Differentiation demand coordination method based on data center mathematical model

Abstract

The invention discloses a differentiated demand coordination method based on a data center mathematical model, and the method comprises the steps: constructing a cloud user load space-time coordination data center mathematical model considering the building thermal dynamic characteristics according to the building thermal dynamic characteristics of a data center and the differentiated real-time demands of cloud user loads; according to the method, uncertain factors are considered, a data center two-stage robust optimization model considering building thermal dynamic is constructed by taking data center daily operation cost minimization as a target, and an optimal difference demand coordination scheme is obtained. According to the method, the influence of uncertain factors is considered, a data center two-stage robust optimization model about the load arrival rate, the air conditioner refrigeration power, the indoor temperature and the operation energy consumption of the data center is established, the cooperative potential of building thermal inertia and load space-time adjustment of the data center is mined, the adjustment flexibility of the load and an air conditioner system is brought into play, and the robustness of the data center is improved. The energy consumption of data center IT equipment and an air conditioning system is effectively reduced, and the operation economy and risk resistance of the data center are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data center mathematical models, and in particular to a differentiated demand coordination method based on a data center mathematical model. Background Art

[0002] With the rapid development of technologies such as big data and cloud computing, the demand for data communication and data computing has grown exponentially, the number and scale of data centers have continued to expand, and their total energy consumption has increased dramatically. In the energy consumption structure of data centers, about 50% of the energy consumption comes from IT equipment, whose main function is to process cloud user data loads, and about 37% of the energy consumption comes from air-conditioning systems. Currently, the main way to reduce the operating costs of data centers is to study the load spatiotemporal adjustment capabilities of data centers.

[0003] However, the existing research methods do not take into account the synergistic potential of the data center's own building thermal inertia and load spatiotemporal regulation, and do not take into account the impact of the uncertainty of conventional circuit load and outdoor temperature on the load scheduling of the data center. Therefore, it is necessary to study a differentiated demand coordination method based on the mathematical model of the data center to tap the synergistic potential of the data center's own building thermal inertia and load spatiotemporal regulation, meet the differentiated real-time requirements of the data center building thermal dynamic characteristics and cloud user loads, and take into account the impact of uncertain factors to obtain the best solution to reduce the operating costs of the data center. Summary of the invention

[0004] The purpose of the present invention is to provide a differentiated demand coordination method based on a data center mathematical model to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: a differentiated demand coordination method based on a data center mathematical model, comprising the following steps:

[0006] S1. According to the building thermal dynamic characteristics of the data center and the differentiated real-time requirements of cloud user loads, a mathematical model of the cloud user load spatiotemporal coordination data center taking into account the ambient temperature and the building thermal dynamic characteristics is constructed;

[0007] S2. Setting constraints on the mathematical model of spatiotemporal coordination of cloud user loads in a data center taking into account the thermal dynamic characteristics of the building;

[0008] S3, based on the cloud user load spatiotemporal coordination data center mathematical model taking into account the thermal dynamic characteristics of the building obtained in S1, with conventional load power and outdoor temperature as uncertain factors and the target data center daily operation cost minimization as the goal, a two-stage robust optimization model of the data center taking into account the thermal dynamic characteristics of the building is constructed;

[0009] S4. The two-stage robust optimization model of the data center taking into account the thermal dynamic characteristics of the building is iteratively solved using the G&GG algorithm to obtain the optimal differential demand coordination solution for uncertain factors.

[0010] Preferably, the S1 also includes:

[0011] S11. Establish a data center data load model;

[0012] S12. Establishing a data center energy consumption model;

[0013] S13. Establish a mathematical model of cloud user load spatiotemporal coordination data center taking into account the thermal dynamic characteristics of the building;

[0014] The establishment of the data center data load model in S11 also includes dividing the cloud user load in the data center into interactive data load and batch data load according to the real-time requirements of cloud users;

[0015] The data center data load model includes a response time model for interactive data loads, wherein the response time model includes a transmission delay time model and an average waiting time calculation model, wherein the transmission delay time model is as follows:

[0016] d itr =tc(1);

[0017] where d itr is the transmission time of interactive user load in period t, tc is a constant;

[0018] The average waiting time calculation model is as follows:

[0019]

[0020] where t w,t is the average waiting time of interactive cloud user load in period t, M is the set of server types in the data center, i is a server type in set M, N is the set of server working states, j is a working state in set N, is the service rate of the data center in the i-th category and in working state j processing interactive cloud user load in period t; is the interactive cloud user load rate during period t;

[0021] The data center data load model also includes a cloud user load processing time model for batch data loads, and the cloud user load processing time model is as follows:

[0022]

[0023] in, is the service rate of batch-type cloud user loads in data center i and in working state j during period t; is the load rate of batch processing cloud users in period t.

[0024] Preferably, the establishment of the data center energy consumption model includes the following steps:

[0025] S121. Establish a power model for IT equipment in a data center;

[0026] S122. Establishing a power model of an air conditioning system for a data center;

[0027] S123, establishing a total power model for the data center;

[0028] The establishment of the IT equipment power model of the data center specifically includes using the data center server power to represent the IT equipment power, and modeling the server power based on the DVFS technology, and obtaining the IT equipment power model as follows:

[0029]

[0030] P m,s,t =P m,st +P m,dy,t (5);

[0031]

[0032] Among them, P m,s,t P is the power of the m-type server in the s working state during the t period. m,st is the corresponding server static power, P m,dy,t is the corresponding dynamic energy consumption; k m is the dynamic energy consumption calculation coefficient of the m-type server; f m is the chip operating frequency;

[0033] The establishment of the air conditioning system power model specifically includes using the building thermal dynamic characteristics of the data center to construct the air conditioning system power model as follows:

[0034]

[0035] Among them, C wall,ij is the wall heat capacity, which is divided into the wall heat capacity with window side and the wall heat capacity without window side; is the wall temperature; is the indoor temperature; is the temperature of the adjacent node; R wall,ij is the wall thermal resistance, which is divided into the wall thermal resistance with window side and the wall thermal resistance without window side; if the wall is exposed to external solar radiation, q ij Take 1, otherwise take 0; vij is the wall heat absorption rate; A wall ,ij is the wall area; is the light intensity in the corresponding direction of the wall; C romm,j Heat capacity for the room; is the outdoor temperature; N room Refers to the region's adjacent nodes; is the window thermal resistance; ω ij is the window refractive index; is the light intensity in the direction corresponding to the window; It is an internal heat source; is the cooling capacity of the air-conditioning system during period t; is the internal heat dissipation coefficient of IT equipment in the data center;

[0036] The establishment of the total power model of the data center specifically includes constructing the total power model according to the IT equipment power model and the air conditioning system power model of the data center, and the total power model of the data center is as follows:

[0037]

[0038] in, is the total power of the data center in period t; is the power of IT equipment during period t; is the power of the air conditioning system during period t; is the total power of the conventional load during period t, which includes the power distribution system and lighting system of the data center, and is a constant;

[0039] The establishment of a cloud user load spatiotemporal coordination data center mathematical model taking into account the thermal dynamic characteristics of the building specifically includes: integrating the total power model of the data center to obtain a cloud user load spatiotemporal coordination data center mathematical model taking into account the thermal dynamic characteristics of the building based on the thermal dynamic characteristics of the data center building and the differentiated real-time requirements of the cloud user load as follows:

[0040]

