 11 A collection of cold storage equipment; C n,c,i,t,s,h C is the cooling power generated by the energy supply production equipment of the i-th cooling energy generating device in the typical day scenario of the n-th node data center in the t-th year and the h-th period; n,c,j,CS-dis,t,s,h C is the cooling power of the jth cold storage device in the nth node data center in the tth year s typical day scenario h period; n,cL,t,s,h C is the cooling load of the data center of the nth node in the h-th period of the typical day scenario of the t-th year; n,c,j,CS-ch,t,s,h is the cooling power of the jth cold storage device in the nth node data center in the tth year s typical day scenario h period, Ω3 is the set of energy supply production equipment that consumes natural gas; 12 G is a collection of gas storage equipment; n,g,t,s,h G is the natural gas power purchased by the data center of the nth node in the typical day scenario of the tth year and the hth period; n,g,i,GS-dis,t,s,h G is the gas discharge power of the jth gas storage device in the nth node data center in the tth year s typical day scenario h period; n,g,j,t,s,h The natural gas power of the energy supply production equipment of the jth natural gas consumption in the typical day scenario h period of the nth node data center in the tth year; G n,g,i,GS-ch,t,s,h is the filling power of the jth gas storage device in the nth node data center in the tth year s typical day scenario h period, Ω 14 A collection of energy supply and production equipment for consuming hydrogen; 13 Assemble for hydrogen storage equipment; The amount of hydrogen purchased by the nth node data center during the h-hour period in the s-day typical scenario in the t-th year; The hydrogen discharge power of the jth hydrogen storage device in the nth node data center in the tth year s typical day scenario h period; The hydrogen power of the energy supply production equipment for the jth energy consumption of hydrogen energy in the typical day scenario h period of the tth year s of the nth node data center; is the hydrogen charging power of the jth hydrogen storage device in the typical day scenario h period in the tth year in the nth node data center.

5. The method according to claim 4, characterized in that The upper limit constraint of the multi-node device planning capacity is determined by the following formula: Where, The investment and construction capacity of the i-th energy supply production equipment of the n-th node data center in year t, is the investment and construction capacity of the j-th energy storage device in the n-th node data center in year t, are the planned capacity upper limits of energy production equipment and energy storage equipment of the nth node data center in year t.

6. The method according to claim 5, characterized in that The power exchange constraint between the energy system and the upper network is determined by the following formula: Where, are the minimum and maximum power purchased from the public grid thermal power of the nth node data center in year t; are the minimum and maximum purchased natural gas power for the nth node data center in year t; The minimum and maximum purchased hydrogen power for the nth node data center in year t respectively; are the minimum and maximum green electricity purchase power of the data center of the nth node in the typical day scenario of the hth period in the tth year; P n,legm,t,i,s,h It is the amount of green electricity provided by the data center of the nth node in the medium- and long-term transaction during the i-th period of the typical day scenario of the t-th year and the h-th period.

7. The method according to claim 6, characterized in that: The multi-node equipment operation planning constraints are determined by the following formula: Where, The minimum value of the green electricity medium- and long-term transaction power supply of the i-th node data center; The maximum green electricity purchase power of the nth node data center in year t, The maximum power consumed by the kth energy supply device consuming electricity in the nth node data center in year t; The maximum thermal power of the energy supply production equipment for the kth energy consumption of heat energy in the nth node data center in the tth year; The maximum natural gas power of the energy supply production equipment for the jth natural gas consumption in the nth node data center in year t; The maximum hydrogen power of the energy supply production equipment for the jth hydrogen-consuming energy in the nth node data center in the tth year; are the rated capacities of the electricity, heat, and cooling energy production equipment of the nth node data center in year t, They are the peak values ​​of other power loads, power load consumed by computing equipment, heat load, and cooling load of the nth node data center in year t.

8. A multi-node computing network collaborative planning device for multi-temporal and multi-spatial scheduling of computing tasks, characterized by: include: A model construction module for establishing a multi-node computing power network planning model and a multi-node energy supply planning model; wherein the multi-node computing power network planning model and the multi-node energy supply planning model are both two-layer model structures, the multi-node computing power network planning model includes a first upper layer model and a first lower layer model, and the multi-node energy supply planning model includes a second upper layer model and a second lower layer model; A first data processing module is configured to determine the multi-node center computing power scale based on the objective function of maximizing the multi-node data center revenue and minimizing carbon emissions, and based on the constraints of latency and multi-node computing power scale, and to send the multi-node center computing power scale to the first lower-layer model; a second data processing module, configured for the first lower-layer model to determine the carbon emissions and profits of the multi-node data center with maximizing the revenue and minimizing the carbon emissions of the multi-node data center as objective functions, and with energy constraints and multi-node computing power scale constraints as constraints, and to send the carbon emissions and profits of the multi-node data center to the first upper-layer model; a third data processing module configured to repeatedly iterate the first upper-layer model and the first lower-layer model to determine a first optimal solution; wherein the first optimal solution includes a multi-node computing power equipment planning scheme and a multi-node computing power energy consumption load curve, and the multi-node computing power energy consumption load curve is used as an input of the energy supply planning model for coupled interaction; A fourth data processing module is configured for the second upper-layer model to determine a multi-node device configuration strategy and planned capacity based on the objective function of maximizing profits and minimizing carbon emissions during the multi-node planning period, and based on the upper limit constraint of the multi-node device planning capacity and the multi-node device commissioning planning constraint as constraints, and to send the multi-node device configuration strategy and planned capacity to the second lower-layer model; A fifth data processing module is configured for the second lower-layer model to determine an optimal operation plan and total operation cost with the objective function of minimizing multi-node operation cost, and with multi-node power balance constraints, equipment commissioning planning constraints, power exchange constraints between the energy system and the upper-level network, and green power ratio constraints as constraints, and to send the optimal operation plan and total operation cost to the second upper-layer model; a sixth data processing module configured to repeatedly iterate the second upper-layer model and the second lower-layer model to determine a second optimal solution; wherein the second optimal solution includes an energy supply planning scheme, a power curve of power generation equipment, a public grid thermal power curve, and a green electricity trading curve; and the power curve of power generation equipment, the public grid thermal power curve, and the green electricity trading curve are used as inputs of a multi-node computing power network planning model for coupled interaction; A cyclic coupling interaction module is used for cyclic coupling interaction between the multi-node computing power network planning model and the multi-node energy supply planning model to realize multi-node computing network collaborative planning for multi-temporal and multi-spatial dimension scheduling of computing power tasks.

9. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and 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, characterized in that A computer program is stored thereon, and when the computer program is executed in a computer, the computer is caused to execute the method according to any one of claims 1 to 7.

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