[0041] in, is the total energy consumption of the data center in period t; is the energy consumption of IT equipment during period t; is the energy consumption of the air conditioning system during period t; is the total energy consumption of conventional load during period t; N T is the scheduling period;

[0042] Preferably, the setting of constraint conditions for the cloud user load spatiotemporal coordination data center mathematical model taking into account the thermal dynamic characteristics of the building comprises the following steps:

[0043] S21. Constructing cloud user data load rate constraints for a mathematical model of a spatiotemporal coordinated data center for cloud user loads taking into account the thermal dynamic characteristics of buildings;

[0044] S22. Constructing the service rate sum constraint of the mathematical model of spatiotemporal coordination of cloud user load in data center taking into account the thermal dynamic characteristics of the building;

[0045] S23, constructing a maximum response time constraint for a mathematical model of a spatiotemporal coordinated data center for cloud user loads taking into account the thermal dynamics of buildings;

[0046] S24, constructing server operating environment temperature constraints for a mathematical model of a cloud user load spatiotemporal coordinated data center taking into account the thermal dynamic characteristics of the building;

[0047] S25. Constructing a maximum data load rate constraint for a mathematical model of a spatiotemporal coordinated data center for cloud user loads taking into account the thermal dynamic characteristics of buildings;

[0048] S26. Constructing server operating frequency and service rate constraints for a mathematical model of a spatiotemporal coordinated data center for cloud user loads taking into account the thermal dynamic characteristics of buildings;

[0049] S27. Constructing the server quantity constraint of the mathematical model of the spatiotemporal coordination data center of cloud user load taking into account the thermal dynamic characteristics of the building;

[0050] S28. Construct the air conditioning system operation constraints of the mathematical model of the data center for spatiotemporal coordination of cloud user loads taking into account the thermal dynamic characteristics of the building.

[0051] Preferably, the setting of the constraint conditions specifically includes the following steps:

[0052] The step of constructing the cloud user data load rate constraint in S21 specifically includes the following steps:

[0053] Calculate the total cloud user data load of the data center using the following formula:

[0054]

[0055] Among them, λ t It is the sum of the data load rates of E data centers in period t, and the data load rate is the cloud user load allocated to the data center per unit time;

[0056] Based on the calculation formula (15), the data load rate of the e-th data center is λ e,t , and λ e,t The calculation formula is as follows:

[0057]

[0058] in, is the sum of the interactive data load rates that need to be processed by E data centers within a unit time period t; is the sum of the batch data load rates that need to be processed by E data centers within a unit period t, is the interactive data load rate that needs to be processed by the e-th data center in unit time period t, is the batch data load rate that needs to be processed by the e-th data center in unit time period t;

[0059] The construction of the maximum response time constraint in S23 specifically includes the following steps:

[0060] The maximum response time of interactive cloud user loads satisfies the following constraint:

[0061] t w,t ≤D itr -d itr (17);

[0062] The maximum response time of batch cloud user loads satisfies the following constraint:

[0063]

[0064] Among them, D itr is the maximum response time of interactive cloud user load; d itr is the load transmission delay time; T batch The maximum response time for batch-type cloud user loads;

[0065] The construction of the air conditioning system operation constraints in S28 specifically includes the following steps:

[0066] Based on the air conditioning system meeting the cooling requirements of the servers in the data center and the operating indoor temperature requirements, the operating power of the air conditioning system meets the following constraint relationship:

[0067]

[0068] in, Sets a power cap for air conditioning system operation.

[0069] Preferably, the setting of the constraint conditions further includes the following steps:

[0070] The construction of the service rate sum constraint in S22 specifically includes the following steps:

[0071] From the perspective of server types, the sum of the service rates of e data centers satisfies the following relationship:

[0072]

[0073] From the perspective of cloud users' real-time requirements, the sum of the service rates of e data centers satisfies the following relationship:

[0074]

[0075] Among them, μ t is the sum of the service rates of the servers in E data centers during period t; is the service rate of the m-type servers in the s-working state in the data center i during period t; is the service rate of E data centers for processing interactive cloud user loads during period t; is the service rate of E data centers for processing batch cloud user loads during period t;

[0076] The step of constructing the server operating environment temperature constraint in S24 specifically includes the following steps:

[0077] When the data center server is operating normally, the temperature range in which the indoor temperature is located satisfies the following constraint relationship:

[0078]

[0079] in, They are the lower and upper limits of the indoor temperature when the data center server is operating normally;

[0080] The construction of the maximum data load rate constraint in S25 specifically includes the following steps:

[0081] When a data center server processes data, the maximum data load that it can handle satisfies the following constraint:

[0082] 0≤λ e,t ≤λ e,max (twenty three);

[0083] Among them, λ e,max is the maximum data load that the e-th data center can process per second;

[0084] The construction of the server operating frequency and service rate constraints in S26 specifically includes the following steps:

[0085] The server processor is equipped with discrete CPU operating frequency and service rate ladders, and the two correspond to each other. The CPU operating frequency and service rate satisfy the following relationship:

[0086] f t ∈{f1,f2,…f h …,f H} (twenty four);

[0087] μ t ∈{μ1,μ2,…μh …,μ H} (25);

[0088] Among them, H is the optional operating frequency type of the server, f t is the CPU operating frequency of the server during period t, μ t is the server service rate in period t;

[0089] In the CPU operating frequency and service rate adjustment of the data center, a selection is made in the sets (24) and (25) according to the real-time network load to obtain the CPU operating frequency and service rate based on the real-time network load, and the CPU operating frequency and service rate based on the real-time network load satisfy the following relationship:

[0090]

[0091]

[0092] in, is the selection variable of the working frequency and service rate during the period. Since the CPU can only work at one frequency during a period, The following constraints should be satisfied:

[0093]

[0094] The step of constructing the server quantity constraint in S27 specifically includes the following steps:

[0095] The constraint relationship satisfied by the number of servers participating in the adjustment in each data center is as follows:

[0096]

[0097] Among them, n sev The number of servers involved in the adjustment in each data center, The upper limit of the number of servers that can participate in the adjustment in each data center.

[0098] Preferably, S3 includes the following steps:

[0099] S31. Select the target data center, obtain the energy consumption of the target data center based on the cloud user load spatiotemporal coordination data center mathematical model taking into account the thermal dynamic characteristics of the building, and take the minimization of the daily operating cost of the target data center as the objective function. The objective function expression is established as follows:

[0100]

[0101] Among them, M dcA,t is the total energy consumption of the selected target data center A in period t, M dcB,tis the total energy consumption of the selected target data center B during period t, c t is the daily transaction electricity price, and the cloud user load spatiotemporal coordination data center mathematical model of data center A and data center B taking into account the thermal dynamic characteristics of the building satisfies the constraints in S21-S28;

[0102] S32. Establish a data center deterministic mathematical model that does not consider uncertain factors, and the compact form of the data center deterministic mathematical model is as follows:

[0103]

[0104] Among them, x and y are variables, and x and y satisfy the following relationship:

[0105]

[0106] Where C is the number of servers in the data center, which is the coefficient column vector of formula (31); D, K, F, G and I u is the coefficient matrix under the corresponding constraints, d and k are constant column vectors, where the inequality constraint is the second row in (32) and satisfies the constraint relations (17)-(19), (22)-(23), and (30). The equality constraint is the third row in (32) and satisfies the constraint relations (15)-(16), (18), (20)-(21), (28)-(29). The relationship between variables x and y is the fourth row and satisfies the constraint relations (26)-(27). The fifth row indicates that in the data center deterministic mathematical model, the uncertain factor variables are the predicted values ​​of each time period, and the predicted values ​​satisfy the following relationship:

[0107]

[0108] in, represents the predicted value of the conventional load power in period t, represents the predicted value of outdoor temperature in period t;

[0109] S33. Construct a box type uncertainty set U to which the uncertain factors conventional load and outdoor temperature belong, and U satisfies the following relationship:

[0110]

[0111] Among them, u P,t and u T,t are uncertain variables, namely, conventional load power and outdoor temperature; and The maximum fluctuation deviation allowed for the uncertain variable;

[0112] S34. Determine the mathematical model based on the data center, take the box-shaped uncertainty set U of uncertain factors as the constraint, and use the two-stage robust optimization method to construct the mathematical model of the data center taking into account the thermal dynamic characteristics of the building as follows:

[0113]

[0114] The first stage problem is the outermost min problem, and the optimization variable is x; the second stage problem is the inner max-min problem, and the optimization variables are u and y. The min problem is equivalent to the objective function of formula (31), which represents the minimization of the daily operating cost of the data center. Ω(x,u) means the feasible domain of the optimization variable y when the value of (x,u) is given, and the expression of Ω(x,u) is as follows:

[0115]

[0116] Among them, γ, λ, ν, and π are dual variables used to deal with the minimization problem in the second stage.

[0117] Preferably, the S4 further includes the following steps:

[0118] S41. Decompose the mathematical model of the data center taking into account the thermal dynamic characteristics of the building, which is constructed by the two-stage robust optimization method in S34, to obtain a main problem model and a sub-problem model, wherein the main problem model is as follows:

[0119]

[0120] Where k and l are the number of iterations, and l∈k, y l is the solution of the data center subproblem after the lth iteration; is the value of the uncertain factors normal load power and outdoor temperature for the first iteration;

[0121] The sub-problem model is as follows:

[0122]

[0123] For the subproblem model (38) of the NP-hard problem, given (x,u), the inner minimization in (38) is transformed into a linear problem, and the inner min in (38) is transformed into the max form according to the pair theory, and merged with the outer max, the transformed subproblem model is obtained as follows:

[0124]

[0125] For the bilinear term u in (40) Tπ, when the uncertainty factors of the data center, the conventional load and the outdoor temperature, are taken as the right boundary in equation (35), the required operating cost of the data center is high. Therefore, the uncertainty U to which the uncertainty factors, the conventional load and the outdoor temperature belong, is optimized as follows:

[0126]

[0127] Where B = [B P,t B T,t ] T is a binary variable, and the value of 1 indicates that the normal load power and outdoor temperature reach the maximum value of the uncertainty set; Γ p,t and Γ T,t are the uncertainty adjustment parameters corresponding to the conventional load power and outdoor temperature, respectively. The smaller the Γ value, the more risky the model is. Therefore, Γ is used to adjust the conservativeness of the scheme. When the uncertainty set of the uncertain factors in formula (41) is substituted into formula (40), the product of continuous variables and binary variables appears. Therefore, the linearization of formula (40) is carried out through the big M method to obtain the following expression:

[0128]

[0129] Where Δu=(Δu P,t ,Δu T,t ) T ,B'=(B' P,t ,B' T,t ) T is a continuous auxiliary variable, is a positive real number;

[0130] Preferably, the S4 further includes the following steps:

[0131] S42: for the main problem model and the sub-problem model obtained in S41, the G&GG algorithm is used to iteratively solve to obtain the optimal differential demand coordination solution for uncertain factors. The solution method includes the following steps:

[0132] S421: Given a set of conventional load power and outdoor temperature as the worst scenario u, set the lower bound LB of the data center daily operating cost provided by the main problem to +∞, the upper bound UB provided by the sub-problem to -∞, and the convergence threshold to ε;

[0133] S422: The worst scenario obtained based on the lth iteration of the data center Solving the main problem (38), we get Update the Nether

[0134] S423: Will solve Substituting it into equation (41) as a fixed value, we get the objective function value of the subproblem: and the value of the uncertain variable u, and use the obtained objective function value as the new upper bound to calculate the updated upper bound

[0135] S424: Determine whether the upper and lower bounds meet the convergence conditions. When UB-LB≤ε, the iteration ends and the optimal solution is obtained. Otherwise, the variable y is increased. k+1 And the following constraints:

[0136]

[0137] Order, re-solve the main and sub-problems until the algorithm converges or reaches the maximum number of iterations.

[0138] Compared with the prior art, the present invention has the following beneficial effects:

[0139] 1. The present invention establishes a user load spatiotemporal coordination data center data model taking into account the thermal inertia of the data center's own building based on the synergistic effect of the spatiotemporal regulation characteristics of the interactive data load and the batch data load in the data center and the building thermal dynamic characteristics of the data center, establishes a quantitative mathematical relationship between the load arrival rate, air-conditioning cooling power, indoor temperature and operating energy consumption of the data center, fully taps the synergistic potential of the data center's own building thermal inertia and load spatiotemporal regulation, and gives full play to the adjustment flexibility of the cloud user load and the air-conditioning system in the data center. Under the premise of ensuring the normal operating environment temperature of the server, the data center can be operated economically and efficiently, the energy consumption of the IT equipment and the air-conditioning system in the data center can be effectively reduced, and the economy of the data center operation can be improved.

[0140] 2. The present invention takes into account the uncertainty of conventional load power and outdoor temperature, and proposes a mathematical model of a data center that takes into account the thermal dynamic characteristics of the building using a two-stage robust optimization method. On the basis of ensuring the normal operating temperature of the data center server, the data center's ability to resist fluctuations in conventional load power and outdoor temperature is significantly improved, and the system's operating economy is avoided from being reduced due to excessive conservatism. This allows the data center to meet the needs of cloud users under the condition of uncertainty in conventional loads and outdoor temperatures, improve the data center's ability to resist risks, and reduce operating costs.

[0141] 3. The present invention proposes a C&CG algorithm that efficiently solves the mathematical model of a data center taking into account the thermal dynamic characteristics of the building and is constructed using a two-stage robust optimization method. The original problem is decomposed into a main problem and sub-problems for alternating calculations to obtain the optimal differential demand coordination solution for uncertain factors. The uncertain set of uncertain factors is constructed to transform the NP-hard problem in the sub-problem into a linear problem, thereby reducing the difficulty of calculation and being able to adjust the conservatism of the model, which is beneficial to reducing the redundancy of the scheduling results.

[0142] 4. The present invention sets constraints on the mathematical model of the data center that coordinates the time and space of cloud user loads in consideration of the thermal dynamic characteristics of buildings, which is beneficial to improving the reliability of the mathematical model of the data center that coordinates the time and space of cloud user loads in consideration of the thermal dynamic characteristics of buildings. The optimal solution to the original problem is obtained by solving the problem, that is, the scheduling plan with the optimal operating cost of the data center when the conventional load power of the data center is the largest and the outdoor temperature is the highest, so as to meet the differentiated coordination needs of the mathematical model of the data center. BRIEF DESCRIPTION OF THE DRAWINGS

[0143] Figure 1 is a flow chart of the overall method of the present invention;

[0144] Figure 2 It is the flowchart of S1 in the present invention;

[0145] Figure 3 is a flow chart of S2 in the present invention;

[0146] Figure 4 is a flow chart of S4 in the present invention;

[0147] Figure 5 This is a flow chart of S4 in the present invention. DETAILED DESCRIPTION

[0148] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0149] In the description of the present invention, it should be noted that the terms "upper", "lower", "inner", "outer", "front end", "rear end", "two ends", "one end", "the other end" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance.

[0150] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "provided with", "connected", etc. should be understood in a broad sense. For example, "connected" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0151] See also Figure 1 and Figure 2 , an embodiment provided by the present invention: a differentiated demand coordination method based on a data center mathematical model, specifically comprising the following steps:

[0152] S1. According to the building thermal dynamic characteristics of the data center and the differentiated real-time requirements of cloud user loads, a mathematical model of the cloud user load spatiotemporal coordination data center taking into account the ambient temperature and the building thermal dynamic characteristics is constructed;

[0153] S1 also includes:

[0154] S11. Establish a data center data load model;

[0155] S12. Establishing a data center energy consumption model;

[0156] S13, establishing a mathematical model of a data center for spatiotemporal coordination of cloud user loads taking into account the thermal dynamic characteristics of buildings;

[0157] Specifically, the establishment of the data center data load model in S11 also includes dividing the cloud user load in the data center into interactive data load and batch data load according to the real-time requirements of cloud users;

[0158] The data center data load model includes a response time model for interactive data loads, wherein the response time model includes a transmission delay time model and an average waiting time calculation model, wherein the transmission delay time model is as follows:

[0159] d itr =tc(1);

[0160] where d itr is the transmission time of interactive user load in period t, tc is a constant;

[0161] The average waiting time calculation model is as follows:

[0162]

[0163] where t w,tis the average waiting time of interactive cloud user load in period t, M is the set of server types in the data center, i is a server type in set M, N is the set of server working states, j is a working state in set N, is the service rate of the data center in the i-th category and in working state j processing interactive cloud user load in period t; is the interactive cloud user load rate during period t;

[0164] The data center data load model also includes a cloud user load processing time model for batch data loads, and the cloud user load processing time model is as follows:

[0165]

[0166] in, is the service rate of batch-type cloud user loads in data center i and in working state j during period t; is the load rate of batch processing cloud users in period t;

[0167] S12, establishing a data center energy consumption model includes the following steps;

[0168] S121. Establish a power model for IT equipment in a data center;

[0169] S122. Establishing a power model of an air conditioning system for a data center;

[0170] S123, establishing a total power model for the data center;

[0171] The establishment of the IT equipment power model of the data center specifically includes using the data center server power to represent the IT equipment power, and modeling the server power based on the DVFS technology. The IT equipment power model is as follows:

[0172]

[0173] P m,s,t =P m,st +P m,dy,t (5);

[0174]

[0175] Among them, P m,s,t P is the power of the m-type server in the s working state during the t period. m,st is the corresponding server static power, P m,dy,t is the corresponding dynamic energy consumption; k m is the dynamic energy consumption calculation coefficient of the m-type server; f m is the chip operating frequency;

[0176] The establishment of the air conditioning system power model specifically includes using the building thermal dynamic characteristics of the data center to construct the air conditioning system power model as follows:

[0177]

[0178] Among them, C wall,ij is the wall heat capacity, which is divided into the wall heat capacity with window side and the wall heat capacity without window side; is the wall temperature; is the indoor temperature; is the temperature of the adjacent node; R wall,ij is the wall thermal resistance, which is divided into the wall thermal resistance with window side and the wall thermal resistance without window side; if the wall is exposed to external solar radiation, q ij Take 1, otherwise take 0; v ij is the wall heat absorption rate; A wall ,ij is the wall area; is the light intensity in the corresponding direction of the wall; C romm,j Heat capacity for the room; is the outdoor temperature; N room Refers to the region's adjacent nodes; is the window thermal resistance; ω ij is the window refractive index; is the light intensity in the direction corresponding to the window; It is an internal heat source; is the cooling capacity of the air-conditioning system during period t; is the internal heat dissipation coefficient of IT equipment in the data center;

[0179] Establishing the total power model of the data center specifically includes constructing the total power model according to the IT equipment power model and the air conditioning system power model of the data center, and the total power model of the data center is as follows:

[0180]

[0181] in, is the total power of the data center in period t; is the power of IT equipment during period t; is the power of the air conditioning system during period t; is the total power of the conventional load during period t, which includes the power distribution system and lighting system of the data center, and is a constant;

[0182] The establishment of a cloud user load spatiotemporal coordination data center mathematical model taking into account the thermal dynamic characteristics of the building in S13 specifically includes: integrating the total power model of the data center to obtain a cloud user load spatiotemporal coordination data center mathematical model taking into account the thermal dynamic characteristics of the building based on the thermal dynamic characteristics of the data center building and the differentiated real-time requirements of the cloud user load as follows:

[0183]

[0184] in, is the total energy consumption of the data center in period t; is the energy consumption of IT equipment during period t; is the energy consumption of the air conditioning system during period t; is the total energy consumption of conventional load during period t; N T The scheduling cycle.

[0185] Furthermore, by adopting the above steps, based on the synergistic effect of the spatiotemporal regulation characteristics of interactive data loads and batch data loads in the data center and the building thermal dynamic characteristics of the data center, a user load spatiotemporal coordination data center data model taking into account the thermal inertia of the data center's own building is established, and a quantitative mathematical relationship between the load arrival rate, air-conditioning cooling power, indoor temperature and operating energy consumption of the data center is established. The synergistic potential of the data center's own building thermal inertia and load spatiotemporal regulation is fully explored, as well as the adjustment flexibility of the cloud user load and the air-conditioning system in the data center is brought into play, so as to effectively reduce the energy consumption of the data center IT equipment and the air-conditioning system, and improve the economy of the data center operation.

[0186] See also Figure 1 and Figure 3 , an embodiment provided by the present invention: a differentiated demand coordination method based on a data center mathematical model, comprising the following steps:

[0187] S2. Setting constraints on a mathematical model of a cloud user load spatiotemporal coordination data center taking into account the thermal dynamic characteristics of the building, and specifically including the following steps:

[0188] S21. Constructing a cloud user data load rate constraint for a cloud user load spatiotemporal coordination data center mathematical model taking into account the thermal dynamic characteristics of the building, specifically comprising the following steps:

[0189] Calculate the total cloud user data load of the data center using the following formula:

[0190]

[0191] Among them, λ t It is the sum of the data load rates of E data centers in period t, and the data load rate is the cloud user load allocated to the data center per unit time;

[0192] Based on the calculation formula (15), the data load rate of the e-th data center is λ e,t , and λ e,t The calculation formula is as follows:

[0193]

[0194] in, is the sum of the interactive data load rates that need to be processed by E data centers within a unit time period t; is the sum of the batch data load rates that need to be processed by E data centers within a unit period t, is the interactive data load rate that needs to be processed by the e-th data center in unit time period t, is the batch data load rate that needs to be processed by the e-th data center in unit time period t;

[0195] S22. Constructing a service rate sum constraint for a mathematical model of a cloud user load spatiotemporal coordinated data center taking into account the thermal dynamic characteristics of a building, specifically comprising the following steps:

[0196] From the perspective of server types, the sum of the service rates of e data centers satisfies the following relationship:

[0197]

[0198] From the perspective of cloud users' real-time requirements, the sum of the service rates of e data centers satisfies the following relationship:

[0199]

[0200] Among them, μ t is the sum of the service rates of the servers in E data centers during period t; is the service rate of the m-type servers in the s-working state in the data center i during period t; is the service rate of E data centers for processing interactive cloud user loads during period t; is the service rate of E data centers for processing batch cloud user loads during period t;

[0201] S23, constructing a maximum response time constraint of a cloud user load spatiotemporal coordination data center mathematical model taking into account the thermal dynamic characteristics of the building, specifically comprising the following steps:

[0202] The maximum response time of interactive cloud user loads satisfies the following constraint:

[0203] t w,t ≤D itr -d itr (17);

[0204] The maximum response time of batch cloud user loads satisfies the following constraint:

[0205]

[0206] Among them, D itr is the maximum response time of interactive cloud user load; d itr is the load transmission delay time; T batch The maximum response time for batch-type cloud user loads;

[0207] S24, constructing a server operating environment temperature constraint for a mathematical model of a cloud user load spatiotemporal coordinated data center taking into account the thermal dynamic characteristics of the building, specifically comprising the following steps:

[0208] When the data center server is operating normally, the temperature range in which the indoor temperature is located satisfies the following constraint relationship:

[0209]

[0210] in, They are the lower and upper limits of the indoor temperature when the data center server is operating normally;

[0211] S25, constructing a maximum data load rate constraint for a cloud user load spatiotemporal coordination data center mathematical model taking into account the thermal dynamic characteristics of the building, specifically comprising the following steps:

[0212] When a data center server processes data, the maximum data load that it can handle satisfies the following constraint:

[0213] 0≤λ e,t ≤λ e,max (twenty three);

[0214] Among them, λ e,max is the maximum data load that the e-th data center can process per second;

[0215] S26, constructing server operating frequency and service rate constraints for a mathematical model of a cloud user load spatiotemporal coordinated data center taking into account the thermal dynamic characteristics of a building, specifically comprising the following steps:

[0216] The server processor is equipped with discrete CPU operating frequency and service rate ladders, and the two correspond to each other. The CPU operating frequency and service rate satisfy the following relationship:

[0217] f t ∈{f1,f2,…f h …,f H} (twenty four);

[0218] μ t∈{μ1,μ2,…μ h …,μ H} (25);

[0219] Among them, H is the optional operating frequency type of the server, f t is the CPU operating frequency of the server during period t, μ t is the server service rate in period t;

[0220] In the CPU operating frequency and service rate adjustment of the data center, a selection is made in the sets (24) and (25) according to the real-time network load to obtain the CPU operating frequency and service rate based on the real-time network load, and the CPU operating frequency and service rate based on the real-time network load satisfy the following relationship:

[0221]

[0222] in, is the selection variable of the working frequency and service rate during the period. Since the CPU can only work at one frequency during a period, The following constraints should be satisfied:

[0223]

[0224] S27, constructing a server quantity constraint for a mathematical model of a cloud user load spatiotemporal coordinated data center taking into account the thermal dynamic characteristics of a building, specifically comprising the following steps:

[0225] The constraint relationship satisfied by the number of servers participating in the adjustment in each data center is as follows:

[0226]

[0227] Among them, n sev The number of servers involved in the adjustment in each data center, The upper limit of the number of servers that can participate in the regulation in each data center;

[0228] S28, constructing air conditioning system operation constraints for a mathematical model of a cloud user load spatiotemporal coordinated data center taking into account the thermal dynamic characteristics of the building, specifically comprising the following steps:

[0229] Based on the air conditioning system meeting the cooling requirements of the servers in the data center and the operating indoor temperature requirements, the operating power of the air conditioning system meets the following constraint relationship:

[0230]

[0231] in, Sets a power cap for air conditioning system operation.

[0232] Furthermore, by setting constraints on the mathematical model of the spatiotemporal coordination of cloud user loads in data centers taking into account the thermal dynamic characteristics of buildings, it is beneficial to improve the reliability of the mathematical model of the spatiotemporal coordination of cloud user loads in data centers taking into account the thermal dynamic characteristics of buildings.

[0233] See also Figure 1 and Figure 4 The present invention provides an embodiment: a differentiated demand coordination method based on a data center mathematical model, in S3, based on the cloud user load spatiotemporal coordination data center mathematical model taking into account the thermal dynamic characteristics of the building obtained in S1, with conventional load power and outdoor temperature as uncertain factors and the target data center daily operation cost minimization as the goal, a two-stage robust optimization model of the data center taking into account the thermal dynamic characteristics of the building is constructed, specifically including the following steps:

[0234] S31. Select the target data center, obtain the energy consumption of the target data center based on the cloud user load spatiotemporal coordination data center mathematical model taking into account the thermal dynamic characteristics of the building, and take the minimization of the daily operating cost of the target data center as the objective function. The objective function expression is established as follows:

[0235]

[0236] Among them, M dcA,t is the total energy consumption of the selected target data center A in period t, M dcB,t is the total energy consumption of the selected target data center B during period t, c t is the daily transaction electricity price, and the cloud user load spatiotemporal coordination data center mathematical model of data center A and data center B taking into account the thermal dynamic characteristics of the building satisfies the constraints in S21-S28;

[0237] S32. Establish a data center deterministic mathematical model that does not consider uncertain factors, and the compact form of the data center deterministic mathematical model is as follows:

[0238]

[0239] Among them, x and y are variables, and x and y satisfy the following relationship:

[0240]

[0241] Where C is the number of servers in the data center, which is the coefficient column vector of formula (31); D, K, F, G and I uis the coefficient matrix under the corresponding constraints, d and k are constant column vectors, where the inequality constraint is the second row in (32) and satisfies the constraint relations (17)-(19), (22)-(23), and (30). The equality constraint is the third row in (32) and satisfies the constraint relations (15)-(16), (18), (20)-(21), (28)-(29). The relationship between variables x and y is the fourth row and satisfies the constraint relations (26)-(27). The fifth row indicates that in the data center deterministic mathematical model, the uncertain factor variables are the predicted values ​​of each time period, and the predicted values ​​satisfy the following relationship:

[0242]

[0243] in, represents the predicted value of the conventional load power in period t, represents the predicted value of outdoor temperature in period t;

[0244] S33. Construct a box type uncertainty set U to which the uncertain factors conventional load and outdoor temperature belong, and U satisfies the following relationship:

[0245]

[0246] Among them, u P,t and u T,t are uncertain variables, namely, conventional load power and outdoor temperature; and The maximum fluctuation deviation allowed for the uncertain variable;

[0247] S34. According to the mathematical model determined by the data center, the box-shaped uncertainty set U of the uncertain factors is used as a constraint, and the two-stage robust optimization method is used to construct a two-stage robust optimization model of the data center taking into account the thermal dynamic characteristics of the building as follows:

[0248]

[0249] The first stage problem is the outermost min problem, and the optimization variable is x; the second stage problem is the inner max-min problem, and the optimization variables are u and y. The min problem is equivalent to the objective function of formula (31), which represents the minimization of the daily operating cost of the data center. Ω(x,u) means the feasible domain of the optimization variable y when the value of (x,u) is given, and the expression of Ω(x,u) is as follows:

[0250]

[0251] Among them, γ, λ, ν, and π are dual variables used to deal with the minimization problem in the second stage.

[0252] Furthermore, considering the uncertainty of conventional load power and outdoor temperature, a mathematical model of the data center taking into account the thermal dynamic characteristics of the building is proposed, which is constructed using a two-stage robust optimization method. On the basis of ensuring the normal operating temperature of the data center server, the ability of the data center to resist fluctuations in conventional load power and outdoor temperature is significantly improved, and the reduction of the system's operating economy due to excessive conservatism is avoided. This allows the data center to meet the usage needs of cloud users under the condition of uncertainty in conventional load and outdoor temperature, while reducing operating costs.

[0253] See also Figure 1 and Figure 5 , an embodiment provided by the present invention: a differentiated demand coordination method based on a mathematical model of a data center, including the two-stage robust optimization model of a data center taking into account the thermal dynamic characteristics of a building proposed in S4, using the G&GG algorithm for iterative solution to obtain the optimal differentiated demand coordination solution for uncertain factors;

[0254] Specifically, S4 also includes the following steps:

[0255] S41. Decompose the mathematical model of the data center taking into account the thermal dynamic characteristics of the building, which is constructed by the two-stage robust optimization method in S34, to obtain a main problem model and a sub-problem model, wherein the main problem model is as follows:

[0256]

[0257] Where k and l are the number of iterations, and l∈k, y l is the solution of the data center subproblem after the lth iteration; is the value of the uncertain factors normal load power and outdoor temperature for the first iteration;

[0258] The sub-problem model is as follows:

[0259]

[0260] For the subproblem model (38) of the NP-hard problem, given (x,u), the inner minimization in (38) is transformed into a linear problem, and the inner min in (38) is transformed into the max form according to the pair theory, and merged with the outer max, the transformed subproblem model is obtained as follows:

[0261]

[0262] For the bilinear term u in (40) Tπ, when the uncertainty factors of the data center, the conventional load and the outdoor temperature, are taken as the right boundary in equation (35), the required operating cost of the data center is high. Therefore, the uncertainty U to which the uncertainty factors, the conventional load and the outdoor temperature belong, is optimized as follows:

[0263]

[0264] Where B = [B P,t B T,t ] T is a binary variable, and the value of 1 indicates that the normal load power and outdoor temperature reach the maximum value of the uncertainty set; Γ p,t and Γ T,t are the uncertainty adjustment parameters corresponding to the conventional load power and outdoor temperature, respectively. The smaller the Γ value, the more risky the model is. Therefore, Γ is used to adjust the conservativeness of the scheme. When the uncertainty set of the uncertain factors in formula (41) is substituted into formula (40), the product of continuous variables and binary variables appears. Therefore, the linearization of formula (40) is carried out through the big M method to obtain the following expression:

[0265]

[0266] Where Δu=(Δu P,t ,Δu T,t ) T ,B'=(B' P,t ,B' T,t ) T is a continuous auxiliary variable, is a very large positive real number.

[0267] Furthermore, a C&CG algorithm is proposed to efficiently solve the mathematical model of a data center taking into account the thermal dynamic characteristics of the building, which is constructed using a two-stage robust optimization method. By decomposing the original problem into a main problem and sub-problems for alternating calculations, the optimal differential demand coordination plan for uncertain factors is obtained. The uncertainty set of uncertain factors is constructed to transform the NP-hard problem in the sub-problem into a linear problem, which reduces the calculation difficulty and can adjust the conservatism of the model, which is conducive to reducing the redundancy of the scheduling results.

[0268] See also Figure 1 and Figure 5 , an embodiment provided by the present invention: a differentiated demand coordination method based on a mathematical model of a data center, including the two-stage robust optimization model of a data center taking into account the thermal dynamic characteristics of a building proposed in S4, using the G&GG algorithm for iterative solution to obtain the optimal differentiated demand coordination solution for uncertain factors;

[0269] Specifically, S4 also includes the following steps:

[0270] S42: for the main problem model and the sub-problem model obtained in S41, the G&GG algorithm is used to iteratively solve to obtain the optimal differential demand coordination solution for uncertain factors. The solution method includes the following steps:

[0271] S421: Given a set of conventional load power and outdoor temperature as the worst scenario u, set the lower bound LB of the data center daily operating cost provided by the main problem to +∞, the upper bound UB provided by the sub-problem to -∞, and the convergence threshold to ε;

[0272] S422: The worst scenario obtained based on the lth iteration of the data center Solving the main problem (38), we get Update the Nether

[0273] S423: Will solve Substituting it into equation (41) as a fixed value, we get the objective function value of the subproblem: and the value of the uncertain variable u, and use the obtained objective function value as the new upper bound to calculate the updated upper bound

[0274] S424: Determine whether the upper and lower bounds meet the convergence conditions. When UB-LB≤ε, the iteration ends and the optimal solution is obtained. Otherwise, the variable y is increased. k+1 And the following constraints:

[0275]

[0276] Order, re-solve the main and sub-problems until the algorithm converges or reaches the maximum number of iterations.

[0277] Furthermore, the optimal solution to the original problem is obtained by solving it, that is, the scheduling scheme with the optimal operating cost of the data center when the conventional load power of the data center is the largest and the outdoor temperature is the highest, so as to meet the differentiated coordination needs of the mathematical model of the data center.

[0278] Working principle: A differentiated demand coordination method based on a mathematical model of a data center, including constructing a spatiotemporal coordination data center mathematical model of cloud user load taking into account the thermal dynamic characteristics of the building according to the thermal dynamic characteristics of the data center and the differentiated real-time requirements of the cloud user load, setting constraints on the spatiotemporal coordination data center mathematical model of cloud user load taking into account the thermal dynamic characteristics of the building, and constructing a two-stage robust optimization model of a data center taking into account the thermal dynamic characteristics of the building based on the obtained mathematical model of the spatiotemporal coordination data center of cloud user load taking into account the thermal dynamic characteristics of the building, with conventional load power and outdoor temperature as uncertain factors and the goal of minimizing the daily operating cost of the target data center. The two-stage robust optimization model of the data center taking into account the thermal dynamic characteristics of the building is iteratively solved using the G&GG algorithm to obtain the optimal differentiated demand coordination solution for uncertain factors.

[0279] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A differentiated demand coordination method based on a data center mathematical model, characterized in that: The steps include: S1. According to the building thermal dynamic characteristics of the data center and the differentiated real-time requirements of cloud user loads, a mathematical model of the cloud user load spatiotemporal coordination data center taking into account the ambient temperature and the building thermal dynamic characteristics is constructed; S2. Setting constraints on the mathematical model of spatiotemporal coordination of cloud user loads in a data center taking into account the thermal dynamic characteristics of the building; S3, based on the cloud user load spatiotemporal coordination data center mathematical model taking into account the thermal dynamic characteristics of the building obtained in S1, with conventional load power and outdoor temperature as uncertain factors and the target data center daily operation cost minimization as the goal, a two-stage robust optimization model of the data center taking into account the thermal dynamic characteristics of the building is constructed; S4. The two-stage robust optimization model of the data center taking into account the thermal dynamic characteristics of the building is iteratively solved using the G&GG algorithm to obtain the optimal differential demand coordination solution for uncertain factors.

2. The differentiated demand coordination method based on a data center mathematical model according to claim 1 is characterized by: The S1 also includes: S11. Establish a data center data load model; S12. Establishing a data center energy consumption model; S13. Establish a mathematical model of cloud user load spatiotemporal coordination data center taking into account the thermal dynamic characteristics of the building; The establishment of the data center data load model in S11 also includes dividing the cloud user load in the data center into interactive data load and batch data load according to the real-time requirements of cloud users; The data center data load model includes a response time model for interactive data loads, wherein the response time model includes a transmission delay time model and an average waiting time calculation model, wherein the transmission delay time model is as follows: d itr =tc (1); where d itr is the transmission time of interactive user load in period t, tc is a constant; The average waiting time calculation model is as follows: where t w,t is the average waiting time of interactive cloud user load in period t, M is the set of server types in the data center, i is a server type in set M, N is the set of server working states, j is a working state in set N, is the service rate of the data center in the i-th category and in working state j processing interactive cloud user load in period t; is the interactive cloud user load rate during period t; The data center data load model also includes a cloud user load processing time model for batch data loads, and the cloud user load processing time model is as follows: in, is the service rate of batch-type cloud user loads in data center i and in working state j during period t; is the load rate of batch processing cloud users in period t.

3. The differentiated demand coordination method based on a data center mathematical model according to claim 2 is characterized by: The establishment of the data center energy consumption model includes the following steps: S121. Establish a power model for IT equipment in a data center; S122. Establishing a power model of an air conditioning system for a data center; S123. Establishing a total power model for the data center; The establishment of the IT equipment power model of the data center specifically includes using the data center server power to represent the IT equipment power, and modeling the server power based on the DVFS technology, and obtaining the IT equipment power model as follows: P m,s,t =P m,st +P m,dy,t (5); Among them, P m,s,t P is the power of the m-type server in the s working state during the t period. m,st is the corresponding server static power, P m,dy,t is the corresponding dynamic energy consumption; k m is the dynamic energy consumption calculation coefficient of the m-type server; f m is the chip operating frequency; The establishment of the air conditioning system power model specifically includes using the building thermal dynamic characteristics of the data center to construct the air conditioning system power model as follows: Among them, C wall,ij is the wall heat capacity, which is divided into the wall heat capacity with window side and the wall heat capacity without window side; is the wall temperature; is the indoor temperature; is the temperature of the adjacent node; R wall,ij is the wall thermal resistance, which is divided into the wall thermal resistance with window side and the wall thermal resistance without window side; if the wall is exposed to external solar radiation, q ij Take 1, otherwise take 0; v ij is the wall heat absorption rate; A wall,ij is the wall area; is the light intensity in the corresponding direction of the wall; C romm,j Heat capacity for the room; is the outdoor temperature; N room Refers to the region's adjacent nodes; is the window thermal resistance; ω ij is the window refractive index; is the light intensity in the direction corresponding to the window; It is an internal heat source; is the cooling capacity of the air-conditioning system during period t; is the internal heat dissipation coefficient of IT equipment in the data center; The establishment of the total power model of the data center specifically includes constructing the total power model according to the IT equipment power model and the air conditioning system power model of the data center, and the total power model of the data center is as follows: in, is the total power of the data center in period t; is the power of IT equipment during period t; is the power of the air conditioning system during period t; is the total power of the conventional load during period t, which includes the power distribution system and lighting system of the data center, and is a constant; The establishment of a cloud user load spatiotemporal coordination data center mathematical model taking into account the thermal dynamic characteristics of the building specifically includes: integrating the total power model of the data center to obtain a cloud user load spatiotemporal coordination data center mathematical model taking into account the thermal dynamic characteristics of the building based on the thermal dynamic characteristics of the data center building and the differentiated real-time requirements of the cloud user load as follows: in, is the total energy consumption of the data center in period t; is the energy consumption of IT equipment during period t; is the energy consumption of the air conditioning system during period t; is the total energy consumption of conventional load during period t; N T The scheduling cycle.

4. The differentiated demand coordination method based on a data center mathematical model according to claim 3 is characterized by: The setting of constraint conditions for a mathematical model of a cloud user load spatiotemporal coordination data center taking into account the thermal dynamic characteristics of a building comprises the following steps: S21. Constructing cloud user data load rate constraints for a mathematical model of a spatiotemporal coordinated data center for cloud user loads taking into account the thermal dynamic characteristics of buildings; S22. Constructing the service rate sum constraint of the mathematical model of spatiotemporal coordination of cloud user load in data center taking into account the thermal dynamic characteristics of the building; S23, constructing a maximum response time constraint for a mathematical model of a spatiotemporal coordinated data center for cloud user loads taking into account the thermal dynamics of buildings; S24, constructing server operating environment temperature constraints for a mathematical model of a cloud user load spatiotemporal coordinated data center taking into account the thermal dynamic characteristics of the building; S25. Constructing a maximum data load rate constraint for a mathematical model of a spatiotemporal coordinated data center for cloud user loads taking into account the thermal dynamic characteristics of buildings; S26. Constructing server operating frequency and service rate constraints for a mathematical model of a spatiotemporal coordinated data center for cloud user loads taking into account the thermal dynamic characteristics of buildings; S27. Constructing the server quantity constraint of the mathematical model of the spatiotemporal coordination data center of cloud user load taking into account the thermal dynamic characteristics of the building; S28. Construct the air conditioning system operation constraints of the mathematical model of the data center for spatiotemporal coordination of cloud user loads taking into account the thermal dynamic characteristics of the building.

5. The differentiated demand coordination method based on a data center mathematical model according to claim 4 is characterized in that: The setting of the constraint conditions specifically includes the following steps: The step of constructing the cloud user data load rate constraint in S21 specifically includes the following steps: Calculate the total cloud user data load of the data center using the following formula: Among them, λ t It is the sum of the data load rates of E data centers in period t, and the data load rate is the cloud user load allocated to the data center per unit time; Based on the calculation formula (15), the data load rate of the e-th data center is λ e,t , and λ e,t The calculation formula is as follows: in, is the sum of the interactive data load rates that need to be processed by E data centers within a unit time period t; is the sum of the batch data load rates that need to be processed by E data centers within a unit period t, is the interactive data load rate that needs to be processed by the e-th data center in unit time period t, is the batch data load rate that needs to be processed by the e-th data center in unit time period t; The construction of the maximum response time constraint in S23 specifically includes the following steps: The maximum response time of interactive cloud user loads satisfies the following constraint: t w,t ≤D itr -d itr (17); The maximum response time of batch cloud user loads satisfies the following constraint: Among them, D itr is the maximum response time of interactive cloud user load; d itr is the load transmission delay time; T batch The maximum response time for batch-type cloud user loads; The construction of the air conditioning system operation constraints in S28 specifically includes the following steps: Based on the air conditioning system meeting the cooling requirements of the servers in the data center and the operating indoor temperature requirements, the operating power of the air conditioning system meets the following constraint relationship: in, Sets a power cap for air conditioning system operation.

6. The differentiated demand coordination method based on a data center mathematical model according to claim 5 is characterized by: The setting of the constraint conditions also includes the following steps: The construction of the service rate sum constraint in S22 specifically includes the following steps: From the perspective of server types, the sum of the service rates of e data centers satisfies the following relationship: From the perspective of cloud users' real-time requirements, the sum of the service rates of e data centers satisfies the following relationship: Among them, μ t is the sum of the service rates of the servers in E data centers during period t; is the service rate of the m-type servers in the s-working state in the data center i during period t; is the service rate of E data centers for processing interactive cloud user loads during period t; is the service rate of E data centers for processing batch cloud user loads during period t; The step of constructing the server operating environment temperature constraint in S24 specifically includes the following steps: When the data center server is operating normally, the temperature range in which the indoor temperature is located satisfies the following constraint relationship: in, They are the lower and upper limits of the indoor temperature when the data center server is operating normally; The construction of the maximum data load rate constraint in S25 specifically includes the following steps: When a data center server processes data, the maximum data load that it can handle satisfies the following constraint: 0≤λ e,t ≤λ e,max (23); Among them, λ e,max is the maximum data load that the e-th data center can process per second; The construction of the server operating frequency and service rate constraints in S26 specifically includes the following steps: The server processor is equipped with discrete CPU operating frequency and service rate ladders, and the two correspond to each other. The CPU operating frequency and service rate satisfy the following relationship: f t ∈{f1,f2,…f h …,f H } (24); m t ∈{μ1,μ2,…μ h …,m H } (25); Among them, H is the optional operating frequency type of the server, f t is the CPU operating frequency of the server during period t, μ t is the server service rate in period t; In the CPU operating frequency and service rate adjustment of the data center, a selection is made in the sets (24) and (25) according to the real-time network load to obtain the CPU operating frequency and service rate based on the real-time network load, and the CPU operating frequency and service rate based on the real-time network load satisfy the following relationship: in, is the selection variable of the working frequency and service rate during the period. Since the CPU can only work at one frequency during a period, The following constraints should be satisfied: The step of constructing the server quantity constraint in S27 specifically includes the following steps: The constraint relationship satisfied by the number of servers participating in the adjustment in each data center is as follows: Among them, n sev The number of servers involved in the adjustment in each data center, The upper limit of the number of servers that can participate in the adjustment in each data center.

7. The differentiated demand coordination method based on a data center mathematical model according to claim 6 is characterized by: The S3 includes the following steps: S31. Select the target data center, obtain the energy consumption of the target data center based on the cloud user load spatiotemporal coordination data center mathematical model taking into account the thermal dynamic characteristics of the building, and take the minimization of the daily operating cost of the target data center as the objective function. The objective function expression is established as follows: Among them, M dcA,t is the total energy consumption of the selected target data center A in period t, M dcB,t is the total energy consumption of the selected target data center B during period t, c t is the daily transaction electricity price, and the cloud user load spatiotemporal coordination data center mathematical model of data center A and data center B taking into account the thermal dynamic characteristics of the building satisfies the constraints in S21-S28; S32. Establish a data center deterministic mathematical model that does not consider uncertain factors, and the compact form of the data center deterministic mathematical model is as follows: Among them, x and y are variables, and x and y satisfy the following relationship: Where C is the number of servers in the data center, which is the coefficient column vector of formula (31); D, K, F, G and I u is the coefficient matrix under the corresponding constraints, d and k are constant column vectors, where the inequality constraint is the second row in (32) and satisfies the constraint relations (17)-(19), (22)-(23), and (30). The equality constraint is the third row in (32) and satisfies the constraint relations (15)-(16), (18), (20)-(21), (28)-(29). The relationship between variables x and y is the fourth row and satisfies the constraint relations (26)-(27). The fifth row indicates that in the data center deterministic mathematical model, the uncertain factor variables are the predicted values ​​of each time period, and the predicted values ​​satisfy the following relationship: in, represents the predicted value of the conventional load power in period t, represents the predicted value of outdoor temperature in period t; S33. Construct a box type uncertainty set U to which the uncertain factors conventional load and outdoor temperature belong, and U satisfies the following relationship: Among them, u P,t and u T,t are uncertain variables, namely, conventional load power and outdoor temperature; and The maximum fluctuation deviation allowed for the uncertain variable; S34. Determine the mathematical model based on the data center, take the box-shaped uncertainty set U of uncertain factors as the constraint, and use the two-stage robust optimization method to construct the mathematical model of the data center taking into account the thermal dynamic characteristics of the building as follows: The first stage problem is the outermost min problem, and the optimization variable is x; the second stage problem is the inner max-min problem, and the optimization variables are u and y. The min problem is equivalent to the objective function of formula (31), which represents the minimization of the daily operating cost of the data center. Ω(x,u) means the feasible domain of the optimization variable y when the value of (x,u) is given, and the expression of Ω(x,u) is as follows: Among them, γ, λ, t, and π are dual variables used to deal with the minimization problem in the second stage.

8. The differentiated demand coordination method based on a data center mathematical model according to claim 7 is characterized by: The S4 also includes the following steps: S41. Decompose the mathematical model of the data center taking into account the thermal dynamic characteristics of the building, which is constructed by the two-stage robust optimization method in S34, to obtain a main problem model and a sub-problem model, wherein the main problem model is as follows: Where k and l are the number of iterations, and l∈k, y l is the solution of the data center subproblem after the lth iteration; is the value of the uncertain factors normal load power and outdoor temperature for the first iteration; The sub-problem model is as follows: For the subproblem model (38) of the NP-hard problem, given (x,u), the inner minimization in (38) is transformed into a linear problem, and the inner min in (38) is transformed into the max form according to the pair theory, and merged with the outer max, the transformed subproblem model is obtained as follows: For the bilinear term u in (40) T π, when the uncertainty factors of the data center, the conventional load and the outdoor temperature, are taken as the right boundary in equation (35), the required operating cost of the data center is high. Therefore, the uncertainty U to which the uncertainty factors, the conventional load and the outdoor temperature belong, is optimized as follows: Where B = [B P,t B T,t ] T is a binary variable, and the value of 1 indicates that the normal load power and outdoor temperature reach the maximum value of the uncertainty set; Γ p,t and Γ T,t are the uncertainty adjustment parameters corresponding to the conventional load power and outdoor temperature, respectively. The smaller the Γ value, the more risky the model is. Therefore, Γ is used to adjust the conservativeness of the scheme. When the uncertainty set of the uncertain factors in formula (41) is substituted into formula (40), the product of continuous variables and binary variables appears. Therefore, the linearization of formula (40) is carried out through the big M method to obtain the following expression: Where Δu=(Δu P,t ,Δu T,t ) T ,B'=(B' P,t ,B' T,t ) T is a continuous auxiliary variable, is a positive real number.

9. The differentiated demand coordination method based on a data center mathematical model according to claim 8 is characterized by: The S4 also includes the following steps: S42: for the main problem model and the sub-problem model obtained in S41, the G&GG algorithm is used to iteratively solve to obtain the optimal differential demand coordination solution for uncertain factors. The solution method includes the following steps: S421: Given a set of conventional load power and outdoor temperature as the worst scenario u, set the lower bound LB of the data center daily operating cost provided by the main problem to +∞, the upper bound UB provided by the sub-problem to -∞, and the convergence threshold to ε; S422: The worst scenario obtained based on the lth iteration of the data center Solving the main problem (38), we get Update the Nether S423: Will solve Substituting it into equation (41) as a fixed value, we get the objective function value of the subproblem: and the value of the uncertain variable u, and use the obtained objective function value as the new upper bound to calculate the updated upper bound S424: Determine whether the upper and lower bounds meet the convergence conditions. When UB-LB≤ε, the iteration ends and the optimal solution is obtained. Otherwise, the variable y is increased. k+1 And the following constraints: Order, re-solve the main and sub-problems until the algorithm converges or reaches the maximum number of iterations.

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